wind turbine reliability prediction a s d p & reliability

Wind Turbine Reliability Prediction – A SCADA data processing & Reliability estimation tool

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WIND TURBINE RELIABILITY PREDICTION A SCADA DATA PROCESSING & RELIABILITY

ESTIMATION TOOL

A Project by

CHRISTOS KAIDIS

Submitted to the Office of Graduate Studies of Uppsala University

in partial fulfilment of the requirements for the degree of

WIND POWER PROJECT MANAGEMENT

September 2013

Major Subject: "Energy Technology"

Master of Science in Wind Power Project Management

2013, Visby, Sweden


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WIND TURBINE RELIABILITY PREDICTION

A SCADA DATA PROCESSING & RELIABILITY ESTIMATION TOOL

A Project by

CHRISTOS KAIDIS

Submitted to the Office of Graduate Studies of Uppsala University

in partial fulfilment of the requirements for the degree of

WIND POWER PROJECT MANAGEMENT

Approved by:

Supervisors: Associate Professor Bahri Uzunoglu (Uppsala University, Campus Gotland)

Filippos Amoiralis (Engineering Consultant, MECAL Independent eXperts BV)

Examiner: Professor Jens Sørensen (D.T.U)

September 2013

Major Subject: "Energy Technology"


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ABSTRACT This research project discusses the life-cycle analysis of wind turbines through the processing of operational data from two modern European wind farms. A methodology for SCADA data processing has been developed combining previous research findings and in-house experience followed by statistical analysis of the results. The analysis was performed by dividing the wind turbine into assemblies and the failures events in severity categories. Depending on the failure severity category a different statistical methodology was applied, examining the reliability growth and the applicability of the “bathtub curve” concept for wind turbine reliability analysis. Finally, a methodology for adapting the results of the statistical analysis to site-specific environmental conditions is proposed.


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ACKNOWLEDGEMENTS First of all, I would like to thank my supervisors Professor Bahri Uzunoglu and Mr. Filippos Amoiralis for their support and encouragement throughout my research. Additionally, I would like to thank all Mr. Eric Kamphues for giving me the opportunity to carry out my research in MECAL Independent eXperts and his assistance during my staying in MECAL. I would also like to thank all my colleagues in MECAL Independent eXperts and the other departments of MECAL for always being willing to answer my questions and add to my thesis with their knowledge and experience. Following, I would like to express my gratefulness to my family for their continuous love and support the last 25 years of my life. I would also like to thank all the teachers and personnel in Uppsala University (Campus Gotland) for contributing to my theoretical tuition and personal development during my Master’s studies. Last but not least, I would like to thank each and every one of my classmates in the Wind Power Project Management 2012-2013 Master’s Programme for making my staying in Visby so memorable and full of precious moments.


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NOMECLATURE AMSAA: Army Material Systems Analysis Activity EWEA: European Wind Energy Association FMECA: Failure Mode Effects and Criticality Analysis HPP: Homogenous Poisson Process ISET: Institut für Solare Energieversorgungstechnik MTBF: Mean Time Between Failures MTTF: Mean Time To Failure MTTR: Mean Time To Repair NASA: National Aeronautics and Space Administration NHPP: Non Homogenous Poisson Process O&M: Operation & Maintenance OEM: Original Equipment Manufacturer PLP: Power Law Process SCADA: Supervisory Control and Data Acquisition WMEP: Wind Measurement & Evaluation Programme WTG: Wind Turbine Generator


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TABLE OF CONTENTS

ABSTRACT ...........................................................................................................................3

ACKNOWLEDGEMENTS ....................................................................................................4

NOMECLATURE ..................................................................................................................5

LIST OF FIGURES ................................................................................................................8

LIST OF TABLES .................................................................................................................9

1 Introduction .................................................................................................................. 10

2 Basics of reliability theory ............................................................................................ 13

2.1 Definitions ............................................................................................................. 13

2.2 Statistical tools in reliability engineering ................................................................ 14

2.2.1 Probability distributions .................................................................................. 14

2.2.2 Point process ................................................................................................... 18

3 Previous research on wind turbine reliability ................................................................. 20

3.1 Reliawind ............................................................................................................... 20

3.2 WMEP ................................................................................................................... 21

3.3 Swedish wind turbine statistics ............................................................................... 22

3.4 Finnish wind turbine statistics ................................................................................ 23

3.5 WindStats (Germany & Denmark) .......................................................................... 24

4 WTG Operational Data ................................................................................................. 26

4.1 General wind farm information & Operational data types ....................................... 26

4.1.1 General information ........................................................................................ 26

4.1.2 O&M Data ...................................................................................................... 27

4.1.3 Additional sources of information ................................................................... 28

4.2 Dataset for current research .................................................................................... 28

5 Data processing ............................................................................................................ 29

5.1 Taxonomy selection for failure data ........................................................................ 29

5.1.1 ReliaWind taxonomy description ..................................................................... 29

5.2 Failure definition .................................................................................................... 30

5.3 SCADA data processing algorithm ......................................................................... 32

5.3.1 Turbine State ................................................................................................... 33

5.3.2 Short Running Periods ..................................................................................... 34

5.3.3 Event Indicators .............................................................................................. 34

5.3.4 Downtime Events ............................................................................................ 36

5.3.5 Alarm Logs ..................................................................................................... 37

5.3.6 Failure division to WTG assemblies ................................................................ 38

6 Failure statistical analysis ............................................................................................. 41


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6.1 Overall results ........................................................................................................ 41

6.2 Statistical analysis .................................................................................................. 45

6.2.1 Recent research ............................................................................................... 45

6.2.2 Methodology proposal ..................................................................................... 46

7 Prediction model ........................................................................................................... 52

7.1 Factors influencing wind turbine reliability ............................................................ 52

7.2 Influence of environmental factors ......................................................................... 53

7.3 Model development ................................................................................................ 54

7.4 Application example ............................................................................................... 55

8 Closure ......................................................................................................................... 57

8.1 Conclusions ............................................................................................................ 57

8.2 Limitations ............................................................................................................. 57

8.3 Future work ............................................................................................................ 58

9 References .................................................................................................................... 59

APPENDIX I ........................................................................................................................ 62

APPENDIX II ...................................................................................................................... 72


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LIST OF FIGURES Figure 1. Global annual installed wind capacity 1996-2012, (GWEC, 2013) ...................................... 10 Figure 2. Global cumulative installed wind capacity 1996-2012, (GWEC, 2013) .............................. 10 Figure 3. Typical cost breakdown for an offshore wind farm in shallow water, (Zhang, et al., 2012) . 11 Figure 4. Relation between MTBF, MTTF & MTTR (Tavner, 2009) .................................................... 14 Figure 5. Distribution function F(t) and probability density function f(t), (Rausand & Høyland, 2004) ............................................................................................................................................................... 15 Figure 6. Reliability function, (Rausand & Høyland, 2004) ................................................................. 15 Figure 7. Repairable system failures with interarrival times t, (Reliability EDGE, 2004) ................... 17 Figure 8. Times to failure of a non-repairable system, (Reliability EDGE, 2004) ............................... 17 Figure 9. Normalized failure rate of assemblies - Reliawind, (Wilkinson & Hendriks, 2011) ............. 20 Figure 10. Normalised hours lost per turbine per year for assemblies - Reliawind, (Wilkinson & Hendriks, 2011) ..................................................................................................................................... 21 Figure 11. Share of main components of total number of failures - WMEP, (Hahn, et al., 2006) ........ 21 Figure 12. Failure frequency and downtimes of components - WMEP, (Hahn, et al., 2006) ............... 22 Figure 13. Distribution of number of failures for Swedish wind power plants (2000-2004), (Ribrant & Bertling, 2007)....................................................................................................................................... 22 Figure 14. Percentage of downtime per component in Sweden (2000-2004), (Ribrant & Bertling, 2007) ...................................................................................................................................................... 23 Figure 15. Distribution of number of failures - Finland, (Stenberg & Holttinen, 2010) ...................... 23 Figure 16. Distribution of downtime - Finland, (Stenberg & Holttinen, 2010) .................................... 24 Figure 17. Variation of the failure rates of WTG assemblies for the two populations, (Tavner, et al., 2007) ...................................................................................................................................................... 24 Figure 18. Reliability growth curve using the PLP for the two populations, (Tavner, et al., 2007) ..... 25 Figure 19. Reliability characteristics for different subassemblies in the WMEP programme dividing faults into minor and major failures (Faulstich, et al., 2010) ............................................................... 31 Figure 20. Possible states of a WTG ..................................................................................................... 33 Figure 21. Event Indicators when the WTG state changes ................................................................... 35 Figure 22. Downtime event categorization ........................................................................................... 36 Figure 23. Overview of the process ....................................................................................................... 40 Figure 24. Results - Normalized failure frequency for wind turbine assemblies (including only identified failures) ................................................................................................................................. 41 Figure 25. Normalized downtime for wind turbine assemblies (including only identified failures) ..... 43 Figure 26. The bathtub curve shape of failure rate over time for a system's lifetime, (Rausand & Høyland, 2004) ...................................................................................................................................... 45 Figure 27. Main shaft set failure rate plot, (Andrawus, 2008) ............................................................. 46 Figure 28. Failure occurrence for 2 operational wind farms, (Buckley, 2013) .................................... 46 Figure 29. Failure rate function plot, Manual Restarts ........................................................................ 48 Figure 30. Failure rate function plot, Minor Repairs ........................................................................... 49 Figure 31. Failure rate function plot, Minor Repair - Pitch system ..................................................... 49 Figure 32. Weibull plot for major failures of Frequency converters of wind farm A ........................... 50 Figure 33. Weibull plot for major failures of Pitch system of wind farm A .......................................... 51 Figure 34. 10-minute SCADA database analysis showing failure rate and downtime as function of Mean wind speed, (Wilkinson, et al., 2012) .......................................................................................... 53 Figure 35. 10-minute SCADA database analysis showing failure rate and downtime as function of average Turbulence intensity, (Wilkinson, et al., 2012) ........................................................................ 53 Figure 36. Failure rate function plot, Manual restarts - Target wind farm .......................................... 56


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LIST OF TABLES Table 1. Relationship between F(t), R(t), f(t) and λ(t), (EPSMA, 2005) ................................................ 16 Table 2. General wind farm information ............................................................................................... 26 Table 3. Alternative sources of information .......................................................................................... 26 Table 4. O&M Data types and info derived .......................................................................................... 27 Table 5. Additional sources of information ........................................................................................... 28 Table 6. Dataset used for this project ................................................................................................... 28 Table 7. Examples of the ReliaWind taxonomy ..................................................................................... 30 Table 8. Failure severity categories ...................................................................................................... 32 Table 9. Example of turbine state definition according to SCADA counters ........................................ 34 Table 10. Example of ignored short running period for a detected event ............................................. 34 Table 11. Example of event indicators .................................................................................................. 35 Table 12. Example of Repairing action ................................................................................................. 36 Table 13. Example of failure analysis results for one wind turbine ...................................................... 38 Table 14. Results - Normalized failure frequency (over the total number of failure events) ................ 42 Table 15. Normalized downtime for wind turbine assemblies (over the total number of failure events) ............................................................................................................................................................... 43 Table 16. PLP model parameters for Manual Restarts ......................................................................... 47 Table 17. PLP model parameters for Minor Repairs ............................................................................ 48 Table 18. PLP model parameters, Minor repair - Pitch system ............................................................ 49 Table 19. Parameters of PLP model for manual restarts & environmental conditions ........................ 55


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1 Introduction The usage of wind to generate energy has its roots thousand years ago when it was used for domestic purposes such as milling grain. The electricity generation from wind started in the beginning of the previous century but it was only after the 80’s that large-scale production projects would be realized (Boyle, 2004). The following decades the wind power sector has shown a continuous growth with the global installed capacity growing every year since 1996 as shown in Figure 1 and the global installed capacity approaching 300GW (Figure 2).

