coe nit rourkela

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Progress Report CoE on Practical Renewable Energy SystemCoE on Practical Renewable Energy System

Coordinator: Prof. Bidyadhar Subudhi

Department of Electrical EngineeringNational Institute of Technology, Rourkela

Amount of Money Sanctioned:

Funds Received and Utilisation

1) F. No. 16‐16/2013‐TS. VII (General) dated 26th March 2013: Rs. 155 Lakhs2) F. No. 16‐16/2013‐TS. VII (SC) dated 26th March 2013: Rs. 30 Lakhs3) F. No. 16‐16/2013‐TS. VII (ST) dated 26th March 2013: Rs. 15 Lakhs

Total: Rs. 200 Lakhs

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Detailed Expenditure of CoE-Renewable Energy Sources as on 15th June,2014

Head of Expenditure Amount of Expenditure as on 15th June 2014

Procurement 2,16,15,548/-Procurement 2,16,15,548/(P.O. has been issued Wind Energy Control System and PV Hybrid Energy System)PO is under process for 100 kW PV Experimental Set up

Assistantship 3,24,000/-(Ph.D. Scholars)

R& D 5,17,833

IOC 1,77,528

Total Expenditure Rs. 2,26,34,909/- (Two Crore Twenty Six Lakh Thirty Four Thousand Nine Hundred Nine only)

InvestigatorsSlNo.

Name Qualification Areas of Interest

1 Prof. B. D.Subudhi,EE Dept.

PhD(Sheffield) Control of PV and Wind Energy Systems, Smart Grid Control, Active Power Filtering

2 Prof.P.K.Ray,EE Dept

PhD(NIT Rourkela) Estimation & Filtering in Power System, Renewable Energy SourcesEE Dept. Sources

3 Prof. K.K. MohapatraEC Dept.

PhD(IIT Kanpur) Embedded Systems , Power Electronics

4. Prof. R.K. SahooME Dept.

PhD (IIT Kharagpur) Heat Transfer, Cryogenic Engineering

5 Prof K.B. MohantyEE Dept.

PhD(IIT Kharagpur) Power Electronic Drives, Vector Drives and Torque Controlled Drives

6 Prof A K Panda PhD(Utkal University) Soft Switching Converters AC Drives and Power Quality6 Prof A.K. PandaEE Dept.

PhD(Utkal University) Soft Switching Converters, AC Drives and Power Quality Analysis in PE Circuits

7 Prof S. SamantEE Dept.

PhD(IIT Kharagpur) Modeling and Control of Power Electronics Converters

8 Prof S.MaityEE Dept.

PhD(IIT Kharagpur) Nonlinear Dynamics in Power Electronics Circuits

9 Prof S.GhoshEE Dept

PhD(IIT Kharagpur) Decentralized Control, Robust control and Time-delay systems

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External CollaboratorsProf S. Mishra, EE, IIT D

Prof. Y.S.R. Sood, NIT Hamirpur, p

Sl. No. Name of the Scholar Title of Thesis Name of the Supervisor(s) Status

1 R.Pradhan Development of New ParameterExtraction Schemes and MaximumPower Point Controllers forPhotovoltaic Power Systems

B.Subudhi Awarded in March 2013

2 Basant Kumar Dash Development of Control Strategies forHybrid Electric Vehicles

B.Subudhi & R.Reddy Synopsis is over and will submit by 30th

June 20143 O.P.Suresh Active and Reactive Power Control of

WECSB.Subudhi July 2010, In

progress4 R.Panigrahi Power quality improvement using

Active power filtersP.C.Panda, B.Subudhi Since Jan 2011, In

progress5 Ventkatratnam Embbeded Control Design of PV

systemK.K.Mahapatra and B.Subudhi

Since july 2011

6 Satyabrata Sahoo Hybrid Energy System B.Subudhi & G.Panda Since Jan 20137 Brundaban Sahoo Robust Control of Wind Energy System B.Subudhi & D.Bagarty Since July 20128 Sasmita Behera Grid Interface of Wind Energy System B.Subudhi & B.B.Pati Since July 20119 Raja Rout Real-time Implementation of Control

Algorithms for an solar powered AUVB.Subudhi Since July 2012

10 Satyajit Mohanty Control of Grid Connected PV System B.Subudhi and P.K.Ray Since Jan 2013

11 Om Prakash Pahari Active & reactive power control of grid connected PV system

B Subudhi Since July 2013

12 Nirjharini Sahoo Active Power Filtering with application to Distributed Generations

G.Panda & B.Subudhi Since Jan 2013

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PhD students hired by CoESatyajit Dash

Active and Reactive Power Control of Wind Energy SystemSupervisor: Prof. B SubudhiSince July 2013

Snehaprava SwainControl of Wind Energy SystemSupervisor: Prof. P.K.RaySince July 2013

u10

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Research CollaborationsIBM: Smart Grid ControlQueen Marry University of London: Modelling & Control of Renewable Quee a y U ve s ty o o do : ode g & Co t o o e ewab eEnergy SystemsFar East Federal Univ., Vladivostok, Russia: Control of Ocean Energy and Solar Powered Autonomous Underwater Robots

Research Foci of the CoE1. PV System Control

•Parameter extraction and MPPT (Maximum Power point Tracking)•Robust Control of Grid Interface PV System•Development of Global MPPT algorithms for PV system under non-uniform Insolation•Grid Integration Issues

2. Wind Energy Conversion System and Control•Active and Reactive Power Control•MPPT(Maximum Power Point Tracking)•Wind Speed Estimation with Wavelet Neural Networks•WECS Grid Integration Issue

3. Distributed Power Generation•Development of Control Techniques in Shunt Active Power Filter

•Development of Real-time Estimation and Filtering Algorithms with Applications to Distributed Generation

4. Fuel Cell u6

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Sl Sponsoring

Agency

Title of Project Amount of grant (INR

Lakh)

Period Co-investigator

1 DST Estimation & Filtering with Application to Distributed

38 2013-2016 P.K.Ray

Some Sponsored Research Funding Secured

Generations2 TEQIP Establishment of Centre of

Excellence on Renewable Energy System

500 2013-2015 P.K.Ray

3 DST UKIERI

Modelling & Control of Hybrid Renewable Energy

Sources

33 2013-2015 M. H. ShaheedQueen Mary,

Univ.of LondonP K RP.K.Ray

4 CPRI Study of power quality problems and counter

measures in present power systems using power electronics devices.

21 2011-2014 P.C.Panda

Sl. No.

