wind farm efficiency improvement using turbine’s...

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Wind farm efficiency improvement using turbine’s SCADA data

Julio J. Melero1,2,*

, Ana P. Talayero1, Carlos Pueyo

1, Roberto Lázaro

1

1 Fundación CIRCE, Zaragoza, Spain

2 Departamento de Ingeniería Eléctrica. Universidad de Zaragoza, Zaragoza, Spain

*

Corresponding email: [email protected]

ABSTRACT

A method to improve the wind farm efficiency is proposed in this paper, where only available

data, wind turbine’s SCADA and wind farm meteorological mast, are used. Two different

works may be performed using those data, periodic studies and performance audits. Both

kinds of works are carried out following the same scheme. The basic working data are the

alarms and the 10-minute data sets of wind speed, wind direction and power. Additional

information may also be used, pitch angle, turbine availability, operation temperatures, wind

flow correction factors and meteorological mast data. All the available information is pre-

processed and filtered in order to detect invalid. Wind speed data are filtered using a Kalman

filter and correlations with the wind speed from near anemometers. Power data are filtered

using a robust filter and correlated with the power from near wind turbines. Finally, the

alarms are also pre-processed to correlate them with the rest of the available information. Data

regeneration is performed, when needed, using correlation techniques. The analysis of all the

pre-processed information allows obtaining different parameters that characterise the

performance of the individual turbines and the whole wind farm. The most important ones are

availability, yield, losses and alarm frequencies. The study and comparison of these figures of

merit allow detecting incorrect operation of individual turbines and deviations from

contractual limits. Finally, the obtained results can also be used to make strategic decisions

for future actions on the wind farm, correction of maintenance plans, repowering, etc.

KEY WORDS

Wind Farm Efficiency, SCADA Data, Wind Turbine Performance, Data Regeneration

INTRODUCTION

Wind energy has experienced a great development from the 1990’s up to now due to the

stable legal framework in Europe and the great technological advances in wind turbines.

Consecutive regulation modifications, bonus reductions and the achieved technological

maturity make interesting to improve the wind farm performance to obtain the optimum wind

energy exploitation.

A fast way of knowing the wind farm performance consists on studying the available data

captured by the Supervisory Control And Data Acquisition (SCADA). Using the SCADA

data, two different analyses may be performed:

Periodic studies whose goal is to acquire a good knowledge of the operation of the

wind farm and so, to improve its global yield

Performance audits focussed in a defined period of time for obtaining information

about the yield, availability, and power performance in the studied period

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To carry out these analyses a methodology based in specific software has been developed.

This software allows evaluating each wind turbine performance, its availability, to estimate

the production and, therefore, calculate the operative performance of each wind turbine and

other specific studies.

The methodology has been developed using data from real operative wind farms. It has been

applied to more than 300 wind farms in Spain during the last five years. This allowed

improving the methodology and the final method is presented in this paper.

All the data presented here correspond to one year of data (between 2011 and 2012) of a real

wind farm located in Spain. For confidentiality reasons, power and wind speed have been

scaled to references and all the presented values are without units.

DESCRIPTION OF THE METHOD

The starting point of the method is the data registered by the SCADA system. The basic

working data are the alarms (with its beginning and ending time) and the 10-minute data of

wind speed, wind direction and power, taken from each SCADA. This information is

processed to estimate the variables that indicate the wind farm performance state: losses,

availability and yield.

To complement the mentioned data, other data are necessary, such as turbine availability, the

relation between free wind speed and nacelle wind speed, pitch angle, operation temperatures,

density, meteorological mast data, production forecast, etc. Some of these additional data are

indispensable to make the study, like the turbine availability. Other data are not essential, and

in some cases are not available, as the meteorological mast data, therefore they are not always

used in the studies.

Figure 1 shows the working flow diagram. Each step in the process is explained below.