Figure 1. Global annual installed wind capacity 1996-2012, (GWEC, 2013)

Figure 2. Global cumulative installed wind capacity 1996-2012, (GWEC, 2013)

With the continuous growth of wind power efforts have been made to optimize the cost of all the aspects constituting a wind power project (Figure 3). In general, O&M costs constitute a sizeable share of the total annual costs of a WT. For a new machine, O&M costs might easily have an average share over the lifetime of the turbine of approximately 20%-25% of total levelized cost per kWh produced – as long the WT is fairly new, the share might constitute 10%-15% increasing to at least 20%-35% by the end of its life (EWEA, 2010).


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Figure 3. Typical cost breakdown for an offshore wind farm in shallow water, (Zhang, et al., 2012)

The estimation of the cost of some aspects of O&M is straight forward (e.g. scheduled service) but for the unscheduled service and the spare parts replacements the prediction becomes more complicated. This leads OEMs (Original Equipment Manufacturers), Research institutes and wind energy consultancies to develop estimation tools for the estimation of O&M cost. One of these tools was developed by MECAL B.V. The MECAL O&M Cost Forecasting Model is a probabilistic model that provides insights in the estimated future wind farm O&M Costs. The Model has been developed by combining years of experience from operations, maintenance, performance monitoring, and wind farm development. During the modelling a most reliably as possible scenario is analysed to simulate the real world. The model is based on the Monte Carlo Simulation and provides guidance during O&M strategy development. Based on a large set of inputs, the Model performs a multiple number of iterations which results are then combined to provide a range of the probable outcomes. The thesis is addressing the multi-variable modelling of MECAL’s O&M Cost Forecasting Model. The focus of the project is to develop and implement a methodology performing the following main tasks:

Process the operational data available in MECAL Model the reliability of wind turbine assemblies so that the results will be used as

input to the O&M Cost Forecasting Model

2%

33%

3%24%

23%

15%

Cost Breakdown

Management

Turbine

Decommissioning

Support structure

O&M

Grid connection


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Thesis overview Chapter 2 In this chapter the theoretical background of the current research is presented. Initially, the definitions of the key terms used are provided. Following, basic elements of statistics and their application in reliability engineering are presented. Chapter 3 This chapter provides a brief description of previous research on wind turbine reliability focusing on research topics that have used a quantitative approach, i.e. analysing operational data from real wind farms. The main findings of each research project are presented in this chapter some of which will be later on used for comparison with the results of the current research. Chapter 4 In this chapter the operational data types available for a wind farm are initially presented. A brief explanation of the information but also the disadvantages and problems that may occur by using each data type is derived. Following, information of the dataset used for the analysis in this research project is provided. Chapter 5 For the extraction of failure information from 10-minute SCADA data an algorithm was developed in VBA (Visual Basic for Applications). In this chapter a detailed description of the steps followed for the development of the SCADA data processing algorithm is provided. Initially, the selection of the Wind turbine taxonomy is justified. Following, the key points of the methodology are explained. Additionally, other sources of operational data that have been used for verification of the algorithm are mentioned. Chapter 6 The extraction of failure information from SCADA data, as developed in the previous chapter, is followed by the statistical analysis of the results. Initially, overall cumulative results concerning the failure occurrence and downtime for each turbine assembly are presented in this chapter. Following, the different statistical methodologies used depending on the failure type are described. Chapter 7 Recent research has been carried out in order to define the impact of environmental factors on WTG reliability. In this chapter an overview of the findings of this research is provided. Additionally, an effort to adapt the results of the statistical analysis performed in the previous chapter to case specific analysis according to the different environmental conditions is presented. Chapter 8 In the final chapter the findings of this research project are summarized, the main conclusions are presented, the limitations of the scope of work are pointed out and suggestions for future research based on the present one are made.


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2 Basics of reliability theory Chapter summary In this chapter the theoretical background of the current research is presented. Initially, the definitions of the key terms used are provided. Following, basic elements of statistics and their application in reliability engineering are presented.

2.1 Definitions The definitions for this section were adopted from the two following reports where the reader can refer for a wider list of definitions. In this list only the definitions of the terms used in this project are included.

Reliability of wind turbine subassemblies (Spinato, et al., 2009) Common reliability analysis methods and procedures (Barbati, 2009)

Reliability: The probability that it will perform its required function under stated conditions for a specified period of time (Spinato, et al., 2009). Failure: The inability of a subassembly to perform its required function under defined conditions; the item is then in a failed state, in contrast to an operational or working state (Spinato, et al., 2009). Failure Mode and Effects Analysis: Analysis used to determine what parts fail, why they usually fail and what effect their failure has on the system (Barbati, 2009). Repair action: Can be an addition of a new part, exchange of parts, removal of a damaged part, changes or adjustment to settings, software update, lubrication or cleaning (Spinato, et al., 2009). Non-repairable system: A system which is discarded after a failure. Examples of non-repairable systems are small batteries or light bulbs (Spinato, et al., 2009). Repairable system: A system that, when a failure occurs, can be restored into operational condition after any action of repair, other than replacement of the entire system. Examples of repairable systems are WTs, car engines, electrical generators and computers (Spinato, et al., 2009). Mean time between failures: This term defines the mean time between failures expressed in hours of operations for a specific module population. It does NOT mean that a module will operate for that many hours before failure (Barbati, 2009). Mean time to failure: This value is very similar to MTBF and is used when evaluating non-repairable systems. MTBF assumes that a device is to experience multiple failures in a lifetime, and after each failure a repair occurs. For non-repairable systems, there is no repair. Therefore, in the lifetime of a non-repairable device, the device fails once and MTTF represents the average time until this failure occurs (Barbati, 2009).


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Mean time to repair: This term defines the expected mean value of an item’s repair time (Barbati, 2009). The relation between MTBF, MTTF and MTTR is illustrated in Figure 4.

Figure 4. Relation between MTBF, MTTF & MTTR (Tavner, 2009)

Manual Restart: Failure event that requires the physical presence of crew at the turbine, in order to reset the turbine controller after it has been tripped by an alarm (Wilkinson & Hendriks, 2011). Minor Repair: Failures caused by minor faults, typically involving sensor or instrumentation failure. Replacement of small parts may be necessary as may some level of trouble-shooting in order to isolate the problem (Wilkinson & Hendriks, 2011). Major Repair: Failure for which more extensive work is required, usually to one of the major mechanical components of the turbine (Wilkinson & Hendriks, 2011).

2.2 Statistical tools in reliability engineering The main references for this overview of engineering statistics where the reader can refer for further viewing are:

System reliability theory (Rausand & Høyland, 2004) The new Weibull handbook (Abernethy, 2001) Offshore wind turbines. Reliability, availability and maintenance (Tavner, 2012)

2.2.1 Probability distributions If the time to failure T is continuously distributed the cumulative distribution function is:

�(�) = Pr(� ≤ �) = � �(�)��

�

�� > 0

The probability density function f(t) is defined as:


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�(�) =�

��(�)

A schematic description of the cumulative distribution function and the probability density function is given in Figure 5.

Figure 5. Distribution function F(t) and probability density function f(t), (Rausand & Høyland, 2004)

The reliability function is:

�(�) = 1 − �(�) = Pr(� > �) �� > 0

Figure 6. Reliability function, (Rausand & Høyland, 2004)

The failure rate function λ(t) is:

�(�) =�(�)

�(�)

The relationship between the distribution function, the probability density function, reliability function and failure rate function is illustrated in


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Table 1. Relationship between F(t), R(t), f(t) and λ(t), (EPSMA, 2005)

2.2.1.1 Weibull distribution The Weibull distribution was invented in 1937 by the Swedish scientist Waloddi Weibull and it has been applied to several engineering problems since then. Some of the advantages of the Weibull distribution are (Abernethy, 2001):

The ability to provide reasonably accurate failure analysis and failure forecasts with small data samples

It provides a simple and useful graphical plot It can be useful even with inadequacies in the data

For the Weibull distribution the distribution function is (Rausand & Høyland, 2004):

�(�) = Pr(� ≤ �) = 1 − �� (�∙�)�

The probability density function:

�(�) = � ∙�� ∙�� ∙�� (�∙�)�

The reliability function:

�(�) = �� (�∙�)�

The failure rate function:

�(�) = � ∙�� ∙�� Where λ (lamda) is the scale parameter and α (alpha) the shape parameter. In reliability engineering the Weibull distribution is used to describe one lifetime of a component and does not allow for more than one failure. Thus, it is required that no failures have occurred before time “t” and after each failure the component is as good as new has


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subsequently been replaced by a new component (Crow, 2004). Given these conditions, the Weibull distribution (or other distribution types) is suitable for reliability analysis of non-repairable systems. One of the common mistakes is analysing inter-arrival data of failures for repairable systems (Reliability EDGE, 2004). The difference between time to failure for non-repairable systems and inter-arrival time of failures of repairable systems is shown in Figure 7 & Figure 8.

Figure 7. Repairable system failures with interarrival times t, (Reliability EDGE, 2004)

Figure 8. Times to failure of a non-repairable system, (Reliability EDGE, 2004)

2.2.1.2 Exponential distribution The exponential distribution can be considered a special case of the Weibull distribution for shape parameter α=1. By replacing α=1 in the equations presented above for the Weibull distribution (Rausand & Høyland, 2004): Probability density function:

�(�) = � ∙�� ∙� Reliability function:

�(�) = �� ∙� Failure rate function:

�(�) = �


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2.2.2 Point process A point process is a stochastic model describing the occurrence of discrete events in time or space. In reliability analysis, failures of repairable systems can be described with point processes (Tavner, 2012). A random variable N(t) that represents for example the number of failure events in the interval [0,t] is called the counting random variable. Subsequently, the number of events in the interval (a,b] will be:

�(�,�]= �(�) − �(�) The point process mean function Λ(t) is the expected number of failures in the interval throughout time t:

�(�) = �[�(�)] The rate of occurrences μ(t) is the rate of change of expected number of failures:

�(�) =��(�)

��

2.2.2.1 Non-homogenous Poisson process (NHPP) A point process is a non-homogenous Poisson process with rate of occurrence μ(t) if (Rausand & Høyland, 2004):

1. �(0) = 0

2. {�(�),� ≥ 0} ℎ��

3. Pr��(� + ��)– �(�) ≥ 2�= �(��)

4. ��(�(� + ��) – �(�) = 1) = �(�)�� + �(��)

If μ(t) is constant then the process is a homogenous Poisson process (HPP).

2.2.2.2 Power Law Process (PLP) The Power Law Process (also called AMSAA model) is a non-homogenous Poisson process for which the rate of occurrence is (NIST/SEMATECH, 2012):

�(�) = � ∙� ∙�� This model became popular for reliability analysis for several reasons some of which are mentioned below (Crow, 2004):


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It introduces the concept of minimal repair. In a system with several failure modes, the repairing action for a single failure mode is considered to bring the system to the state it was before the failure (or to an “as-bad-as-old” state).