Sponsoring Agency

Title of Project Project Cost (INR Lakh)

Period Investigators

1 CPRI Development of New Estimationand Control Techniques for ShuntActive Power Filter with

30 2 Years P.K.Ray

B.D. Subudhi

Sponsored Research Funding Applied

Active Power Filter withApplications to Aircraft Systems

1 MNRE Development of a PhasorMeasurement Unit for Wide AreaMeasurement System in a SmartGrid

36 3 Years P.K.Ray

B.D. Subudhi

2 DST-SERI (A)

Development of a CompactControl Unit for a roof top PV

44 3 Years P.K.Ray

B.D. SubudhiSystem

3 DST-SERI (B)

Development of Grid IntegrationStrategies for Large PV System .

142 3 Years B.D. Subudhi

P.K.Ray

S.Ghosh

P. Kale

u3

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PhD Theses AwardedR.Pradhan

Development of New Parameter Extraction Schemes and Maximum Power Point Controllers for Photovoltaic Power Systemsfor Photovoltaic Power SystemsSupervisor: Prof. B.SubudhiAwarded in March 2013

PhD Theses Submitted K. C. Bhuyan

DEVELOPMENT OF CONTROLLERS USING FPGA FOR FUEL CELLS IN STANDALONE AND UTILITY APPLICATIONS STANDALONE AND UTILITY APPLICATIONS Supervisor: Prof. K.K. Mahapatra

MTech Theses Awarded1.Wind Speed Estimation by Neural Networks,Prangya Parimita Pradhan

Awarded in May 2014, Prof. B.Subudhi (Supervisor)y , ( p )

2. Modelling of PV System and Prediction of Solar Insolation,Reema Mohanty

Awarded in May 2014, Prof. B.Subudhi (Supervisor)

3. State Estimation through wireless Communication for a Smart Grid

Joysankar Dwibedy,Awarded in May 2014, Prof. B.Subudhi (Supervisor)

4 SEMS for WECS Vidyabhusan Awarded in May 2014 Prof B Subudhi 4. SEMS for WECS,Vidyabhusan,Awarded in May 2014, Prof. B.Subudhi (Supervisor)

5. Fuzzy PI Control of WECS,Suchismita Acharya,Awarded in May 2014, Prof. B.Subudhi (Supervisor)

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MTech Theses Awarded (Contd.)6. Power System Frequency Estimation by Linear and Nonlinear Techniques, Nilesh N Sindhe, Awarded in May 2014, Prof. P. K. Ray (Supervisor)

7. Automatic Load Frequency Control of Multi area Power System, Sushmita Ekka, Awarded in May 2014, Prof. P. K. Ray (Supervisor)

8. Power System Harmonics Estimation using different Signal Processing Techniques, Rishikesh K. Jaiswal, Awarded in May 2014, Prof. P. K. Ray (Supervisor)

9. Economic Load Dispatch in Power System using PSO, Bhishma Narayan Prasad, d d fAwarded in May 2014, Prof. P. K. Ray (Supervisor)

10. A Hybrid Recursive Least Square PSO based algorithm for Power System Harmonics Estimation, Mahasweta Biswal, Awarded in May 2014, Prof. P. K. Ray (Supervisor)

Journal Publications from CoER.Panigrahi, P.C.Panda and B.Subudhi, A Robust Extended Complex Kalman Filter and Sliding Mode Control based Shunt Active Power Filter, Electric Power Components and Systems, vol.42,no.5,pp.520–532, 2014

B.Subudhi and R.Pradhan, A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power System, IEEE Trans. Sustainable Energy, vol.4, no.1, pp.89-98, Jan 2013.y , gy, , , pp , J

B. Subudhi and S.S. Ge, Sliding mode Control and observer based slip ratio control of Electric and Hybrid Electric Vehicles, IEEE Trans. on Intelligent Transportation System, vol.13, no.4, pp.1617-1626,2012

P.K.Ray and B.Subudhi, Ensemble Kalman Filtering Algorithm applied to Power System Harmonics Estimation, IEEE Trans. on Instrumentation and Measurement , vol.61, no.12, pp.3216-3224, 2012

B. Subudhi, P. K. Ray and S. Ghosh, “Variable Leaky LMS Algorithm Based Power System Frequency Estimation” IET Science, Measurement & Technology, vol.6, issue 4, pp. 288-297, 2012

P K R d B S b dhi BFO d RLS l h f P S H E A li d S f P.K.Ray and B.Subudhi, BFO optimized RLS algorithm for Power System Harmonics Estimation, Applied Soft Computing (Elsevier), 12 (2012) 1965–1977

B. Subudhi and R.Pradhan, A Comparative Study on Solar Array Parameter Extraction Methods, International Journal of Renewable Energy Technology (Inderscience) , vol.3, no.3, pp-315, 2012

B.Subudhi and O.P.Suresh, Sliding mode approach to torque and pitch control for an wind energy system using FPGA, Journal of Archives of Control Sciences, Volume 22(LVIII), 2012, No. 3, pp. 255–272

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Conference PublicationsB.K.Dash and B.Subudhi, A Fuzzy Adaptive Sliding Mode Slip Ratio Controller of a HEV, 2013 IEEE Intl. Conf. on FuzzySystems (FUZZ-IEEE 2013), Hyderabad, India July 2013

R.Pradhan and B.Suudhi, An Adaptive Prediction Error Filter for Photovoltaic Power Harvesting Applications, IndiaConference (INDICON), 2012 Annual IEEE, 2012, Kochi, 7-10 Dec. 2012

O.P.Suresh and B.Subudhi, Sliding mode approach to torque and pitch control for an wind energy system using FPGA,, g pp q p gy y g ,India Conference (INDICON), 2012 Annual IEEE, 2012 , Kochi, 7-10 Dec. 2012

R.Pradhan and B.Subudhi, A Steepest-Descent based Maximum Power Point Tracking Technique for a Photovoltaic PowerSystem, Intl Conf.Allahabad, 2012

R.Pradhan and B.Subudhi, A New Digital Double Integral Sliding Mode Maximum Power Point Tracker for PhotovoltaicPower Generation Application, 3rd IEEE International Conference on Sustainable Energy Technologies, IEEE ICSET 2012,Kathmandu, Nepal, 24-27 Sep 2012.