Figure 1. Working flow diagram

P-Vnacelle Data Alarms Other Data

P-Vnacelle Data Alarms Other Data

TDF Files Result 1

pre-processing

Result 2

Final Report

Processing Analysis

Reporting

Analysis

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Data treatment is based in the application of complementary processes that allows locating the

wrong data registered by the SCADA in an effective way and, in some cases, to increase the

data availability using data regeneration methods.

The software used in the analysis consists of small pieces of code easily adaptable for each

kind of study in function of its characteristic. The use of this methodology instead of only one

program allows improving the efficiency in each step of the process of data analysis.

The method is applied to the meteorological data, the production data and the alarms, to

guarantee space and time coherence.

Data pre-processing

The wind turbine manufacturers provide their generators with different SCADA systems even

for the same turbine model. This fact complicates the interpretation of the data and a pre-

processing is necessary to adapt the data to a unique format. Once the original data have been

adapted to a common format, it is possible to start the treatment of information registered

independently.

The first step consists on a visual inspection of the wind speed data, the power data and the

alarms, identifying possible storage errors or information gaps.

The turbine alarms have a specific treatment because in most of the wind turbine models, a

single failure can unchain a set of different alarms. Therefore, it is necessary to define priority

criterions, which organize and sort the alarms, to characterise the behaviour of each different

machine. These criteria are defined in a specific way in each study but having a common aim:

to obtain only one alarm in a defined time interval.

Data processing

In this stage, all available information is collected, crosschecked and joined for its subsequent

analysis and interpretation.

The crosscheck mainly consists on identify all the wind speed and power alarms, where the

10-minutes data affected by an alarm are distinguished.

The alarms can be classified in two categories, warnings and errors. Warnings are only

informative while errors cause a reduction in production or the stopping of the turbine.

Moreover, the error messages can be justified or not-justified relating with the machine

operation. An example of justified error could be a high ambient temperature exceeding the

temperature threshold of the wind turbine. Not-justified errors are all of them obtained as

consequence of operation failures due to troubles in some of the main components of the

turbine or to prevention actions to avoid possible damages. The treatment of the data

identified with an alarm will depend of the alarm category and the objectives of the study.

Once the information is joined, an exhaustive data revision is necessary. Filtering techniques

that help to identify erroneous data are applied. In case of a huge quantity of erroneous data or

information gaps, it may be necessary to carry out the data regeneration using correlation

techniques in order to improve data availability. This task is of great importance because the

conclusions of the study have a strong dependency on data availability. Low data availability

means that the results obtained will be partially representative.

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The filtering and data regeneration methodologies are based in different techniques. Wind

speed data filtering is performed using the Kalman equations (Welch and Bishop, 2001),

power data are filtered applying robust techniques (Sainz et al., 2009), visual inspection is

used to validate the filtering results and, finally, data regeneration is carried out using the bin

method (Beltrán et al., 2009). All this techniques are explained in the next sections.

Wind speed data filtering and regeneration

It is well known that wind speed data in a site has a random character. However, when

temporal series are analysed, a dependency between the current and the previous data is

observed (Huang. and Chabali, 1995). One of the most used methods to analyse temporal

series in dynamic systems is the Kalman filter. Simplifying the methodology, the Kalman

equations are divided into two groups, time update phase and measurement update phase

(Welch and Bishop, 2001) as presented in Figure 2.

Figure 2. Diagram of the Kalman equations.

When the wind speed measured at the nacelle and at the meteorological mast present a great

difference with the wind speed estimated with the Kalman equations, they are identified as

anomalous and its consideration in the study is evaluated.

One limitation of the Kalman filter becomes when there exists a slow temporal degradation in

the anemometers. In this case, the filter is not able to detect the problem. In order to solve it,

an analysis based in correlations between nearest nacelle anemometers is carried out. In this

way, if any wind speed data presents a no-coherent value with the data registered in the

nearest anemometers its use in the study is considered.