If the time to the first follows the Weibull distribution (with shape parameter β and scale parameter λ) each succeeding failure follows the Power Law model assuming a minimal repair. For this reason the Power Law model is also called Weibull Process, though this name should be avoided as it can create misconceptions (NIST/SEMATECH, 2012).

It is easy to use and understand. For the Power Law the waiting time to the next failure, given a failure at time T, has distribution function (NIST/SEMATECH, 2012):

��(�) = 1 − �� ∙[(�� )� � �� ] Power Law model parameter estimation A general maximum likelihood estimation for the parameters of the Power Law model is given by Crow in the AMSAA report No. 138 (Crow, 1975). The parameters (β,λ) are calculated by the equations:

� =∑ ��

��

∑ (�� − ��

� )��

� =∑ ��

��

� ∙∑ [�� ∙��− ��

� ∙��]− ∑ ∑ ��(��)� �

��

��

� : �ℎ� �� : �ℎ� �� ℎ ��

��: �ℎ� �� ℎ ��

��: �ℎ� ��

�: �ℎ� �� ℎ� �� ℎ�� ℎ� ��


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3 Previous research on wind turbine reliability This chapter provides a brief description of previous research on wind turbine reliability focusing on research topics that have used a quantitative approach, i.e. analysing operational data from real wind farms. The main findings of each research project are presented in this chapter some of which will be later on used for comparison with the results of the current research.

3.1 Reliawind The Reliawind project is among the most recent research project on wind turbine reliability finalised in 2011 with the participation of major wind turbine manufacturers, operators and research institutes (Gamesa, Alstom and GL Garrad Hassan to mention some) (Reliawind, 2011). The database of Reliawind contained (in the last update released) data from around 350 operating WTGs of rated power larger than 850kW for varying periods of time; approximately 35000 downtime events have been identified (Wilkinson, et al., 2011). In Figure 9 & Figure 10 are shown the normalised results of Reliawind concerning the failures of wind turbine assemblies.

Figure 9. Normalized failure rate of assemblies - Reliawind, (Wilkinson & Hendriks, 2011)


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Figure 10. Normalised hours lost per turbine per year for assemblies - Reliawind, (Wilkinson & Hendriks, 2011)

3.2 WMEP The Wind Measurement & Evaluation Programme (WMEP) was of the most direct and sizeable efforts to monitor wind turbine reliability with the participation of 1467 WTGs in the period from 1989 until the end of 2004 (Langniss, 2006). The rated power of the majority of the wind turbines participating in the programme was below 1MW (Hahn, et al., 2006). A detailed table with all the turbines participating in WMEP is given in APPENDIX II. In Figure 11 & Figure 12 the main results of WMEP concerning the failure frequency and downtime of components are presented.

Figure 11. Share of main components of total number of failures - WMEP, (Hahn, et al., 2006)


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Figure 12. Failure frequency and downtimes of components - WMEP, (Hahn, et al., 2006)

3.3 Swedish wind turbine statistics A reliability study for Swedish wind power plants during the period 1997-2005 was carried out as part of the research of the Royal Institute of Technology of Stockholm on wind turbine O&M (Ribrant, 2006). The research contained data from 723 wind turbines from Sweden and apart from the general reliability analysis gave a closer insight to gearbox failures which were considered critical because the long downtime per gearbox failure found in the initial results (Ribrant & Bertling, 2007). The main results concerning failure frequency and downtimes are shown in Figure 13 & Figure 14.

Figure 13. Distribution of number of failures for Swedish wind power plants (2000-2004), (Ribrant & Bertling, 2007)


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Figure 14. Percentage of downtime per component in Sweden (2000-2004), (Ribrant & Bertling, 2007)

3.4 Finnish wind turbine statistics In Finland, an effort to monitor the failures of the country’s wind turbines was made by VTT. In this research project data from 72 operating wind turbines of Finland between the years 1996-2008 were used. The results of the project show the percentage of failures and downtime for each wind turbine component. Additionally, an effort to distinguish the main root causes for failures is made (Stenberg & Holttinen, 2010). The overall results are presented in Figure 15 & Figure 16.

Figure 15. Distribution of number of failures - Finland, (Stenberg & Holttinen, 2010)


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Figure 16. Distribution of downtime - Finland, (Stenberg & Holttinen, 2010)

3.5 WindStats (Germany & Denmark) Efforts were made to analyse failure data gathered from the Windstats newsletter. Windstats Newsletter is a quarterly international wind energy publication with news, reviews, and WT production and operating data from thousands of WTs published as a supplement to the magazine Windpower Monthly (Faulstich, et al., 2009). The data contained wind turbines from Germany and Denmark and were analysed separately for each country. The overall results are presented in Figure 17. Apart from general results, the Windstats data were used for statistical analysis using the Power Law process in order to track the reliability growth of the wind turbines in Germany and Denmark as shown in Figure 18.

Figure 17. Variation of the failure rates of WTG assemblies for the two populations, (Tavner, et al., 2007)


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Figure 18. Reliability growth curve using the PLP for the two populations, (Tavner, et al., 2007)


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4 WTG Operational Data In this chapter the operational data types available for a wind farm are initially presented. A brief explanation of the information but also the disadvantages and problems that may occur by using each data type is derived. Following, information of the dataset used for the analysis in this research project is provided.

4.1 General wind farm information & Operational data types

4.1.1 General information General information about the wind farms is also needed in order to classify failures through operational data. The information needed and its functionality is presented in Table 2.

Information Functionality

Wind farm/Wind turbine info

Number of WTGs in wind farm

Failure classification Rated power

Manufacturer

Components type

Site conditions

Monthly mean wind speed Connect Failures-Environmental

Conditions Turbulence intensity

Terrain roughness

Other O&M provider

O&M Evaluation Quality of service

Table 2. General wind farm information

For some of the elements mentioned above, alternative methods to derive information are proposed in Table 3.

Information Alternative Functionality

Site conditions Wind farm location Derive environmental conditions information from meteorological databases & maps

Rated power Power groups (e.g. 1-1,5 MW) Failure classification Table 3. Alternative sources of information


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4.1.2 O&M Data O&M data can be acquired in the following forms:

a) Maintenance logs b) Operation & Alarm logs c) 10-minute SCADA& Alarms d) Questionnaire e) Service provider bills f) Component purchase bills

In Table 4 the above mentioned O&M data forms are sorted in priority order (starting from the most useful), followed by the information that can be derived and the drawbacks of each one.

O&M Data type Information derived Disadvantages

A. Maintenance logs • Accurate failure info • Information for downtimes • Cost of repair

• Sometimes available only in hardcopies • Can be difficult to read or incomplete

B. Operation & Alarm logs • Failures and duration

• Unknown alarm codes • No environmental conditions info • Numerous stops for the same failure

C. 10-minutes SCADA & Alarms

• Failure data • Environmental parameters • Information for further analysis (e.g. Root cause analysis) • Comparison/verification of logs (if both available)

• Large amount of data, require time-consuming processing • Not all alarms indicate failures • No maintenance activity described

D. Service provider bills • Maintenance cost • Indications for the kind of failures

• Less detailed info about failures

E. Component purchase bills

• Information for component replacements

• No downtime information • No failure information

Table 4. O&M Data types and info derived

.


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Concerning 10-minute SCADA, in order to avoid a large amount of data that is difficult to be transferred and processed specific columns can be requested instead of the whole data set. More specifically, the following columns of the SCADA data contain the information initially needed for the present research:

1. Ambient wind speed 2. Grid production power 3. Hour counters, Service On 4. Hour counters, Turbine OK 5. Hour counters, Alarm Active 6. Ambient wind speed standard deviation

4.1.3 Additional sources of information The O&M data types mentioned in Table 4 can provide reliability information of varying accuracy and level of detail. In Table 5 additional information sources are presented; these information sources cannot be used to derive failure rates but as complementary to the above mentioned and/or for verification of the results.

Source Information derived

1. Turbine availability figures • Overall availability • Rough estimation of failure rates

2. Inspection reports • Indications of future failures • Time span between failure indication and actual failure

Table 5. Additional sources of information

4.2 Dataset for current research

For this project, SCADA data from two European wind farms were provided from MECAL B.V. The WTG type, population and time length of data are different for the two wind farms. Due to confidentiality reasons, details about the location, turbine manufacturer and type will not be provided. The two wind farms will be named as A & B. It should be also mentioned that for wind farm B the time length of the data was not the same for all the turbines. Information concerning the dataset used is presented in Table 6.

Wind farm Number of WTGs Rated Power (kW) Days of data Turbine*days

A 23 3000 944 21712

B 36 850-1750 760 25381 Table 6. Dataset used for this project


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5 Data processing For the extraction of failure information from 10-minute SCADA data an algorithm was developed in VBA (Visual Basic for Applications). In this chapter a detailed description of the steps followed for the development of the SCADA data processing algorithm is provided. Initially, the selection of the Wind turbine taxonomy is justified. Following, the key points of the methodology are explained. Additionally, other sources of operational data that have been used for verification of the algorithm are mentioned.

5.1 Taxonomy selection for failure data Wind turbine taxonomy is a structure that names the main features of a WTG in a standardised terminology (Tavner, 2011). The definition of a taxonomy before starting a WTG reliability research project is necessary in order to de fine accurately failure locations and also describe turbines from different WTG manufacturers in a common way (Wilkinson, 2011). There have been several efforts of developing a Wind turbine taxonomy differing on their principal structure and level of detail. The main criteria for the development of these taxonomies have been the information availability (so that the level of detail of the taxonomy will correspond to the level of detail of the information available) and the function of each component with the components performing the same function grouped together (Ribrant, 2006). For this research the taxonomy developed for the ReliaWind project will be used. The reasons for this selection were the following:

The ReliaWind taxonomy was developed in order to focus on SCADA and Service Log data (Tavner, 2011, p. 9), a fact that meets the needs of this project which will focus on SCADA data and Alarm Logs for reliability analysis.

It divides the WTG in categories of various detail (with the most detailed level containing 257 WTG components) offering the potential of statistical analysis in a more detailed level when the amount and accuracy of operational data will permit.

It was developed with the assistance of two major WTG operators (GL Garrad Hassan and Eon Climate and Renewables) indicating that it will be widely used in the future.

It should be mentioned that there is also another wind turbine taxonomy with high level of detail developed by Sandia National Laboratories (Peters, et al., 2009). The reason that the ReliaWind taxonomy was preferred is that it was also applied in data analysis in the context of the ReliaWind project.

5.1.1 ReliaWind taxonomy description The ReliaWind taxonomy is structured based on the following guidelines (Tavner, 2011):

The taxonomy includes all the WTG concepts components in 5 levels, namely: o System o Sub-System o Assembly o Sub-Assembly


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o Component

A hybrid approach is applied for the components grouping; signalling, supervisory and control components are grouped according to their function and mechanical components are grouped according to their position in the WTG.

The taxonomy characteristics described above are clarified in the examples shown in Table 7. The mechanical components are grouped according to their position (e.g. all the components of the gearbox are grouped together) and the electrical components according to their function (e.g. all the sensors placed in different locations of the turbine are grouped together in the Control & Communication System assembly).

System Sub-System Assembly Sub-Assembly Component

WTG Drive Train Module Gearbox Bearings Planet Bearing

WTG Nacelle Mode Yaw System Yaw Brake Yaw Brake Disc

WTG Electrical Module Control and Communication

System Condition

Monitoring System Sensors Table 7. Examples of the ReliaWind taxonomy

The full Wind turbine taxonomy enriched with reference numbers in order to make it more easily used can be found in APPENDIX I.