R Panigrahi P C Panda and B Subudhi Comparison of Performances of Hysteresis and Dead Beat Controllers in ActiveR.Panigrahi, P.C.Panda and B.Subudhi, Comparison of Performances of Hysteresis and Dead Beat Controllers in ActivePower, 3rd IEEE International Conference on Sustainable Energy Technologies, IEEE ICSET 2012, Kathmandu, Nepal, 24-27 Sep 2012.Kathmandu

R.Panigrahi, P.C.Panda and B.Subudhi, New strategy for generation of reference current in active power filters withdistortion in line voltage, IEEE Conf. Madras, 2012

u9

Short Term Courses by CoE Control of Renewable Power Generation Systems:

7-11 July 2014yCoordinators: B Subudhi, P K Ray and S Ghosh

u9 write some more for example your publuser, 6/17/2014

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Research Works of the CoE

1. PV System Control1.1 Parameter extraction of PV System and Adaptive MPPT Control Design

Students Name: Raseswari Pradhan (Ph.D awarded)

Supervisor Name: Prof B.D.Subudhi

Objectives j

To propose efficient algorithms in terms of fast convergence and accuracy for extraction of parameters of a PV panel.

To develop a mathematical model of a PV panel having obtained parameters by the proposed parameter extraction algorithms.

To evaluate efficiency of the proposed parameter extraction algorithms through both simulated and experimental results.

To review on the reported approaches to MPPT design.

To propose new MPPT algorithms such that the accuracy in tracking reference voltage can be achieved in less time.

To propose new adaptive controllers for MPPT considering the uncertainties of the PV system dynamics due to changing solar

irradiance at different weather conditions.

To simulate the proposed MPPT algorithms in MATLB/SIMULINK and real-time simulation model in OPAL-RT simulator.

To develop a prototype of a PV control and experimental implementation of the proposed MPPT algorithms.

To evaluate the efficacy of the proposed MPPT algorithms.

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Experimental Set-upSPARTAN 3AFPGA board

Voltage

sensor

DC/DC Boost converter

Temperature DisplayFPGA board

Proto-type PV SystemPV Panel

LoadDAQDSO

PVPanel

DC-DCBoost

Converter1-

InverterLoadLC

FilterRC

Filter

21

FPGABoard

PC

SignalConditioner

v1 i1 v2 i2 i3v3

pulse1 Pulse

Conditioned signals6

(6-no.)Data and Command

Flow

Block diagram of PV system prototype

Prototype Experimental Set-up of the PV Control systemThe PV system is a stand-alone type. It consists of PV array, DC/DC boost converter, inverter, SPARTAN 3A FPGA board, signal conditioners (voltage and current sensors), personal computer and analog filtering circuits.

Prototype experimental set-up of the PV system Vi Microsystems Spartan-3A DSP Trainer Kit withfollowing components such as (1) SPARTAN-3A DSPProcessor, (2) PLL Clock Setting, (3) JTAG Connector,(4) RS232 Serial Port, (5) Parallel Port, (6) LCD Display,(7) PWM Connector, (8) SDA Bus Connector, (9) PowerSupply and (10) USB

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(a) Parameter Extraction Problem Formulation

( )

output current ipv of a single-diode five-parameter model

( )0 exp 1p pv pv s s

php pv pv

s shpv

b

q n v i n v ii

ak TR R

I In R

+ + = − − −

P-V Characteristics

Proposed Hybrid NRM parameter extraction

I-V Characteristics

24

Method I(NRM),Method II(Comprehensive method), Proposed Hybrid NRM

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Normal condition

Simulation Results Proposed BFO parameter extraction algorithm

Shedding condition

BFO technique is better than PSO technique25

Absolute MPP Power Error empp (%) at STC

PV Module

Proposed

Hybrid

NRM

PSO ComprehensiveProposed

BFO

Proposed Hybrid NRM parameter extraction simplifiedthis problem to a two-order problem. In this algorithm,existing NRM is modified by adding an additional

Contributions

Proposed Hybrid NRM parameter extraction and BFO parameter extraction algorithm comparison

NRM

Shell SQ85 0.0044 0.0354 0.0473 0.00133

PM648 0.00485 0.0083 0.0334 0.00257

SSI-M6-205 0.003 0.004 0.013 0.00012

g y giterative algorithm.

Proposed BFO parameter extraction algorithm is aglobal optimization algorithm. It also has boundarylimit of parameters.

The PV model parameters such as Iph, I0, Rs, Rsh andith th th diti t t d

Shell ST40 0.00134 0.0016 0.0018 0.000507

Computational time (s)

PM648 3.58 5.65 14.36 2.36

SSI-M6-205 3.184309 6.13 14.28 1.86

a vary with the weather conditions are extracted.

The comparison of the computational time andabsolute MPP power error by the proposed BFOmethod with that of hybrid NRM, PSO andcomprehensive method for a number of PV panels areshown in the table.

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(b) MPPT Problem FormulationThere exists a single point called MPP at any solar irradiance at which output power of the PV system is the

maximum. 0pv

pv

dpdv

=

Operating point of a PV system can be adjusted to match

At MPP

p g p y j

with this calculated Vref using a DC/DC boost converter to

operate at MPPT.

For this a controller block is employed in the PV system

to accomplish the above adjustment in the DC/DC boost

converter.

D i i ff i MPPT l i h h i h

PV system with MPPT

Designing effective MPPT algorithms together with a

controller are the important tasks in view of achieving

maximum power extraction from an installed PV system.

This MPPT can estimate the MPPs of a PV system

on-line using a RLS based system identifier and a

Proposed Auto‐tuning based Adaptive MPPT (ATAMPPT)

on-line using a RLS based system identifier and a

NRM technique. This MPPT takes place on-line

and uses the on-line estimated MPPs of the PV

panel.

28

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Simulation Results

Proposed ATAMPPT

Real time Simulation Results

29

Observation: Auto-tuned PID-controller can track the reference MPP

voltage Vref of 48.9V with minimal fluctuations in PV voltage. It is

also capable of tracking MPP at variable weather conditions.