The nearest nacelle anemometers are also used to regenerate the data. Firstly, the correlation

between the wind speed registered in the considered anemometer and the wind speed of a near

anemometer is studied. This correlation is not perfectly linear, mainly due to the distance

between the wind turbines and the orography. A way to improve the correlation is applying

the bin method, which has been verified in this kind of works (Beltrán et al., 2009). In case of

the existence of registering errors or information gaps in one or more wind turbines, it is

possible to regenerate the values applying this correlation method. The anemometer with least

error correlation will be used as reference nacelle anemometer. Figure 3 shows the correlation

between nacelle anemometers of two contiguous wind turbines in a wind farm.

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Figure 3. Wind speed correlation measured in two contiguous wind turbines

When the lack of information affects to all the wind turbines and the meteorological mast, the

data regeneration is not possible and the data availability decreases, increasing the study

uncertainty.

Power data filtering and regeneration

Two different techniques are used for filtering the power data. The first one is based in the

comparison of measured power data in each wind turbine with the registered power in the

nearest wind turbines, as it is done with the wind speed data. The method used in this

comparison is also the bin method (Beltrán et al., 2009).

The second technique applies a robust filter to the power curve of each wind turbine. This

robust filter is computed from the statistical values fitted through the Least Median of Squares

(LMedS), which allow removing the residual power values in function of a previously defined

threshold (Sainz et al., 2009).

Once both techniques are applied, a last analysis where the registered power is studied in

function of other variables, as temperature or wind direction. This allows performing specific

studies of the wind farm performance in function of different variables.

Finally, in the same way as it is done for the wind speed, it is possible to regenerate lost

power data. This is performed through correlations with near wind turbines. If the correlations

are good enough and there exist registering errors, the data availability can be improved.

Figure 4 shows the correlation of power data between two near wind turbines.

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Figure 4. Correlation of power data between two near wind turbines

Alarm regeneration

The same situation of non-registered data found in the wind speed and power data can be

encountered in the case of the alarms.

The regeneration of the alarms is possible if the recording error is partial and some conditions

are fulfilled. It is needed the existence of data of one or more wind turbines with wind speed

data in the range where the turbines operate. Recording errors or gaps present in wind speed

ranges below cut-in and above cut-off wind speed can not be regenerated.

As a result of the data processing, a file called TDF is obtained. This file together with the

alarms contains all the necessary information to analyse the wind farm performance.

Information analysis

The next step in the study is to analyse the information of the TDF file and the alarms to

obtain the results that, interpreted, make up the final report.

TDF file analysis

This file contains all the necessary information to characterize the performance of each

turbine in the wind farm. The graphical representation of the 10-minute mean values of the

output power versus the nacelle wind speed reflects the wind turbine performance. This graph

allows identifying anomalous performance in different time ranges and changes in wind

turbine power performance curve due to different possible causes: directional performance,

nacelle anemometer faults, blade problems or turbulence intensity. Figure 5 shows a wind

turbine power performance curve where the values have been normalised to nominal power

and cut-off wind speed.

In Figure 5, it is possible to observe the next events:

Periods of time where the wind turbine is affected by alarms

Periods where the wind turbine has an anomalous performance

Periods with wind speed or power data storage problems

Periods affected by sectorial performance or environmental parameters

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The availability of pressure and temperature data allows correcting the power performance

curve with the air density using the same methodology presented in the power performance

standard (IEC 61400-12-1:2005). In this way, the obtained curve is corrected according to the

air density where the manufacturer guarantees the right performance of the wind turbine.

Figure 5. Power - Nacelle wind speed relation. Values normalised to nominal power and cut-off wind

speed

In the same way, when the power performance curve has been verified (Llombart et al.,

2005), the coefficients relating the free wind speed to the nacelle wind speed can be

estimated. Thus, a relation between the generated power and the free wind speed can be

computed. Although this relation is not computed following the standard (IEC 61400-12-

1:2005), it can be used as a better approximation to the real power performance curve.