5.2 Failure definition Failure is defined to be the inability of a subassembly to perform its required function under defined conditions; the item is then in a failed state, in contrast to an operational or working state (Spinato, et al., 2009). Moving from the general definition to implementing a reliability study limits should be customized in order to be precise on what will be considered a failure. In recent WTG reliability projects the limitations stated for a downtime event to be considered a failure were (Wilkinson, 2011) :

The total duration of the event is ≥ 1 hour Human intervention is required to set the turbine back operational state

Other researchers have tried to quantify the severity of a failure event according to the duration of the failure event by dividing failures in minor (duration ≤ 1 day) and major (duration > 1 day) considering that for a failure event of duration longer than 1 day the service team will travel at least twice to the site (Faulstich, et al., 2010). An example with results of failure separation in minor and major is shown in Figure 19.


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Figure 19. Reliability characteristics for different subassemblies in the WMEP programme dividing faults into minor and major failures (Faulstich, et al., 2010)

For the purposes of this project the following criteria were set in order to consider a downtime event as a failure:

The downtime event is due to a technical problem of the WTG, thus downtime events due to environmental conditions (e.g. extreme wind or no low wind) or grid problems are not counted as failures

Human intervention is required to set the turbine back to operational state The main difference compared to previous researchers, this project does not pose any duration limit to the failure events, i.e. failure events of total downtime ≤ 1 hour that required a manual restart are also taken into consideration. The reason for that is that failure events that require only a manual restart and can possibly last less than 1 hour (for an onshore and easily accessible wind farm) can be considered insignificant onshore but can cause long downtimes offshore were accessibility is a major issue. Since MECAL’s O&M cost model is designed also for offshore wind farms it was decided to include also these events. As a result, the failure occurrence is expected to be higher compared to other WTG reliability research projects. For the division of the failures to severity sub-categories the initial logistic delay (i.e. the time needed from the start of the failure event until the technician reaches the WTG) is ignored and only the service time is taken into consideration. With these assumptions the failure events are divided to 3 severity categories as shown in Table 8.


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Service Time

Manual Restart ≤ 1 hour

Minor Repair 1 hour ≤ Service Time ≤ 8 hours

Major Repair ≥ 8 hours Table 8. Failure severity categories

The time limits were decided in cooperation with the WTG inspection team of MECAL (Timessen & Links, 2013). 1 hour was considered as the time needed to perform a visual check of the turbine and a manual restart without any repairing action. The division between Minor and Major repair was made under the assumption that a repair that needs the technician crew to be present for more than a working day (i.e. 8 hours) is a Major one. Any repairing action that can be performed within a working day is considered a Minor repair. The same titles for the severity categories are introduced in the ReliaWind project (Tavner, 2011, p. 13) as “Maintenance Categories” also with the presence of a fourth category called “Major Replacement” based on the military standard for FMEC analysis MIL-STD-1629A (U.S. Department of Defense, 1980) . The definition of the maintenance category in Reliawind implies that detailed information concerning the maintenance activities was available. Since the present research focuses on SCADA data an effort has been made to quantify the limits for the severity categories. Additionally, the fourth category (Major Replacement) defined in Reliawind requires maintenance activities information and is not possible to be identified only through the analysis of SCADA data.

5.3 SCADA data processing algorithm For this research project 10-minute SCADA data in combination with the relevant alarm logs were processed in order to extract the failure events of operational wind farms and perform further statistical analysis. More specifically, the following columns of the 10-minute SCADA data were used:

SCADA counters o Turbine OK counter o Service On counter o Alarm counter

Timestamp Power generated Average wind speed

Additionally, the alarm logs available provide information about:

Code of the alarm initiating a failure Starting time of the failure

For the data processing an algorithm was developed with the use of Visual Basic for Applications and Microsoft Excel 2010. The algorithm processes the 10-min SCADA data, it defines the failure events by combining the information with the alarm logs. The final result is the processed information for the failure events in a given period of time divided according to


33

their severity and the assembly where they occurred. A step-by-step description of the algorithm is provided in the following sections.

5.3.1 Turbine State

Figure 20. Possible states of a WTG

Initially, the algorithm defines the state of the WTG according to the SCADA counters. The SCADA counters are indications showing how many seconds the WTG was in each state (Operational, Alarm and Service) in every 10-minute span (thus taking values from 0 to 600); a relevant example is provided in Table 9. The 3 possible states that a WTG can be in are:

Operational: When the WTG is generating or is capable of generating electricity. The Turbine OK counter has values greater than 0 and the Alarm counter is 0.

Alarm: When a failure has occurred and the WTG cannot perform its function. The Alarm counter has values greater than 0.

Service: When repairing or maintenance action takes place. The Service On counter has values greater than 0.

The distinction between these different turbine states is mainly based on the SCADA counters but there are some special cases that are treated in a different way. They can be summarized as:

One of the alarm descriptions that appear when the WTG is in alarm state (according to the SCADA counters) is “Pause pressed on keyboard”. In this case the algorithm inserts a correction and the turbine is considered to be in service state since the description indicates the presence of a technician in the turbine.

There are cases when the WTG appears to be operating normally according to the

SCADA counters (i.e. Turbine OK = 600), the wind speed is between cut-in and cut-out but the WTG is not generating energy. In these cases, the turbine is considered to be in Alarm state.


34

Table 9. Example of turbine state definition according to SCADA counters

5.3.2 Short Running Periods As Short Running Periods are defined the situations when the WTG is in alarm state for a period of time, briefly operates again, returns to alarm state and eventually starts operating normally again. After examining some of these cases and comparing them with the relevant maintenance logs we concluded that the alarm periods that are interrupted by short running periods in most of the cases belong to the same failure event. Thus, the short running periods are ignored and the events (Alarm or Service) before and after are merged. The example in Table 10 illustrates description above. The maximum duration of a short running is one hour. Other authors mention similar events as “Back-to-back” events and consider them as separate failure events (Peters, et al., 2012).

Table 10. Example of ignored short running period for a detected event

5.3.3 Event Indicators As Event Indicators are defined the moments when the WTG changes from one state to another. Depending on the initial and final state a different event indicator occurs. The event indicators are defined as:

Service Start: The WTG state changes from Operational to Service Service End: The WTG state changes from Service to Operational Pause Start: The WTG state changes from Operational to Alarm Pause End: The WTG state changes from Alarm to Operational Pause End / Service Start: The WTG state changes from Alarm to Service

Wind Farm WTG # TimeStamp Turbine OK counter Service On Alarm Turbine State

2009-06-01 09:50 600 0 0 Operating

2009-06-01 10:00 600 0 0 Operating

2009-06-01 10:10 100 0 500 Alarm

2009-06-01 10:20 0 0 600 Alarm

2009-06-01 10:30 0 0 600 Alarm

2009-06-01 10:40 0 400 200 Service

2009-06-01 10:50 0 600 0 Service

SCADA Columns

Wind Farm WTG # TimeStamp Turbine OK counter Service On Alarm Turbine State Flag

2010-06-27 03:00 0 0 600 Alarm

2010-06-27 03:10 0 0 600 Alarm

2010-06-27 03:20 100 0 500 Alarm

2010-06-27 03:30 600 0 0 Operational Short Run

2010-06-27 03:40 600 0 0 Operational Short Run

2010-06-27 03:50 249 0 351 Alarm

2010-06-27 04:00 0 0 600 Alarm

SCADA Columns


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In Figure 21 a schematic description of the event indicators is given. Following, an example of the event indicators defined by the algorithm after data processing is shown in Table 11.

Figure 21. Event Indicators when the WTG state changes

Row # Project # Turbine ID TimeStamp Event Indicator

48489 6-02-12 13:00 Pause start

48491 6-02-12 13:20 Pause end

48717 8-02-12 3:00 Pause start

48722 8-02-12 14:30 Pause end / Service start

48724 8-02-12 14:50 Service end

51361 26-02-12 22:30 Pause start

51363 26-02-12 22:50 Pause end

51907 1-03-12 17:50 Pause start

51909 1-03-12 18:10 Pause end Table 11. Example of event indicators

In the scope of this research the total downtime is divided into “Alarm duration” and “Service duration”. As alarm duration is defined the initial logistic delay time, i.e. the time needed from the moment the WTG stops until the first human intervention (Service state). When the WTG state changes to Service then the assumption that the repairing action lasts until the turbine starts operating normally again is made. For this reason, there is no event indicator for the state change from Service to Alarm state. Even though Service to Alarm can be observed, this does not indicate that the service action is terminated. An example is shown in Table 12. Making this assumption information concerning logistic delay after the first service action (e.g. waiting time for spare parts, technicians unavailability) cannot be distinguished. Thus, the actual repairing time is possibly shorter than what is estimated as “Service duration”.


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Table 12. Example of Repairing action

5.3.4 Downtime Events The purpose of defining the event indicators was to be able to distinguish and categorize the downtime events that occurred during the period the SCADA data of which is examined. The categorization of the downtime events by the algorithm developed is presented in Figure 22.

The different types of downtime events are defined according to the event indicators as following:

Failure (Pause Start – Pause End / Service Start – Service End): A failure event starts when the turbine state changes to Alarm (Pause Start) followed by a downtime period

Wind Farm WTG # TimeStamp Turbine OK counter Service On Alarm Turbine State Flag

2011-12-30 19:50 600 0 0 Operational

2011-12-30 20:00 146 0 454 Alarm

2011-12-30 20:10 0 0 600 Alarm

2011-12-30 20:20 0 0 600 Alarm

2011-12-30 20:30 0 0 600 Alarm

2011-12-30 20:40 0 102 498 Service Repairing




2011-12-30 21:20 0 0 600 Alarm Repairing

2011-12-30 21:30 0 0 600 Alarm Repairing




SCADA Columns

Figure 22. Downtime event categorization


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until human intervention is detected (Pause End / Service Start) and ending when the WTG starts operating again.

o The duration between Pause Start and Pause End / Service Start is defined as “Alarm duration”

o The duration between Pause End / Service Start and Service is defined as “Service duration”

o The distinction of the failure events in Manual Restart, Minor Repair & Major Repair is given in Table 8.

Auto-Restart (Pause Start – Pause End): Downtime event which is solved by the

WTG itself or with a remote restart, without the natural presence of a technician needed.

Scheduled Service (Service Start – Service End): Downtime event during which the

turbine was in service state without any alarm The Automatic Restarts and Scheduled Services are not counted as failures and will not be part of the statistical analysis that will follow. Though, they can be used for further research topics (e.g. Maintenance planning, Connection between the frequency of automatic restarts and failure events).

5.3.5 Alarm Logs The 10-minute SCADA data are used as described in the previous sections of this chapter in order to define the failure events and categorize them according to their duration. Alarm logs contain additional information concerning the kind of the failures through the alarm numbers and descriptions provided by WTG Control and Communication System. The columns used from the Alarm log are:

Event Detected Timestamp Error number Error description

In order to connect the information extracted from the 10-minute SCADA data with the alarm logs some data modifications were needed. The timestamp of the alarm log is given in accuracy of 1 minute while the SCADA has 10-minutes accuracy. For this reason, the alarm log timestamp was rounded-up to the next 10 minutes timestamp they have different frequency measurements. For each failure event the alarm that initiated the event is considered responsible for the failure, which means that the assembly from which the WTG Control system received a signal is considered the one that have failed. As pointed out also by other researchers, the fact that a failure occurred in a component does not necessarily mean that the component itself is responsible for the failure (Tavner, et al., 2007). Further research that exceeds the scope of this project would be needed to identify the root cause of each failure. There have been cases that the 10-minute time span when a failure event occurred did not agree with the alarm log indication. In these cases the previous and next 10-minutes span was examined. If there still was no match the failure event defined by the SCADA data processing was marked as “Unknown”.