Simulation Results

Proposed ATAMPPT

Tracking time and voltage fluctuation are less in case of ATAMPPT compared to P&O-MPPT, INC-MPPT and APO-MPPT

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GM

Simulation Results Proposed ATAMPPT

PM

PV system with ATAMPPT is stable

Gain margin (GM) = 40dBPhase margin (PM) = 19.260

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Real-time Simulation Results

V-A

PO

[V] A

V-IN

C [V

] B

Proposed ATAMPPT

v PV

Time [s]

A = 25mV v PV

Time [s]

B = 40mV

V]

32

v PV-

P&

O [V

Time [s]

C

C = 27mV

A < B < C

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ipv-ATAMPPT [A]

Real-time Simulation Results Proposed ATAMPPT

vpv-ATAMPPT [V]

vdc-ATAMPPT [V]

Time [s]

D

Real-Tracking time and voltage fluctuation are less in case of ATAMPPT compared to P&O-MPPT, INC-MPPT and APO-MPPT

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Time [s]D = 15mV

D < A < B < C

P&O-MPPT

Experimental ResultsProposed ATAMPPT

ATAMPPT

Less tracking time and voltage fluctuation in case of ATAMPPT compared to P&O-MPPT34

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A new ATAMPPT is proposed for PV systems

MPPs of a PV system are estimated on-line using a RLS based systemidentifier and a NRM technique

Summary and Contribution

Its effectiveness are verified by comparing with existing MPPTs such asP&O, INC and APO. It is found that the ATAMPPT has less tracking error,tracking time and voltage fluctuations.

Simulation in MATLAB/SIMULINK, real-time simulation in Opal-RT andexperimental results using the prototype set-up are presented to validate theperformance of the ATAMPPT

It provides effective tracking of MPP as well as regulates the load voltagefixed at different weather conditions

35

The proposed ATAMPPT has NRM as MPPT algorithm and an auto-tuned adaptive PID-controller. Initial condition of the NRM algorithm iscarefully chosen so it is not suffering from singularity problem

Contributions

A new MPPT called APEFC MPPT designed using recursive least square (RLS) with a variable forgetting factor and

adaptive predictive error filter based controlling concept.

This MPPT alleviates the shortcoming of ATAMPPT i.e. requirement of accurate estimated PV panel parameters in a

(c) Adaptive Predictive Error Filter based MPPT

short period hence may be inappropriate in handling quick weather variations.

Further, this MPPT considers external disturbances. The proposed APEFC is an adaptive PID-controller. Here, the

APEF part of this APEFC MPPT is an adaptive PD-controller where the proportional and derivative gains are tuned

on-line by pole-placement algorithm.

The integral term only acts as catalyst in the APEFC-MPPT and hence speeds up the dynamic response of the PVThe integral term only acts as catalyst in the APEFC MPPT and hence speeds up the dynamic response of the PV

system. Therefore, an empirically chosen fixed value of integral gain has been used.

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Changing Solar irradiance

Simulation Results Proposed new RLS-APEFC-MPPT Controller

Changing Solar Cell Temperature

37

Bode plot


Both GM and PM are positive

Also gm pmω ω< PV system with Proposed APEFC-MPPT is stable38

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SSI-M6-205 PV Panel


Proto-type PV System

Less Steady-state error and Tracking time in case of RLS-APEFC-MPPT39

Started late but settles fast

Faster tracking speedT ki ti l

Proposed APEFC-MPPT


Faster tracking speed

Tracking time less

Proposed APEFC-MPPT

Faster tracking speedReaches near zero earlier

Proposed APEFC-MPPT is more efficient than RLS-APEFC-MPPT 40

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MPPT Controller evmpp (mV) Tracking time (ms)

LMS-APEFC (µ=0.1) 4 80

ATAMPPT 1.6 40

SSI-M6-205 PV Panel


NLMS-APEFC 0.5 38

GALSLMS-APEFC 0.5 25

RLS-APEFC 0.2 5

Proposed APEFC 0.05 3

MPPTevmpp (V)

Tracking time

Proposed APEFC-MPPT is better in both SSI-M6-205 and Prototype PV panels

Controllerevmpp (V)

(ms)

ProposedAPEFC-MPPT

0.03 1

RLS-APEFC-

MPPT0.1 3

Proto-type PV System

41

ATAMPPT

Experimental Results Proposed new RLS-APEFC-MPPT Controller

APEFC-MPPT

Less tracking time and voltage fluctuation in case of APEFC-MPPT compared to ATAMPPT42

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This Chapter proposed a new RLS-APEFC-MPPT controller.

The RLS-APEFC-MPPT has variable forgetting factor.

This proposed MPPT is designed with an adaptive PID-controllerh it t t d b d ti di ti filt


where its parameter are tuned by an adaptive predictive filter

It possesses both faster response and lesser steady-state error thanthat of LMS-APEFC, NLMS-APEFC, GALSLMS-APEFC basedMPPTs and ATAMPPT.

The effectiveness and accuracy of the proposed APEFC techniquehave been verified through simulation and experimental studies

This proposed APEFC MPPT is found to be computationally lesscomplex, and effective in tracking MPP of a PV system.

43

(d) Double Integral Sliding Mode MPPT and Adaptive Double Integral Sliding Mode

MPPT• The RLS-APEFC MPPT is computationally complex with still high steady-state error and chattering in PV voltage.

• By using double integral sliding mode controller (DISMC), a DC/DC converter, it exhibits fast dynamic response, less steady-

state error and reduced chattering in PV voltage.

• Therefore two new MPPTs are developed exploiting the concept of DISMC for a stand-alone PV system such as DISMC-

MPPT and adaptive DISMC-MPPT. These MPPTs are designed in such a way that there is guaranteed stability achieved.

• In literature, two distinct sliding surfaces have been used namely sliding surface 1 (SS1) and sliding surface 2 (SS2). DISMC

with SS1 has less number of components and control variables but has higher chattering in PV voltage whereas DISMC with

SS1 has less chattering and fast tracking but has more number of control variables hence costly and complex.SS1 has less chattering and fast tracking but has more number of control variables hence costly and complex.

• Therefore, a new sliding surface is selected and used to design two DISMC-MPPTs. In the proposed DISMC-MPPT, sliding

surface is assumed fixed whilst in the proposed adaptive DISMC-MPPT; sliding surface is updated with changing weather

conditions.