Moreover, the power performance curve, the file information allows knowing the production

generated in each wind turbine as the aggregation of each 10-minute power.

Production faults or losses in each wind turbine are estimated from the comparison of data

affected by an alarm against a reference power performance curve. This reference is created

before the analysis from a data range where the wind turbine works correctly and it is

independently created for each wind turbine. The quantity of used data is high enough to

define the wind turbine performance and the season changes.

Losses can be estimated for an alarm group with a common reason, such as the alarms

associated to maintenance, losses caused to compulsory stops due to grid operation conditions

(REAL DECRETO 661/2007), etc.

Yield is computed as the power in each wind turbine divided by the expected production. The

expected production is the addiction of the production and the estimated losses associated to

non-justified alarms.

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100(%)

PLP

PYield

(1)

where P is the generated power and PL the non-justified power losses

Depending on the final goal of the analysis, a periodic study or an audit, the values will be

calculated in a more detailed way or they will be presented as combined results. The

associated losses and energy yields will also be computed in order to obtain the final results of

the report.

Study of the alarms

The alarms are analysed be means of statistical studies, taking into account the following

indices:

Appearance frequency.

Alarm length and repetition space length.

Wind speed where the alarm appears.

Temporality (frequency of apparition at specific hour or month).

These indices reveal the more important events because they are related with frequent events

or with alarms appearing at high wind speeds where the wind farm production is very

sensitive to faults. This information also allows finding dependencies between turbine

production and weather conditions or the electric grid state. This knowledge can help to

establish operational procedures allowing reducing the alarm apparitions and increasing the

wind turbine working hours and its production.

The study of the alarms also allows computing the wind turbine availability. The duration of

the non-justified errors and their associated loss of production are calculated. The duration of

the errors is computed as the difference between the initial and final timestamps of the alarms

having an accuracy of seconds. The result of this computation is the time where the wind

turbine stays at no adequate working conditions due to non- justified faults. Then, the

availability is estimated as the relation between the time when the wind turbine has been

available and the total time that should be available.

Availability(%) = 1-tnon- justifid error

ttotal working

æ

èçç

ö

ø÷÷×100 (2)

It is important to mention here that there is not just one accepted formula for the definition of

availability. This is because the total time term can include all the wind speed range or just the

wind speed working range of the wind turbine. In the same manner, the wind turbine available

time depends on the justification of the errors and they change in every study.

Example of application and results obtained The methodology described in previous sections can be applied to periodic studies and audits,

allowing the calculation of the losses, the availability and the energy yield.

The differences between both kinds of works are the data volume, the definition of the study

time ranges, and the treatment and analysis of the alarms. These differences produce changes

in the searched results and the conclusions are specific for each report.

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As an example, the working method has been applied to a year of data of a wind farm with 25

turbines. Some possible results that can be obtained are presented next.

Figure 6 shows normalised values of the production, losses and energy yield for each wind

turbine of the wind farm. It can be clearly observed the wind turbine (T4) presenting a

relevant fault during the studied period. Two more wind turbines, T24 and T25, show

noteworthy losses but without an important fault. This can be due mainly to two reasons,

frequent stops at low wind speeds and occasional stops with high wind speeds.

Figure 6. Wind turbines production, losses and yield in a wind farm

Figure 7. Wind turbine availability against contractual availability

Figure 7 shows the wind turbines availability compared to the contractual availability for the

whole wind farm. It can be observed that some wind turbines do not keep the contractual

availability, existing a bigger difference in the turbines presenting higher losses as, for

example, T4. It is noteworthy that wind turbine 3 has higher losses than wind turbine 2 but

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with higher availability. This is because the stopping time of turbine 3 was smaller than that

of turbine 2 but at higher wind speeds.

Figure 8 shows the monthly evolution of the performance and the availability of the wind

farm.