38

5.3.6 Failure division to WTG assemblies The categorization of the failure events to the different parts of the WTG that they occurred was decided to be made at the assembly level of the taxonomy that was selected. The reasons for this choice are:

The available data for this project would not be enough to have enough failure events in each category of a more detailed level (sub-assembly or component)

The taxonomies used in previous research in wind turbine reliability are closer to the assembly level of the Reliawind taxonomy used for this project. Thus it would be easier to compare the results of this project with other results from literature.

The alarm codes that appeared in the results of the SCADA data analysis were assigned to the different wind turbine assemblies. This was done according to the description given in the alarm log for each error code, additional information from the manufacturer (e.g. troubleshooting manual) when available and the experience of MECAL’s inspection crew. Summarizing the above mentioned processes that have been implemented in the SCADA data processing algorithm developed, the key tasks performed are:

Distinguishing state changes of the WTG Detecting the downtime events Separate them according to the event type Separate failure events according to their duration Assign the failures to the WTG assemblies

An example of the final results from a SCADA data set for a wind turbine is presented in Table 13.

Row # Project # Turbine ID TimeStamp Event type Alarm duration Service duration Total downtime Assembly

41611 16-03-10 22:10 Minor Repair 9:20:00 3:20:00 12:40:00 Pitch System

52609 21-06-10 9:20 Minor Repair 1:30:00 6:50:00 8:20:00 Gearbox

64698 13-09-10 8:30 Manual Restart 2:30:00 0:50:00 3:20:00 Unknown

67444 2-10-10 12:20 Minor Repair 1:40:00 5:10:00 6:50:00 Frequency Converter

67512 3-10-10 0:10 Major Repair 6:10:00 125:50:00 132:00:00 Frequency Converter

83466 22-01-11 1:30 Manual Restart 2:10:00 0:10:00 2:20:00 Grid Connection



130307 13-12-11 17:30 Manual Restart 1:50:00 0:30:00 2:20:00 Pitch System

Table 13. Example of failure analysis results for one wind turbine


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An overview figure of the algorithm developed and described in this chapter is provided in the following Figure 23.


40

Figure 23. Overview of the process


41

6 Failure statistical analysis The extraction of failure information from SCADA data, as developed in the previous chapter, is followed by the statistical analysis of the results. Initially, overall cumulative results concerning the failure occurrence and downtime for each turbine assembly are presented in this chapter. Following, the different statistical methodologies used depending on the failure type are described.

6.1 Overall results In this section the overall results of the SCADA data analysis of the two wind farms that were chosen as test case are presented. In Figure 24 and the relevant Table 14 the normalized failure frequency is presented. Additionally, in Figure 25 and Table 15 the normalized downtime for each wind turbine assembly is also shown.

Figure 24. Results - Normalized failure frequency for wind turbine assemblies (including only identified failures)

000%

002%

004%

006%

008%

010%

012%

Normalized failure events

Manual Restart

Minor Repair

Major Repair


42

WTG Assemblies Manual Restart Minor Repair

Major Repair

Grand Total

Auxiliary Electrical System 1,29% 1,43% 0,43% 3,15%

Blade 0,14% 0,00% 0,00% 0,14%

Control and Communication System 3,43% 5,01% 1,29% 9,73%

Frequency Converter 8,01% 5,15% 2,43% 15,59%

Gearbox 2,29% 4,15% 0,57% 7,01%

Generator 5,01% 2,72% 0,86% 8,58%

Grid Connection 6,01% 3,00% 0,57% 9,59%

Hydraulics System 1,00% 2,00% 0,29% 3,29%

Main Shaft Set 0,00% 0,14% 0,00% 0,14%

Nacelle Auxiliaries 0,14% 0,14% 0,14% 0,43%

Pitch System 7,73% 9,73% 2,15% 19,60%

Power Electrical System 0,14% 0,43% 0,43% 1,00%

Tower 6,44% 0,57% 0,29% 7,30%

Unknown 5,87% 3,72% 1,57% 11,16%

Yaw System 1,57% 1,29% 0,43% 3,29%

Grand Total 49,07% 39,48% 11,44% 100,00% Table 14. Results - Normalized failure frequency (over the total number of failure events)

From the results in Figure 24 the following observations can be made:

The wind turbine assemblies which appear to have higher failure frequency are o Pitch system (19.6%) o Frequency converter (15.59%) o Control & Communication system (9.73%)

The frequency of the event types is higher for less severe events with Manual restarts representing almost half of the events (49.07%). Though it should be mentioned that for several assemblies the occurrence of Minor restarts is higher compared to Manual restarts.

From the wind turbine reliability previous research projects (as presented in chapter 3) it would be more reasonable to compare the results with those from the Reliawind project because the wind turbines used are of rated power >850kW as well as those examined in the current research project. In the results of Reliawind the assemblies with the highest failure frequency are (see Figure 9):

o Pitch system (21.29%) o Frequency converter (12.96%) o Yaw system (11.28%)

The results agree with those of Reliawind that the two most critical assemblies are the Pitch system and the Frequency converter. The Yaw system failures in the current research are lower compared to the results of Reliawind.

There is a significant percentage of failure events that were not identified (11.16%) (see Table 14). This can be because of unclear or ambiguous alarm description or no alarm at all in the Alarm log. In these cases, additional O&M information (e.g. Maintenance logs) would be useful.


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Figure 25. Normalized downtime for wind turbine assemblies (including only identified failures)

Row Labels Manual Restart Minor Repair

Major Repair

Grand Total

Auxiliary Electrical System 0,36% 0,32% 2,33% 3,01%

Blade 0,01% 0,00% 0,00% 0,01%

Control and Communication System 0,54% 1,73% 5,73% 8,00%

Frequency Converter 1,75% 1,72% 13,76% 17,23%

Gearbox 1,06% 1,82% 3,34% 6,22%

Generator 0,87% 0,91% 0,72% 2,50%

Grid Connection 0,97% 0,67% 3,48% 5,11%

Hydraulics System 0,31% 4,07% 0,19% 4,57%

Main Shaft Set 0,00% 0,16% 0,00% 0,16%

Nacelle Auxiliaries 0,10% 0,03% 0,59% 0,72%

Pitch System 0,89% 3,24% 7,84% 11,97%

Power Electrical System 0,07% 0,28% 8,63% 8,99%

Tower 2,17% 0,19% 3,59% 5,95%

Unknown 1,33% 1,00% 21,15% 23,48%

Yaw System 0,28% 0,51% 1,31% 2,10%

Grand Total 10,70% 16,64% 72,65% 100,00%

Table 15. Normalized downtime for wind turbine assemblies (over the total number of failure events)

000%

002%

004%

006%

008%

010%

012%

014%

016%

018%

020%

Downtime

Manual Restart

Minor Repair

Major Repair


44

From the results in Figure 25 the following observations can be made:

The wind turbine assemblies that cause the longest downtime are: o Frequency converter (17.23%) o Pitch system (11.97%) o Power electrical system (8.99%)

As expected by the event type definition the contribution of the major repairs to the

total downtime is significantly larger compared to the other two categories. Accordingly, the total downtime for minor repairs is longer than the total downtime for manual restarts despite the higher frequency of manual restarts.

The frequency converter failures have a higher contribution to the total downtime

(17.23%) compared to their contribution to the total number of failures (12.96%). The opposite appears for the pitch system; the pitch system has lower contribution to the total downtime (11.97%) compared to its contribution to the total number of failures (19.6%). In general, the contribution of the assemblies to the total number of failures compared to their contribution to the total downtime does not show large deviation. An exception is the power electrical system with only 1% of the total number of failures but 8.99% of the total downtime. Though, it should be taken into consideration that the turbine population used for this research is relatively small, thus the results can be affected by specific incidents.

The percentage of the total downtime due to unidentified failure events is significant (23.48%). This high percentage appears because of very long (unidentified) downtime events (with duration longer than 30 days) in few turbines of one of the wind farms As mentioned before, additional sources of O&M information can identify more of the unknown events and decrease this percentage.

In some of the previous research on wind turbine reliability it was observed that the gearbox and generator had a much higher contribution to the total downtime compared to the percentage of failures occurred (Figure 12, Figure 14, Figure 16). This does not occur in the turbine population examined in this project. In general, major failures in gearboxes and generators have long downtimes because the replacement of these assemblies (if needed) is a long operation with possibly long logistic delay time (special equipment, weather restrictions). Though, the relatively short period of the data used for this project indicates that no major replacements of these assemblies took place during this period.


45

6.2 Statistical analysis

6.2.1 Recent research In chapter 3 an overview of the research projects on wind turbine reliability carried out in the past was made. The initial efforts focused on counting the failure occurrence and extracting reliability results in terms of failure rates (failures per wind turbine assembly per year). This was mainly based on the assumption that the failure rate of wind turbine assemblies follows the “bathtub curve” shape (Figure 26), i.e. having a constant failure rate during the useful life period.

Figure 26. The bathtub curve shape of failure rate over time for a system's lifetime, (Rausand & Høyland, 2004)

In more recent research projects efforts have been made to examine the evolution of the failure rate during the lifetime of a wind turbine. Spinato et al. used the Power Law process to model the reliability growth of Danish and German wind turbines but also the reliability growth of several wind turbine assemblies (Spinato, et al., 2009). The reliability growth plot for the German Danish wind turbines was shown in Figure 18. Additionally, Andrawus used the Weibull distribution to model the failures of operational wind farms examined (Andrawus, 2008). An example of failure rate plot modelled with the 2-parameter Weibull distribution is shown in Figure 27. Moreover, recent findings from failure data of 2 operational wind farms have demonstrated failure behaviour different than the bathtub curve. The author of that report expresses his doubts about the assumption of constant failure rate (Buckley, 2013). The failure frequency for the two wind farms used in the research of Buckley is shown in Figure 28.


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Figure 27. Main shaft set failure rate plot, (Andrawus, 2008)

Figure 28. Failure occurrence for 2 operational wind farms, (Buckley, 2013)

6.2.2 Methodology proposal For the statistical analysis of the failure events extracted with the use of the SCADA data processing algorithm, depending on the event type the following methods were selected:

Power Law Process model for the Manual restarts & Minor repairs Weibull distribution for the Major repairs

6.2.2.1 Manual restarts Manual restarts of a wind turbine as defined in chapter 2 are failure events that require the presence of the technical crew but no repairing action takes place. The only intervention is rebooting the turbine controller. It cannot be considered that the condition of the turbine has improved after a manual restart and thus using a distribution would not be appropriate for modelling manual restarts. As thoroughly explained in chapter 2 a distribution can be used to


47

model a single lifetime, consequently using a distribution indicates that the system is “as-good-as-new” after the failure (Crow, 2004). For modelling inter-occurrence of manual restarts the Power Law Process (PLP) model is used under the assumption that the system is “as-bad-as-old” after the failure. For the estimation of the parameters β & λ of the PLP model a simple VBA algorithm was created to solve (numerically) the system of equations presented in section 2.2.2.2:

� =∑ ��

��

∑ (�� − ��

� )��

� =∑ ��

��

� ∙∑ [�� ∙��− ��

� ∙��]− ∑ ∑ ��(��)� �

��

��

The results of the parameter estimator were verified with the use of the reliability software Reliasoft RGA 9 (trial version) (Reliasoft, 2013). Since there is no actual repairing during a manual restart there is no reason to group the manual restarts to the wind turbine assemblies. All the manual restarts for each wind farm are grouped together for the estimation of the parameters of the PLP. The results are presented in Table 16 and the relevant reliability plots in Figure 29.