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Simulation Results Proposed DISMC-MPPT and an Adaptive DISMC-MPPT

Less Steady-state error and Tracking time in case of DISMC-MPPT45

Parameters SMC-MPPT ISMC-MPPT ProposedDISMC-MPPT

Vref (V) 28.0438 28.0438 28.0438

h (V) 28 0511 28 049 28 047


h1 (V) 28.0511 28.049 28.047

h2 (V) 28.0444 28.039 28.0406

h (V) 0.77 0.01 0.0064

SSE (mV) 3.9 0.2 0.0028

SSE (% of Vref) 13 0.8 0.01

Maximum 0 4562 6 9562 5 4562

Overshoot (V)0.4562 6.9562 5.4562

Settling Time (s) > 1 0.12 0.11

46

Overall tracking performances in case of Proposed DISMC-MPPT are better than SMC and ISMC MPPTs

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DISMC-MPPT ISMC-MPPT


Proposed DISMC-MPPT has less chattering

SMC-MPPT

47

radi

ance

s [W

/m2 ] i

ii

iii


Sola

r Irr

Time[s]

i: 1000W/m2, ii: 700W/m2

and iii: 500W/m2

Proposed DISMC-MPPT is better than SMC and ISMC MPPTs48

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Experimental Results Proposed DISMC-MPPT and an Adaptive DISMC-MPPT

DISMC-MPPT

Although tracking time is higher but less voltage fluctuation in case of DISMC-MPPT compared to APEFC-MPPT

49

Controller

Properties

Tan’s

DISMC-

MPPT

Jiao’s

DISMC-

MPPT

Proposed

DISMC-

MPPT

Proposed

Adaptive

DISMC-

MPPT

Number of current sensors Three Two Two Two


Sliding surface parameters Fixed Fixed Fixed Adaptive

Complexity More Less Less Less

Control VariablesVpv,Vdc,

ipv, iL& iC1

Vpv,Vdc,

ipv, & iC1

Vpv,Vdc,

ipv, & iC1

Vpv,Vdc,

ipv, & iC1Cost more less less less

Reaching time 22 ms < 5ms 110 ms < 5 ms

Settling-time 22 ms > 25ms 5 ms 2.5 ms

Chattering 1.43V 9.3V 28mV 12.5mV

SSE 0.26 V 0.59 V 0.01 V 0.002 VSettling-time during step-change in input 2.5ms 20ms 2ms 1.2ms

Maximum overshoot duringstep-change in input 0.5 volts 4 V 5.4562 V 1 V

50 Overall MPPT property case of Proposed DISMC-MPPT is better

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DISMC-MPPT

Volta

ge [V

]

Tracking Time(1.4s)

Open-circuit

1.6VA B

Experimental Results Proposed DISMC-MPPT and an Adaptive DISMC-MPPT

Adaptive DISMC-MPPTge [V

] Tracking Time(0.9s)

1V

PV V Open circuit

conditionMPP condition

Time [s]

Less tracking time and voltage fluctuation in case of Adaptive DISMC-MPPT compared to DISMC-MPPT

PV V

olta

g

MPP voltageOpen-circuit

voltage

Time [s]

1VA B

51

• A DISMC-MPPT and an Adaptive DISMC-MPPT are proposed in this chapter.

• These two DISMC-MPPTs have been constructed with new sliding surfaces.


• Simple control structure and fixed frequency operation.

• In DISMC-MPPT, controller coefficients are fixed whilst in the adaptive DISMC-MPPT, controller coefficients are updating with changing weather conditions.

• Proposed fixed gain DISMC-MPPT is found to be more efficient than SMC-MPPT and ISMC-MPPT.MPPT and ISMC MPPT.

• Proposed Adaptive DISMC-MPPT is found to be more efficient than the fixedgain DISMC-MPPT, DISMC-MPPT with Tan’s sliding surface, DISMC-MPPTwith Jiao’s sliding surface and APEFC-MPPT

52

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27

(e) Self-tuned IPID-controller based MPPTThe DISMC-MPPT with adaptive sliding surface yields better tracking results than that of P&O-MPPT, INC-MPPT,

APO-MPPT, ATAMPPT and APEFC-MPPT with less voltage and current fluctuations, less tracking error and less

tracking time but, the performance of adaptive DISMC-MPPT is dependent on the selection of its sliding surface.

Therefore, there is need of designing a new MPPT using a black-box model of PV system that is identified on-lineTherefore, there is need of designing a new MPPT using a black box model of PV system that is identified on line

considering error in measurement (disturbances).

Hence, a self-tuning MPPT with IPID-controller and RLS identifier for a PV system is proposed. In this self-tuned

MPPT, tracking of MPP is done in a single step by taking as cost function. This MPPT uses incremental PID-

controller.

pv

pv

dpdv

Closely matching

Simulation Results Proposed a self-tuned MPPT designed with IPID-controller

.

54 PV curve with RLS-estimated parameters of PV system matching closely to actual curve

Less estimation error

8/29/2014

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Simulation Results Proposed a self-tuned MPPT designed with IPID-controller

55 Performance of Self-tuned MPPT is better than GMV, Pole-placement and Auto-tuned MPPTs

Simulation Results

Proposed a self-tuned MPPT designed with IPID-controller

0.5V

Volta

ge [V

]

MPP condition

Open-circuit condition

A

BExperimental

Result

56

Time [s]

PV V

Tracking Time(0.6s)

Tracking time is 0.6s and voltage fluctuation is 0.5V

8/29/2014

29

Tracking Results

PV V

olta

ge[V

]re

nt

TrackingTime

MPP conditionOpen-circuit condition

Experimental Results Proposed a self-tuned MPPT designed with IPID-controller

Switching signal

PV C

urr

[A]

Tracking Time

Time [s]

al g

57

Gat

e si

gna

Time [s]

Type of

MPPT

Simulated MPP Tracking Characteristics

Tracking

time (s)

Voltage

chattering (V)SSE (V)

Maximum

overshoot (V)

MPPT

Efficiency (%)

P&O 0.3 2 3.5 0.5 96.5

Comparison Between Different MPPT Proposed a self-tuned MPPT designed with IPID-controller

P&O 0.3 2 3.5 0.5 96.5

Incremental

Conductance0.27 2.4 2.5 0.5 97.5

Adaptive

P&O0.17 1.5 2.2 0.3 97.8

Auto-tuned 0.04 0.063 2 3 98

APEFC 0.005 0.05 0.0920 0.25 99.9

DISMC 0 025 4 6 0 7 5 4562 99 3

Proposed Self-tuned MPPT is better than other MPPTs58

DISMC 0.025 4.6 0.7 5.4562 99.3

Adaptive

DISMC0.005 0.028 0.08 1 99.92

Self-tuned 0.004 0.04 0.03 0.15 99.98

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Type of MPPT

Experimental MPP Tracking

Characteristics

Tracking time Voltage chattering

Comparison Between Different MPPT Proposed a self-tuned MPPT designed with IPID-controller

(s) (V)

P&O 0.9 8

Auto-tuned 0.8 5

APEFC 0.7 3

DISMC 0.76 2.5

Adaptive DISMC 0.45 2

S lf t d 0 26 1

Proposed Self-tuned MPPT is better than other MPPTs59

Self-tuned 0.26 1

This chapter proposed a self-tuned MPPT designed with IPID-controller.