Figure 8. Monthly evolution of the performance and the availability

The real average power performance of a wind turbine or a whole wind farm allows to detect

inefficient operations and to adjust the power prediction generally required by the

administration or the grid operator (REAL DECRETO 661/2007). In Figure 9 the normalised

computed power performance curve for a wind turbine is showed and compared with the

contractual curve. Differences between both curves can be observed in the middle and in the

high wind speed ranges. If these wind speed ranges are the most frequent at the site, there will

be a continuous production loss.

Figure 9. Normalised wind turbine production in function of the

normalised nacelle wind speed.

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Statistical information, alarm frequency, duration and more frequently hour or season of

apparition of the alarms can be obtained from the alarms study. All this information may

allow taking strategic decisions to increment the wind turbine availability.

Figure 10 shows the frequency of the 10 most registered alarms (A1, ..A10) in a wind farm

during the studied period. As the figure shows, there are alarms with high appearing

frequency. In this case, it is necessary to analyse the cause and try to solve it.

Figure 10. Alarm appearance frequency per wind turbine during a year

Having available the adequate information, it is possible to establish a comparison between

the obtained production and the estimated in the wind energy assessment. Deviations obtained

are used to adjust the project’s revenue forecast.

If the objective of the study is an audit of the wind farm, where the studied time period is

long, the results help to take decisions for the future of the wind farm management as

repowering plans, change of contractual guarantees or maintenance contract renovation.

Long-term periodic studies make it possible to track the energy yield and the turbines

availability. This allows a quick detection of problems and its dynamical solution, increasing

the overall project efficiency.

Finally, specific computations can be carried out, including new parameters as economic

values due to energy sale, production assessment, etc., adapting the scope of the report to

different requirements.

CONCLUSIONS

The making of either periodic reports or audits that control the wind farm performance

involves an improvement in the knowledge of the performance of the wind farms, critical

points and exploitation decision-making of wind farms. These analyses are carrying out just

based on the treatment and interpretation of data registered at SCADA systems.

The wind farm performance characterization, detailing the performance of each wind turbine,

facilitates the monitoring of the energy yield and the viability of the project. The information

obtained allows detecting operational changes due to component deterioration, sensor

problems or production losses.

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Long-term periodic studies allow to quickly correct the deviations and to keep the wind farm

in a high performance state.

Finally, the cost of the registered data treatment is very low because it does not require new

dispositive installation. The data study inversion can be considered basic to improve the wind

farm efficiency.

REFERENCES

Beltrán, J., Llombart, A. and Guerrero, J.J. (2009). “A bin method with data range selection

for detection of nacelle anemometers faults”, EWEC.

Huang, Z. and Chalabi, Z.S. (1995). “Use of time-series analysis to model and forecast wind

speed” J. Wind Eng. Ind. Aerodyn., 56: 31 l-322

IEC 61400-12-1:2005. Wind turbines - Part 12-1: Power performance measurements of

electricity producing wind turbines Ed. 1

Li, S., Wunsch, D.C., O’Hair, E.A. and G.Giesselmann, M. (2001). “Using Neural Networks

to Estimate Wind Turbine Power Generation”, IEEE trans. On Energy Conversion, Vol.

16, Nº 3, pp 276-282, September 2001.

Llombart, A., Pueyo, C., Fandos, J.M., Guerrero, J.J. Robust data filtering in wind power

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Llombart, A., Watson, S. J., Llombart, D. and Fandos, J.M. (2005). “Power Curve

Characterization I: improving the bin method” ICREPQ, 2005.

REAL DECRETO 661/2007, de 25 de mayo, por el que se regula la actividad de producción

de energía eléctrica en régimen especial. BOE nº 126 pp22846-22873 de 26 de Mayo de

2007

Rousseeuw; P. and Leroy, A. (1987). Robust Regression and Outlier Detection (John Wiley,

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