Wind farm Turbine * Days Beta (β) Lamda (λ)

A 21712 1,1136 0,0048

B 25381 1,0323 0,0053 Table 16. PLP model parameters for Manual Restarts


48

Figure 29. Failure rate function plot, Manual Restarts

6.2.2.2 Minor Repairs The same methodology as explained for manual restarts is applied also for minor repairs. During minor repairs service action takes place repairing or replacing components of the wind turbine assembly. As described in the wind turbine taxonomy (section 5.1), an assembly can be considered a system consisted of several sub-assemblies and components. Consequently, a minor repairing action or a replacement of a component is still considered that brings the system back to operating state in the condition it was before the failure. Because of the relatively small turbine population and time length of the sample used for this project the minor repairs for each wind farm are grouped together in order to estimate the parameters of the PLP model. Though, it is suggested to model the minor repairs of each assembly separately if a larger dataset is available. From the current dataset the PLP model was applied to the minor repairs of the pitch system which was the most critical assembly (and thus the amount of minor repairs was sufficient for the analysis). The results for the minor repairs in the two wind farms under discussion are presented in Table 17 and the relevant reliability growth plots in Figure 30and for the pitch system in Table 18 and Figure 31.


A 21712 1,0522 0,0090

B 25381 1,1829 0,0017 Table 17. PLP model parameters for Minor Repairs

0,002

0,004

0,006

0,008

0,01

0,012

0,014

0 200 400 600 800 1000

Fa

ilu

re r

ate

(F

ail

ure

s/d

ay

)

Time (days)

Manual Restarts

Wind Farm A

Wind Farm B


49

Figure 30. Failure rate function plot, Minor Repairs


A 21712 0,9463 0,0053

B 25381 0,8483 0,0015 Table 18. PLP model parameters, Minor repair - Pitch system

Figure 31. Failure rate function plot, Minor Repair - Pitch system

0

0,002

0,004

0,006

0,008

0,01

0,012

0,014

0,016

0 200 400 600 800 1000

Failu

re R

ate

(fa

ilure

s/d

ay)

Time (days)

Minor Repairs

Wind Farm A

Wind Farm B

0

0,001

0,002

0,003

0,004

0,005

0,006

0 200 400 600 800 1000

Fai

lure

rat

e (f

ailu

res/

da

y)

Time (days)

Minor Repair - Pitch system

Wind Farm A

Wind Farm B


50

6.2.2.3 Major Repairs For the major repairs the assumption that the assembly is “as-good-as-new” after the repairing action is considered realistic to be made. In this case there is no inter-occurrence of failure events but a new lifetime of the assembly starts after each major repair. Thus, the major repairs can be modeled with the Weibull distribution. For the estimation of the Weibull shape and scale parameters (β,λ) “Dr. Bob’s Reliability Calculator”, an Excel-based tool developed by NASA was used (NASA , 2013). Because of the relatively small turbine population and time length of the sample used for this project only major failures of assemblies for which there was sufficient number of events. Especially for wind farm B for which the dataset was shorter no more than 2 major failures were detected for the same assembly. Thus, it was considered of little statistical value to perform Weibull analysis in such a small sample. The weibull distributions of the pitch system and the frequency converter for wind farm A are presented in Figure 32 & Figure 33.

Figure 32. Weibull plot for major failures of Frequency converters of wind farm A

.01 .1 1 10 100 1000 10000 100000

.01

.05

.1

.5

1

5

10

5063.2

909599

99.999.99

1 3 1

7

11

Cu

mu

lati

ve O

ccu

rren

ce (

%)

Time (days)

Frequency Converter

Weibull


51

Figure 33. Weibull plot for major failures of Pitch system of wind farm A

.001 .01 .1 1 10 100 1000 10000 100000

.01

.05

.1

.5

1

5

10

5063.2

909599

99.999.99

1

3

1

5

13

Cu

mu

lati

ve

Occ

urr

ence

(%

)

Time (days)

Pitch System

Weibull


52

7 Prediction model Recent research has been carried out in order to define the impact of environmental factors on WTG reliability. In this chapter an overview of the findings of this research is provided. Additionally, an effort to adapt the results of the statistical analysis performed in the previous chapter to case specific analysis according to the different environmental conditions is presented.

7.1 Factors influencing wind turbine reliability The first research projects on wind turbine reliability had focused on observing the occurrence of failures in order to extract failure statistics. Grouping the failure data together and thus weighing equally all the turbines observed was considered of the major disadvantages of WMEP, one of the most sizeable research projects on wind turbine reliability (Faulstich & Hahn, 2009). The influence of several parameters on wind turbine reliability has been examined in research projects mainly focusing on the following:

Environmental conditions (wind speed, turbulence intensity, ambient temperature) Wind turbine rated power Control concepts Turbine concepts Specific assembly types

Faulstich et al. have used the database of WMEP in order to examine the reliability of different turbine types, technical concepts and the relation of the failure rate with the average wind speed and the wind energy index (Faulstich, et al., 2009). The seasonal variation of failure intensity and other effects of weather and location on wind turbine failure rates were examined by Tavner et al. (Tavner , et al., 2010). The effect of environmental conditions on the reliability of specific wind turbine assemblies has been examined, for example the correlation between turbulence intensity and pitch system failures (Tavner , et al., 2011). An extensive research on the effect of environmental parameters on wind turbine reliability using data from over 20 GW of operating wind turbines from all over the world was made by Wilkinson et al. (Wilkinson, et al., 2012). Other researchers have carried out qualitative reliability studies comparing the reliability of different configurations of wind turbine assemblies including: generators (Tavner, 2012, p. 69), power electronic converters (Arifujjaman, 2013), gearboxes (Smolders, et al., 2010).


53

7.2 Influence of environmental factors In the scope of this research the focus will be placed on the effect of environmental parameters on wind turbine reliability. More specifically, the findings of Wilkinson et al. for the effect of mean wind speed and turbulence intensity on turbine reliability as presented in their research report will be the main reference (Wilkinson, et al., 2012). The reasons that this research report will be used are:

It is the most recent, thus more modern WTGs have been examined A vast amount of operational data has been used increasing the statistical value of the

results The results of the 10-min SCADA data analysis from the above mentioned research project are presented in Figure 34 & Figure 35.

Figure 34. 10-minute SCADA database analysis showing failure rate and downtime as function of Mean wind speed, (Wilkinson, et al., 2012)

Figure 35. 10-minute SCADA database analysis showing failure rate and downtime as function of average Turbulence intensity, (Wilkinson, et al., 2012)


54

7.3 Model development In chapters 5 and 6 the failure definition from 10-min SCADA data and the statistical analysis of the results were presented. The reliability of the manual restarts and minor repairs was modelled with the power law process and the major repairs with the Weibull distribution. In order to introduce the influence of environmental parameters the following assumptions were made:

1. All the environmental parameters used (Mean wind speed & Turbulence intensity) have the same influence on wind turbine reliability.

2. All the failure types (Manual restart, Minor repair, Major repair) are equally affected by the environmental parameters.

3. All the wind turbine assemblies are equally affected by the environmental parameters introduced.

4. The failure rate trend (increasing or decreasing) is not affected but only the values of the instant failure rate.

Taking into account these assumptions and the influence of average wind speed and turbulence intensity as shown in Figure 34 & Figure 35 the parameter λ (lamda) is modified according to the relationship between the environmental parameters of the wind farm for which λ has been calculated from operational SCADA and those of the wind farm for which a reliability estimation is to be made. For each environmental parameter a ratio between the value for the wind farm target (the one the reliability estimation will be made) and the value for the wind farm initial calculated:

�� =��

��

The modified value of λ will be:

�� = ��∙�� ∙�� An example to illustrate the application of the above mentioned methodology is given in the next section.


55

7.4 Application example We assume we want to make estimation for the manual restarts of a wind farm (Target wind farm) with average yearly wind speed 8m/sec and average turbulence intensity 0,15. The results for the manual results of wind farms A&B calculated in section 6.2.2.1along with the environmental parameters are presented in Table 19.

Wind farm

Turbine * Days

Beta (β)

Lamda (λ)

Average yearly wind speed(m/s)

Turbulence intensity

A 21712 1,1136 0,0048 6 0,1

B 25381 1,0323 0,0053 10 0,2 Table 19. Parameters of PLP model for manual restarts & environmental conditions

For wind farm A, the ratio concerning the influence of wind speed will be:

�� =��

�� =

1,21

0,845= 1,432

And for the turbulence intensity:

�� =�� ,��

�� ,�=

2,49

1,39= 1,791

Thus, the modified λ for wind farm A will be:

�′� = 0,0048 ∙1,432 ∙1,791 = 0,0123 Similarly for wind farm B:

�� =��

�� =

1,21

1,45= 0,835

�� =�� ,��

�� ,�=

2,49

4,35= 0,57

�′� = 0,0053 ∙0,835 ∙0,57 = 0,00252

The final estimation for the wind farm – target will be a weighted average on the population of the two wind farms using the modified λ parameters:

�(�)�� =��

�� ∙��(�)� +

��

�� ∙��(�)�


56

The resulting failure rate function plot is presented in Figure 36.

Figure 36. Failure rate function plot, Manual restarts - Target wind farm

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0 200 400 600 800 1000

Fa

ilu

re r

ate

(F

ail

ure

s/d

ay

)

Time (days)

Manual restarts - Target wind farm

Wind farm A

Wind Farm B

Wind Farm target


57

8 Closure In the final chapter the findings of this research project are summarized, the main conclusions are presented, the limitations of the scope of work are pointed out and suggestions for future research based on the present one are made.

8.1 Conclusions During this project an algorithm was created in order to extract the failure history with the use of 10-min SCADA data along with the relevant Alarm log. Following the analysis, the results went through a different statistical analysis depending on the failure type. Finally, a preliminary estimation model was created introducing the influence of average wind speed and turbulence intensity on wind turbine reliability. The following conclusions can be drawn:

Processing SCADA data only (along with the alarms logs) can provide a sufficient description of the failure history. 88,84% of the failures were identified for the two wind farms examined in the project.

The assemblies with the higher failure frequency in the wind farms examined were the

pitch system, the frequency converter and the control and communication system.

The assemblies that cause the longer downtime in the wind farms examined are the frequency converter, the pitch system and the power electrical system.

Wind turbine assemblies which are complex systems operating in very different conditions. Consequently, the bathtub curve for their failure rate should not be taken for granted and should be analyzed on base-to-base cases.

Using the Weibull distribution and NHPP can model reliability more precisely and if the failure rate is constant it can be concluded (then we get an exponential distribution and HPP respectively).

Grouping the data of several wind farms together is “hiding” information concerning the influence of environmental and other parameters on wind turbine reliability and can provide misleading results.

Additional operational information can illustrate the failure history and increase the percentage of the failure events identified.

8.2 Limitations

For this project only 10-minute SCADA data were used. Detailed verification with maintenance logs or other sources would eliminate the uncertainties.