For designing this MPPT, PV system is considered as an ARX model. P t f thi ARX d l id tifi d i RLS l ith


Parameters of this ARX model are identified using RLS algorithm.

With simulation and experimental studies, it is found that this MPPT is able to provide less fluctuation in PV voltage.

It has also has less steady state tracking error SSE.

Its MPP tracking time is very less. Further, maximum overshoot during the transient period of MPP tracking is lessduring the transient period of MPP tracking is less.

60

8/29/2014

31

References

1. R. Singh, Randhir, Y. R. Sood and N. P. Padhy, "Development of renewable energy sources for Indian power sector moving

towards competitive electricity market", General Meeting in Power & Energy Society (PES'09), pp. 1-6, 26-30 Jun 2009,

Calgary, Alberta, Canada.

2. R. Dhakal, J. Kofford, B. Logue, M. Ropp, D. Galipeau, and Y. Xingzhong, "Electrodeposited AlSb compoundg pp p g g p p

semiconductor for thin film solar cells", 34th IEEE in Photovoltaic Specialists Conference (PVSC), pp. 1699-1701, 7-12

Jun 2009, Philadelphia, PA, USA.

3. P. Denholm and R. M. M. Margolis, "Evaluating the limits of solar photovoltaics (PV) in traditional electric power

systems", Energy policy, vol. 35, no. 5, pp. 2852-2861, 2007.

4. W. Xiao, M. G. J. Lind, W. G. Dunford and C. Antoine, "Real-time identification of optimal operating points in

h t lt i t " IEEE T ti I d t i l El t i l 53 4 1017 1026 2006photovoltaic power systems", IEEE Transactions on Industrial Electronics, vol. 53, no. 4, 1017-1026, 2006.

5. N. Femia, P. Giovanni, S. Giovanni and V. Massimo, "Optimization of perturb and observe maximum power point tracking

method", IEEE Transactions on Power Electronics, vol. 20, no. 4, pp. 963-973, 2005.

6. M. Datta, S. Tomonobu, Y. Atsushi, F. Toshihisa and K. Chul-Hwan Kim, "A coordinated control method for leveling PV

output power fluctuations of PV–diesel hybrid systems connected to isolated power utility," IEEE Transactions on Energy

Conversion, vol. 24, no. 1, pp. 153-162, 2009.

Publications1. B. Subudhi and R. Pradhan, “A Comparative Study on PV Panel Parameter Extraction Methods”, International Journal on

Renewable Energy Technology (Inderscience), vol. 3, no. 3, pp. 295-315, 2012.2. B. Subudhi and R. Pradhan, “A Comparative Study of Maximum Power Point Tracking Techniques for Photovoltaic

System”, IEEE Trans. on Sustainable Energy, vol. 4, no. 1, pp. 89-98, 2013.3. R. Pradhan and B. Subudhi, “An Adaptive Double-Integral-Sliding-Mode-Maximum-Power-Point-Tracker for a

Photovoltaic System”, Control Engineering Practice (Revised Copy submitted).4. R. Pradhan and B. Subudhi, “Double Integral Sliding Mode MPPT Control of a Photovoltaic System”, IEEE Trans. on, g g y ,

Control Systems Technology (Under Review).5. R. Pradhan and B. Subudhi, “An Adaptive Predictive Error Filter based Maximum Power Point Tracker for a Photovoltaic

System”, IET Power Electronics (Revised Copy submitted).6. R. Pradhan and B. Subudhi, “Design and Real-time Implementation of a New Auto-tuned Adaptive MPPT Control for a

Photovoltaic System”, International journal on Power and Energy System, Elsivier (Under Review).7. R. Pradhan and B. Subudhi, “A Self-Tuned Adaptive Maximum Power Point Tracker for a Photovoltaic System”, IEEE

Trans. on Sustainable Energy (Revised Copy submitted).8. R. Pradhan, B. Subudhi and P.K. Ray, “A Real-time linearized Maximum Power Point Tracker for Photovoltaic System”,

IEEE PEDS-2013, 22-25 April 2013, Kitakyushu, Japan.

9. R. Pradhan and B. Subudhi, “An Adaptive Prediction Error Filter for Photovoltaic Power Harvesting Applications”, IEEEINDICON-2012, Kochi, Kerala, 7-9 December, 2012.

10. R. Pradhan and B. Subudhi, “A Steepest-Descent based Maximum Power Point Tracking Technique for a PhotovoltaicPower System”, IEEE ICPCES-2012, MNNIT, Allahabad, UP, 17-19 December 2012.

8/29/2014

32

11. R. Pradhan and B. Subudhi, “A New Digital Double Integral Sliding Mode Maximum Power Point Tracker for PhotovoltaicPower Generation Application”, ICSET-2012, Kathmandu, Nepal, 24-27 September 2012.

12. B. Subudhi and R. Pradhan, “Characteristics Evaluation and Parameter Extraction of a Solar Array Based on ExperimentalAnalysis”, IEEE PEDS-2011, Singapore 5-8 December 2011.

13. B. Subudhi and R. Pradhan, “A Comparative Study on Solar Parameter Extraction Methods”, NSC-2010, Surathkal,Karnataka, 10-12 December, 2010.

14. R. Pradhan, B. Subudhi and P.K. Ray, “A Real-time linearized Maximum Power Point Tracker for Photovoltaic System”,IEEE PEDS-2013, 22-25 April 2013, Kitakyushu, Japan.

15. R. Pradhan and B. Subudhi, “An Adaptive Prediction Error Filter for Photovoltaic Power Harvesting Applications”, IEEEINDICON-2012, Kochi, Kerala, 7-9 December, 2012.

16. R. Pradhan and B. Subudhi, “A Steepest-Descent based Maximum Power Point Tracking Technique for a PhotovoltaicPower System”, IEEE ICPCES-2012, MNNIT, Allahabad, UP, 17-19 December 2012.

17. R. Pradhan and B. Subudhi, “A New Digital Double Integral Sliding Mode Maximum Power Point Tracker for Photovoltaic17. R. Pradhan and B. Subudhi, A New Digital Double Integral Sliding Mode Maximum Power Point Tracker for PhotovoltaicPower Generation Application”, ICSET-2012, Kathmandu, Nepal, 24-27 September 2012.

18. B. Subudhi and R. Pradhan, “Characteristics Evaluation and Parameter Extraction of a Solar Array Based on ExperimentalAnalysis”, IEEE PEDS-2011, Singapore 5-8 December 2011.

19. B. Subudhi and R. Pradhan, “A Comparative Study on Solar Parameter Extraction Methods”, NSC-2010, Surathkal,Karnataka, 10-12 December, 2010.

1.2 Robust Control of Grid Interface PV SystemStudents Name: Om Prakash Pahari (PhD)Supervisor Name: Prof B. Subudhi

ObjectiveTo study and investigate PV system Grid connection issues.

To develop intelligent MPPT based on the insolation variation or weather conditions.

To investigate on Power Quality/active and reactive power control of a grid connected PV system.

8/29/2014

33

Grid-Interfaced PV systems are capable to contribute power demands of the grid however; the major concerns of such

systems are safety and instability they cause to existing power grid. The efficiency of such system varies abruptly with

intermittent solar insolation, weather and grid faults.

Therefore, this research focuses to develop experimental set-up of Grid connected PV system and study the MPPT, Power

References

1. Kulkarni, A.; John, V., "Mitigation of Lower Order Harmonics in a Grid-Connected Single-Phase PV Inverter," IEEE

Transactions on Power Electronics vol 28 no 11 pp 5024 5037 Nov 2013

, p p p y y ,

Quality, grid faults, islanding, Grid synchronisation etc. while maintaining the IEEE 1547 standards.

Transactions on Power Electronics, vol.28, no.11, pp.5024,5037, Nov. 2013

2. Yan Zhou; Hui Li; Liming Liu, "Integrated Autonomous Voltage Regulation and Islanding Detection for High Penetration

PV Applications," IEEE Transactions on Power Electronics, vol.28, no.6, pp.2826,2841, June 2013

3. Patel, H.; Agarwal, V., "A Single-Stage Single-Phase Transformer-Less Doubly Grounded Grid-Connected PV Interface,"

IEEE Transactions on Energy Conversion, vol.24, no.1, pp.93,101, March 2009

1.3 Development of Global MPPT algorithms for PV system under non-uniform Insolation

Students Name: Satyajit Mohanty (PhD)

Supervisor Name: Prof B.D.Subudhi and Prof. P.K Ray

Objective:Objective:

To develop MPPT control algorithms in order to generate maximum power output from the PV system with changing

insolation levels, temperature variations and other environmental conditions.

To investigate for MPPT algorithms required for standalone and Grid connected PV systems.

Work done:

St d th ff t f ti l h di PV h t i tiStudy the effects of partial shading on PV array characteristics

Due to uniform insolation, a single peak appears in the P-V characteristics of the PV system.

0 20 40 60 80 100 1206

7

8

9

10

11

PV array Volltage(V)

PV

arr

ay C

urre

nt(A

)

0 20 40 60 80 100 1200

200

400

600

800

1000

1200

PV array voltage(V)

PV a

rray

pow

er(W

)

8/29/2014

34

0 20 40 60 80 100 12010

12

14

16

18

20

22

Cur

rent

of P

V a

rray

0 20 40 60 80 100 1200

200

400

600

800

1000

1200

1400

1600

Pow

er fr

om P

V a

rray(

W)

Output characteristics of PV array with non-uniform insolation (a) I-V characteristics (b)P-V characteristics

0 20 40 60 80 100 120Voltage of PV array

0 20 40 60 80 100 120Voltage across PV array(V)

Due to variation in solar insolation and partial shading conditions, a no of multiple peaks appear in the P‐V characteristics ofthe PV system

150

200

250

300

rent

(mA)

100

150

200

250

rren

t(mA

)

V‐I curve obtained from solar simulatorusing frosted glass and 50% shading

0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.60

50

100

Voltage(V)

Cur

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.550

50

100

Voltage(V)

Cur

References

[1] Kun Ding, X. Bian, H.H Liu, and Tao Peng, “A MATLAB Simulink Based PV module model and its application under

conditions of non-uniform irradiance”, IEEE Trans. on Energy Conversion, vol. 27, no.4, 2012, pp. 864-871.

[2] Young-Hyok Ji, Doo-Yong Jung, Jun-Gu Kim, Jae-Hyung Kim, Tae-Won Lee and Chung-Yuen Won, “A Real Maximum Power

Point Tracking Method for Mismatching Compensation in PV Array Under Partially Shaded Conditions”, IEEE Trans. on Power

Electronics, vol. 26, no. 4, Apr. 2011, pp. 1001-1009.

[3] Hiren Patel and Vivek Agarwal, “Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded

Conditions”, IEEE Trans. on Industrial Electronics, vol. 55, no. 4, Apr. 2008, pp.302-310.

[4] Hiren Patel and Vivek Agarwal “MATLAB-Based Modeling to Study the Effects of Partial Shading on PV Array[4] Hiren Patel and Vivek Agarwal, MATLAB Based Modeling to Study the Effects of Partial Shading on PV Array

Characteristics”, IEEE Trans. on Energy Conversion, vol. 23, no. 1, Mar. 2008, pp.302-310.

8/29/2014

35

2. Wind Energy Conversion System and Control2.1 Active and Reactive Power Control of DFIG based Grid connected WECS

Student Name: Satyajit Das (PhD)


Obj tiObjectives

To develop an algorithm to control the generation of active power with change in wind speed and extraction of maximum power

from wind.

As well as controlling the reactive power for unity power factor operation of WECS.

Work layout

The vector control (vc) is implemented on DFIG based WECS using flux oriented and stator voltage oriented methods for

decoupling control of active and reactive power control.

In these methods, the stator flux and mechanical torque (or the stator power components) are assumed to be decoupled and

controlled by the rotor current components along the real and imaginary axes of stator flux reference frame.

500

1000

1500

2000

dc li

nk v

olta

ge

The GSC (grid side converter) provides the

constant dc link voltage and also

responsible for controlling the reactive

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-500

0

Time

p g

power flow into the grid

2

4

6

8

10

12

14

16

18x 10

6

Rea

ctiv

e po

wer

(var

)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3x 10

6

Time

outp

ut p

ower

(w)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

Time(sec)

The active power flow in to the grid is

approximately tracking the reference

active power, which conforms the proper

function of the MSC.

8/29/2014

36

Future Work

1. To investigate on power quality, active and reactive power control of a grid connected WECS by implementing different

control technique such as Direct Torque Control (DTC), Direct Power Control (DPC) using MATLAB/ Simulink.