The amount of data available led to the decision to limit the failure classification to an assembly level. A larger dataset could give the opportunity to categorize the failures in


58

sub-assembly or component level. Though, failure statistics in assembly level can be considered a satisfactory result that can be applied in reliability prediction modelling.

The total downtime was divided in initial logistic delay and all the period after the first repairing action was considered service time. Under this assumption the service time may be overestimated, fact that can have an impact on the cost modelling.

Only the impact of wind speed and turbulence intensity was considered. Additionally, it was considered that both parameters have an equal impact on wind turbine reliability.

8.3 Future work Large scale wind projects are a relatively recent venture and thus there are not many wind farms for which we have full life-cycle data available. Nevertheless, the technology evolution is so rapid that reliability data from past decades can be of limited usability to predict the reliability of modern WTGs. Reliability prediction is a field with lots of space for improvements. Connected to this thesis, every limitation of those mentioned above can be a new research topic. Further future work ideas would be:

Connection and cross-checking of 10-minute SCADA data with other sources of information. An overview of sources of information about operational data is provided in chapter 4.

Root cause analysis is always the most precise way to define failures and proceed to improvements, though it is time-consuming as it is difficult to be automated. Making root cause analysis through an automated process with little subjectivity would be a problem to be addressed.

The influence of environmental and other parameters on wind turbine reliability

should be further investigated. Quantitative results will be very useful for future research.

In this project the “as-bad-as-old” and “as-good-as-new” assumptions were used. If

more detailed information is available the reliability can be modelled with Renewal process stating to what extend the system has been repaired after the failure event.

The connection between the occurrence of manual restarts, minor repairs and major

repairs can be examined.


59

9 References Abernethy, R. B., 2001. The New Weibull Handbook. 4th ed. North Palm Beach(Florida): Robert B. Abernethy. Andrawus, J. A., 2008. Maintenance optimisation for wind turbines, Aberdeen: The Robert Gordon University. Arifujjaman, M., 2013. Reliability comparison of power electronic converters for grid-connected 1.5kW wind energy conversion system. Renewable Energy, 5 March, Volume 57, pp. 348-357. Barbati, S., 2009. Common reliability analysis methods and procedures, Reliawind consortium. Boyle, G., 2004. Renewable Energy, Power for a sustainable future. 1st ed. Oxford University Press, Incorporated. Buckley, S., 2013. Forecasting wind farm component failures and availability post-warranty. Vienna, EWEA. Crow, L. H., 1975. Reliability analysis for complex, repairable systems. Technical report No. 138, Aberdeen Proving Ground, Maryland: U.S. Army Material Systems Analysis Activity. Crow, L. H., 2004. Practical Methods for Analyzing the Reliability of Repairable systems. Reliability EDGE, 5(1), pp. 4-9. EPSMA, 2005. Guidelines to Understanding Reliability Prediction, Northants: European Powr Supply Manufacturers Association. EWEA, 2010. Costs & Prices. Wind Energy the Facts, Volume 2. Faulstich , S. & Hahn, B., 2009. Comparison of different wind turbine concepts due to their effects on reliability, Project Upwind. Faulstich, S., Hahn, B., Lyding, P. & Tavner, P., 2009. Reliability of offshore wind turbines. Identifying risks by onshore experience. Stockholm, EWEA. Faulstich, S., Hahn, B. & Tavner, P., 2010. Wind turbine downtime and its importance for offshore deployment, John Wiley & Sons, Ltd.. GWEC, 2013. Global Wind Energy Council official website. [Online] Available at: http://www.gwec.net/global-figures/graphs/ [Accessed September 2013]. Hahn, B., Durstewitz, M. & Rohrig, K., 2006. Reliability of Wind turbines, Experiences of 15 years with 1500 turbines, Kassel, Germany: ISET. Langniss, O., 2006. The German 250MW Wind Program, Stuttgart, Germany: Center for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW).


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NASA , 2013. NASA official website. [Online] Available at: http://kscsma.ksc.nasa.gov/Reliability/Default.html [Accessed 6 August 2013]. NIST/SEMATECH, 2012. e-Handbook of Statistical Methods. [Online] Available at: http://www.itl.nist.gov/div898/handbook/apr/section1/apr172.htm [Accessed August 2013]. Peters, V. A., Alistair, O. B. & Bond, C. R., 2012. Continuous Reliability Enhancement for Wind (CREW) Database: Wind Plant Reliability Benchmark, Livermore: Sandia National Laboratories. Peters, V. A., Stinebaugh, J. A., Veers, P. S. & Hill, R. R., 2009. Wind Turbine Reliability Database Update, Livermore, California: Sandia National Laboratories. Rausand, M. & Høyland, A., 2004. System Reliability Theory. Models, Statistical Methods and Applications. Hoboken(New Jersey): John Wiley & Sons. Reliability EDGE, 2004. Avoiding a common mistake in the analysis of repairable systems. Reliability EDGE, 7(1), pp. 3-8. Reliasoft, 2013. Reliasoft Corporation official website. [Online] Available at: http://www.reliasoft.com/rga/features2.htm [Accessed August 2013]. Reliawind, 2011. The Reliawind consortium official website. [Online] Available at: http://www.reliawind.eu/ [Accessed June 2013]. Ribrant, J., 2006. Reliability performance and maintenance, A survey of failures in wind power systems, Stockholm: KTH School of Electrical Engineering. Ribrant, J. & Bertling, L. M., 2007. Survey of failures in wind power systems with focus on Swedish wind power plants during 1997-2005. IEEE Transactions on Energy conversion, March, 22(1), pp. 167-173. Smolders, K., Long , H., Feng, Y. & Tavner, P., 2010. Reliability analysis and prediction of wind turbine gearboxes. Warsaw, EWEA. Spinato, F., Tavner, P., van Bussel, G. & Koutoulakos, E., 2009. Reliability of wind turbine subassemblies. IET Renewable Power Generation, 3(4), pp. 387-401. Stenberg, A. & Holttinen, H., 2010. Analysing failure statistics of wind turbines in Finland, Espoo, Finland: VTT, Technical Research Centre of Finland. Tavner , P. et al., 2010. Study of effects of weather & location on wind turbine failure rates. Warsaw. Tavner , P., Qiu, Y., Korogiannos, A. & Feng, Y., 2011. The correlation between wind turbine turbulence and pitch failure. Brussels, EWEA.


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Tavner, P., 2009. Reliability & Availability of Wind Turbine Electrical & Electronic Components. Elsinki. Tavner, P., 2011. Recommendations from the Reliawind Consortium for the Standardisation for the Wind Industry of Wind Turbine Reliability Taxonomy, Terminology and Data Collection, Reliawind. Tavner, P., 2012. Offshore Wind Turbines. Reliability, availability and maintenance. 1st ed. London: The Institution of Engineering and Technology. Tavner, P., Xiang, J. & Spinato, F., 2007. Reliability Analysis for Wind Turbines. Wiley Interscience, Volume 10, pp. 1-18. U.S. Department of Defense, 1980. Military Standard, Procedures for performing a failure mode, effects and criticality analysis, Washington DC: Department of Defense. Wilkinson, M., 2011. Empirical analysis of wind turbine reliability. Brussels, EWEA. Wilkinson, M. & Hendriks, B., 2011. Report on wind turbine reliability profiles, s.l.: Reliawind. Wilkinson, M. et al., 2011. Measuring wind turbine Reliability, Results of the Reliawind project, GL Garrad Hassan. Wilkinson, M., Van Delft, T. & Harman, K., 2012. Effect of environmental parameters on wind turbine reliability. Copenhagen, EWEA. Zhang, J., Chowdhury, S., Messac, A. & Castillo, L., 2012. A response surface-based cost model for wind farm design. Energy policy, March, pp. 538-550.


62

APPENDIX I System level:

System

1.WTG

2.Wind Farm

Sub-System level:

Sub-System

1.1.Drive Train Module

1.2.Electrical Module

1.3.Nacelle Module

1.4.Rotor Module

1.5.Support Structure

2.1.Collection System

2.2.Meteorological Station

2.3.Operational Infrastructure

2.4.Substation

Assembly level:

Assembly

1.1.1.Gearbox

1.1.2.Generator

1.1.3.Main Shaft Set

1.2.1.Auxiliary Electrical System

1.2.2.Control and Communication System

1.2.3.Frequency Converter

1.2.4.Power Electrical System

1.3.1.Hydraulics System

1.3.2.Nacelle Auxiliaries

1.3.3.Yaw System

1.4.1.Blade

1.4.2.Hub

1.4.3.Pitch System

1.5.1.Foundation

1.5.2.Tower

2.1.1.Cable

2.2.1.Meteorological Station I

2.3.1.Operational Infrastructure I

2.4.1.Grid Connection


63

Sub-Assembly level:

Sub-Assembly

1.1.1.1.Bearings

1.1.1.2.Cooling System I

1.1.1.3.Gears

1.1.1.4.Housing

1.1.1.5.Lubrication System I

1.1.1.6.Sensors I

1.1.2.1.Cooling System II

1.1.2.2.Lubrication System II

1.1.2.3.Rotor

1.1.2.4.Sensors II

1.1.2.5.Stator

1.1.2.6.Structural and Mechanical

1.1.3.1.High Speed Side

1.1.3.2.Low Speed Side

1.1.3.3.Mechanical Brake

1.1.3.4.Sensors III

1.2.1.1.Electrical Services

1.2.1.2.Lightning Protection System

1.2.2.1.Ancillary Equipment

1.2.2.2.Communication System

1.2.2.3.Condition Monitoring System

1.2.2.4.Controller Hardware

1.2.2.5.Controller Software

1.2.2.6.Safety Chain

1.2.3.1.Converter Auxiliaries

1.2.3.2.Converter Power Bus

1.2.3.3.Power Conditioning

1.2.4.1.Measurements

1.2.4.2.Power Circuit

1.3.1.1.Hydraulic Power Pack

1.3.1.2.Actuator

1.3.1.3.Torque Converter

1.3.1.4.Differential

1.3.1.5.Viscous Coupling

1.3.2.1.Meteorological Sensors

1.3.2.2.Nacelle Sensors

1.3.2.3.Safety System


64

Sub-Assembly

1.3.2.4.Bedplate

1.3.2.5.Cover

1.3.2.6.Generator Frame

1.3.3.1.Yaw Brake

1.3.3.2.Yaw Drive

1.3.3.3.Yaw Sensors

1.4.1.1.Blade Lightning Protection Termination

1.4.1.2.Blade Lightning Down conductor

1.4.1.3.Deicing System

1.4.1.4.Leading Edge Bond

1.4.1.5.Nuts And Bolts

1.4.1.6.Paint And Coating

1.4.1.7.Root Structure

1.4.1.8.Sandwich Shell

1.4.1.9.Spar Box

1.4.1.10.Spar Cap

1.4.1.11.Spar Web

1.4.1.12.Trailing Edge Bond

1.4.2.1.Exit Hatch

1.4.2.2.Nose Cone

1.4.3.1.Pitch Cabinet

1.4.3.2.Pitch Drive

1.4.3.3.Pitch Sensors

1.5.1.1.Gravity Based Foundation

1.5.1.2.Monopile

1.5.1.3.Onshore

1.5.1.4.Space Frame or Tripod

1.5.2.1.Access Equipment

1.5.2.2.Tower I

2.1.1.1.Cable I

2.2.1.1.Meteorological Station II

2.3.1.1.Operational Infrastructure II

2.4.1.1.HV Link

2.4.1.2.Substation Transformer

2.4.1.3.Utility Communication and Control


65

Component level:

Component

1.1.1.1.1.Carrier Bearing

1.1.1.1.2.Planet Bearing

1.1.1.1.3.Shaft Bearing

1.1.1.2.1.Hose

1.1.1.2.2.Pump

1.1.1.2.3.Radiator

1.1.1.3.1.Hollow Shaft

1.1.1.3.2.Planet Carrier

1.1.1.3.3.Planet Gear

1.1.1.3.4.Ring Gear

1.1.1.3.5.Spur Gear

1.1.1.3.6.Sun Gear

1.1.1.4.1.Bushing

1.1.1.4.2.Case

1.1.1.4.3.Mounting

1.1.1.4.4.Torque Arm System

1.1.1.5.1.Hose

1.1.1.5.2.Motor

1.1.1.5.3.Motor

1.1.1.5.4.Primary Filter

1.1.1.5.5.Pump

1.1.1.5.6.Reservoir

1.1.1.5.7.Seal

1.1.1.5.8.Secondary Filter

1.1.1.6.1.Debris

1.1.1.6.2.Oil Level

1.1.1.6.3.Pressure 1

1.1.1.6.4.Pressure 2

1.1.1.6.5.Temperature

1.1.2.1.1.Cooling Fan

1.1.2.1.2.Filter

1.1.2.1.3.Hose

1.1.2.1.4.Radiator

1.1.2.2.1.Pump

1.1.2.2.2.Reservoir

1.1.2.3.1.Commutator

1.1.2.3.2.Exciter

1.1.2.3.3.Resistance Controller


66

Component

1.1.2.3.4.Rotor Lamination

1.1.2.3.5.Rotor Winding

1.1.2.3.6.Slip Ring

1.1.2.4.1.Core Temperature Sensor

1.1.2.4.2.Encoder

1.1.2.4.3.Wattmeter

1.1.2.5.1.Magnet

1.1.2.5.2.Stator Lamination

1.1.2.5.3.Stator Winding

1.1.2.6.1.Front Bearing

1.1.2.6.2.Housing

1.1.2.6.3.Rear Bearing

1.1.2.6.4.Shaft

1.1.2.6.5.Silent Block

1.1.3.1.1.Coupling

1.1.3.1.2.Rotor Lock

1.1.3.1.3.Shaft

1.1.3.1.4.Transmission Shaft

1.1.3.2.1.Axial Bearing

1.1.3.2.2.Compression Coupling

1.1.3.2.3.Connector Plate

1.1.3.2.4.Main Bearing Seal

1.1.3.2.5.Main Bearing Temperature Sensor

1.1.3.2.6.Main Shaft

1.1.3.2.7.Radial Bearing

1.1.3.2.8.Rotor Lock

1.1.3.2.9.Slip Ring

1.1.3.3.1.Caliper

1.1.3.3.2.Disk

1.1.3.3.3.Pad

1.1.3.3.4.Tranmission Lock

1.1.3.4.1.High Speed Sensor

1.1.3.4.2.Low Speed Sensor

1.1.3.4.3.Position Sensor

1.2.1.1.1.24 Dc Feeder

1.2.1.1.2.Auxiliary Transformer

1.2.1.1.3.Breaker

1.2.1.1.4.Cabinet

1.2.1.1.5.Fan


67

Component

1.2.1.1.6.Fuse

1.2.1.1.7.Grid Protection Relay

1.2.1.1.8.Light

1.2.1.1.9.Mechanical Switch

1.2.1.1.10.Power Point

1.2.1.1.11.Protection Cabinet

1.2.1.1.12.Pushbutton

1.2.1.1.13.Relay

1.2.1.1.14.Space Heater

1.2.1.1.15.Surge Arrester

1.2.1.1.16.Thermal Protection

1.2.1.1.17.Ups

1.2.1.2.1.Air Termination

1.2.1.2.2.Bonding Element

1.2.1.2.3.Earth Connector

1.2.1.2.4.Earth Termination

1.2.1.2.5.Sliding Contact

1.2.1.2.6.Spark Gap System

1.2.1.2.7.Surge Arrester

1.2.2.1.1.Breaker

1.2.2.1.2.Cabinet Temperature Sensor

1.2.2.1.3.Cable

1.2.2.1.4.Contactor

1.2.2.2.1.Analog I/O Unit

1.2.2.2.2.Digital I/O Unit

1.2.2.2.3.Ethernet Module

1.2.2.2.4.Field Bus Master

1.2.2.2.5.Field Bus Slave

1.2.2.2.6.Frequency Unit

1.2.2.3.1.Condition Cables

1.2.2.3.2.Data Logger

1.2.2.3.3.Protocol Adapter Card For Data Logger

1.2.2.3.4.Sensors

1.2.2.4.1.Controller Power Supply

1.2.2.4.2.Cpu

1.2.2.4.3.Internal Communication System

1.2.2.4.4.Main I/O Unit

1.2.2.4.5.Watch Dog Unit

1.2.2.4.6.Closed Loop Control Software


68

Component

1.2.2.4.7.Supervisory Control Software

1.2.2.5.1.Closed Loop Control Software

1.2.2.5.2.Supervisory Control Software

1.2.2.6.1.Emergency Button

1.2.2.6.2.Max Speed Switch

1.2.2.6.3.Power Switch

1.2.2.6.4.Short Circuit Switch

1.2.2.6.5.Vibration Switch

1.2.2.6.6.Watch Dog Switch

1.2.2.6.7.Wind-Up Switch

1.2.3.1.1.Auxiliary Power Supply

1.2.3.1.2.Cabinet

1.2.3.1.3.Cabinet Heating System

1.2.3.1.4.Cabinet Sensor

1.2.3.1.5.Communication And Interface Unit

1.2.3.1.6.Control Board

1.2.3.1.7.Generator Side Fan

1.2.3.1.8.Grid Side Fan

1.2.3.1.9.Measurement Unit

1.2.3.1.10.Power Supply

1.2.3.1.11.Power Supply 24v

1.2.3.1.12.Tachometer Adapter

1.2.3.1.13.Thermostat

1.2.3.2.1.Branching Unit

1.2.3.2.2.Capacitor

1.2.3.2.3.Contactor

1.2.3.2.4.Generator Side Converter

1.2.3.2.5.Generator Side Power Module

1.2.3.2.6.Grid Side Converter

1.2.3.2.7.Grid Side Power Module

1.2.3.2.8.Inductor

1.2.3.2.9.Load Switch

1.2.3.2.10.Pre-Charge Unit

1.2.3.3.1.Common Mode Filter

1.2.3.3.2.Crowbar

1.2.3.3.3.Dc Chopper

1.2.3.3.4.Generator Side Filter

1.2.3.3.5.Line Filter Assembly

1.2.3.3.6.Voltage Limiter Unit


69

Component

1.2.4.1.1.Ta

1.2.4.1.2.Tv

1.2.4.2.1.Cables

1.2.4.2.2.Machine Contactor

1.2.4.2.3.Machine Transformer

1.2.4.2.4.Mv Busbar / Isolator

1.2.4.2.5.Mv Switchgear

1.2.4.2.6.Soft Start Electronics

1.3.1.1.1.Motor

1.3.1.1.2.Pump

1.3.1.1.3.Pressure Valve

1.3.1.1.4.Filter

1.3.1.2.1.Bushing

1.3.1.2.2.Cylinder

1.3.1.2.3.Hose / Fitting

1.3.1.2.4.Hydraulic Linear Drive

1.3.1.2.5.Limit Switch

1.3.1.2.6.Linkage

1.3.1.2.7.Miscellaneous Hydraulics System

1.3.1.2.8.Position Controller

1.3.1.2.9.Proportional Valve

1.3.1.2.10.Pump

1.3.2.1.1.Anemometer

1.3.2.1.2.Wind Vane

1.3.2.2.1.Emergency Vibration Sensor

1.3.2.2.2.Yaw Encoder

1.3.2.3.1.Beacon

1.3.2.3.2.Down Conductor

1.3.2.3.3.Fall Arrester

1.3.2.3.4.Firefighting System

1.3.2.3.5.Nacelle Cover Metallic Mesh

1.3.2.3.6.Lightning Protection Termination

1.3.2.3.7.Service Crane

1.3.2.4.1.Bolts

1.3.2.4.2.Cast or Welded Structure

1.3.2.5.1.Fibreglass


70

Component

1.3.2.5.2.Hatch

1.3.2.6.1.Bolts

1.3.2.6.2.Cast or Welded Structure

1.3.3.1.1.Yaw Brake Calipers

1.3.3.1.2.Yaw Brake Disc

1.3.3.1.3.Yaw Brake Hoses

1.3.3.1.4.Yaw Brake Paths

1.3.3.2.1.Damper

1.3.3.2.2.Yaw Bearing

1.3.3.2.3.Yaw Gearbox

1.3.3.2.4.Yaw Motor

1.3.3.2.5.Yaw Pinion

1.3.3.3.1.Wind-Up Counter

1.3.3.3.2.Yaw Encoder

1.4.1.1.1.Blade Lightning Protection Termination

1.4.1.2.1.Blade Lightning Down conductor

1.4.1.3.1Deicing System

1.4.1.4.1.Leading Edge Bond

1.4.1.5.1.Nuts And Bolts

1.4.1.6.1.Paint And Coating

1.4.1.7.1.Root Structure

1.4.1.8.1.Sandwich Shell

1.4.1.9.1.Spar Box

1.4.1.10.1.Spar Cap

1.4.1.11.1.Spar Web

1.4.1.12.1.Trailing Edge Bond

1.4.2.1.1.Exit Hatch

1.4.2.2.1.Nose Cone

1.4.3.1.1.Battery

1.4.3.1.2.Battery Charger

1.4.3.1.3.Heater

1.4.3.1.4.Local Controller

1.4.3.1.5.Switchboard

1.4.3.2.1.Motor

1.4.3.2.2.Motor Cooling

1.4.3.2.3.Motor Cooling System

1.4.3.2.4.Motor Drive

1.4.3.2.5.Pinion

1.4.3.2.6.Pitch Bearing


71

Component

1.4.3.2.7.Pitch Gearbox

1.4.3.3.1.Position Encoder

1.4.3.3.2.Temperature Sensor

1.4.3.3.3.Voltmeter

1.5.1.1.1.Concrete

1.5.1.1.2.Steel Reinforcement

1.5.1.2.1.Corrosion Protection

1.5.1.2.2.Pile

1.5.1.2.3.Transition Piece

1.5.1.3.1.Concrete

1.5.1.3.2.Nuts & Bolts

1.5.1.3.3.Piles

1.5.1.3.4.Steel Reinforcement

1.5.1.4.1.Corrosion Protection

1.5.1.4.2.Piles

1.5.1.4.3.Structures

1.5.2.1.1.Ladder

1.5.2.1.2.Landing Pad

1.5.2.1.3.Lightning Protection

1.5.2.2.1.Climb Assist

1.5.2.2.2.Maintenance Crane

1.5.2.2.3.Nuts & Bolts

1.5.2.2.4.Paint/Coating

1.5.2.2.5.Tower Section

2.1.1.1.1.Cable

2.2.1.1.1.Meteorological Station

2.3.1.1.1.Operational Infrastructure

2.4.1.1.1.HV Link

2.4.1.2.1.Substation Transformer

2.4.1.3.1.Utility Communication and Control


72

APPENDIX II

wind turbine reliability prediction a s d p & reliability

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