2. To develop an experimental set-up of Grid connected WECS.

3 To design new strategy for controlling active and reactive power for the Wind Energy Conversion System and3. To design new strategy for controlling active and reactive power for the Wind Energy Conversion System and

implementation of the same in real time experimental set up.

References[1] S. Muller, M. Deicke, and R. W. De Doncker, “Doubly fed induction generator systems for wind turbines,”IEEE Ind. Appl. Mag., vol. 8, no. 3,pp. 26–33, May/Jun. 2002.

[2] J. Hu, Y. He, L. Xu, and B. W. Williams, “Improved control of DFIG systems during network unbalance using PI-R current regulators ” IEEE Trans Ind Electron vol 56 no 2 pp 439 451 Feb 2009regulators, IEEE Trans. Ind. Electron., vol. 56, no. 2, pp. 439–451, Feb. 2009.

[3] R. Pena, J. C. Clare, and G. M. Asher, “Doubly fed induction generator using back-to-back PWM converter and is application to variable-speed wind–energy generation,” Proc. IEE B Electr. Power Appl., vol. 143,no. 3, pp. 231–241, May 1996.

[4] G. S. Buja and M. P. Kazmierkowski, “Direct torque control of PWM inverter-fed ac motors-a survey,” IEEE Trans. Ind. Electron., vol. 51,no. 4, pp. 744–575, Aug. 2004.

2.2 MPPT (Maximum Power point Tracking)Students Name: O.P.Suresh (Ph.D), Suchismita Acharya(M.Tech)


ObjectiveDevelopment of new MPPT technique for DFIG based Grid connected WECS

Power coefficient Vs TSR Power coefficient vs tip speed ratio at zero pitch angle

Power coefficient vs tip speed ratio at different pitch angle

8/29/2014

37

References

[1] T. Thiringer and J. Lindres, “Control by variable rotor speed of a fixed-pitch wind turbine operating in a wide speed

range,” IEEE Trans. Energy Conversion, vol. 8, pp. 520–526, Sept. 1993.

[2] I. Eskandarzadeh and H. Ince, “Modeling and output power optimization of a wind turbine driven doubly output

induction generator,” Proc. IEEE – Electr. Power Applicat, vol.141, no.2, pp. 33–38, March.1994.

Publications

[3] Fengxiang Wang, Chengwu Lin, and Longya Xu “A chopping and doubly-fed adjustable speed system without bi-

dirctional converter,” IEEE Trans. Ind. Applicat., vol. 20, pp. 2393–2397, October 2002.

[4] Yifan Tang, and Longya Xu “A flexible active and reactive power control strategy for a variable speed constant frequency

generating system,” IEEE Trans. Power Electronic, vol. 10, pp. 472–478, July 1995.

1. O.P.Suresh and B.Subudhi, Sliding mode approach to torque and pitch control for an wind energy system using FPGA,

India Conference (INDICON), 2012 Annual IEEE, 2012 , Kochi, 7-10 Dec. 2012

2. O.P.Suresh and B.Subudhi, Sliding mode approach to torque and pitch control for an wind energy system using FPGA,

India Conference (INDICON), 2012 Annual IEEE, 2012 , Kochi, 7-10 Dec. 2012

Objectives and work layout

I t ti f i d i i ti t bl

2.3 Wind Speed Estimation with Wavelet Neural NetworksStudent Name: Prangya Parimita Pradhan (M.Tech)


Integration of wind energy in existing power system causes problemsPower Quality,StabilityPower Dispatching

Control methodEffective wind speed/ power forecasting.

WORK LAYOUTThe available wind speed sample (2982) collected from National Renewable Energy Laboratory (NREL)[1] has been

decomposed up to 5th level with Maximum Overlap Discrete Wavelet transform(MODWT).

Each decomposed sample forecasted individually with different neural networks.

Finally each level forecasted outputs are added to get the wind speed forecast up to 30 hour ahead.

8/29/2014

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1.Wind speed estimation with feed-forward multilayer neural network:

This neural network consists of three layer as input layer, hidden layer and output layer.

Si id l f i h b l d i i f i i h hidd l d fi d

Wind Speed Forecast

Sigmoidal function has been selected as activation function in the hidden layer, defined as

Where x is the input to the hidden layer that is the decomposed wind speed samples (detail coefficients of each

level from 1st to 5th level and approximate coefficients of 5th level) patterns.

2. Wind speed estimation with wavelet neural network:

Feed-forward wavelet neural network also consists of three layer same as feed-forward multilayer neural

k h i i l hidd l d h l

11

( ) xexφ −+=(.)φ

network that is input layer, hidden layer and the output layer.

Instead of sigmoidal function Mexican hat has been selected as mother wavelet that is the activation

function in the wavelet layer ( hidden layer), defined as

20.5(( ) / )2, ( ) (1 ( ) ) iu b ai

a b iu bu e

aψ − −−

= − ×

(.)ψ

1

2

3

4

spee

d(m

/s),e

rror

predictedactualerror

Results of wind speed estimation with neural network

Mean absolute error(MAE)=10.14%

0 10 20 30 40 50 60 70 80 90 100-1

0

time(hour)

win

d

wind speed forecast of 100 successive hour ahead forecasting with multilayer feed-forward neural network

3

4

),err

or

predictedactualerror

0 10 20 30 40 50 60 70 80 90 100-1

0

1

2

time(hour)

win

d sp

eed(

m/s

)

wind speed forecast of 100 successive hour ahead forecasting with feed-forward wavelet neural network

Mean absolute error(MAE)=1.53%

8/29/2014

39

Future Work

Comparative studies with other methods for wind speed forecasting.

Wind power forecast will be carried out by recursively taking the previous wind power forecast values along with the wind

speed forecast obtained from neural network.

References[1]National Renewable Energy Laboratory[Online]. Available: http://www.nrel.gov/wind/integrationdatasets/eastern/data.html.

[2] “Indian Wind Energy and Economy,” Indianwindpower.com. Retrieved 2009-09-17.

[3] Shu Fan, James R. Liao, Ryuichi Yokoyama, Luonan Chen and Wei-Jen Lee, “Forecasting the Wind Generation Using a Two-

To design a control strategy to overcome the limitation of existing approaches .

Stage Network Based on Meteorological Information,” IEEE Trans. Energy Convers., vol. 24, no. 2, June 2009.

[4] Kanna Bhaskar and S. N. Singh, “AWNN-Assisted wind power forecasting using feed-forward neural network, “IEEE

Transactions on sustainable energy, vol.3,no. 2,april 2012.

[5] Xinghuo Yu, M. OnderEfe, and OkyayKaynak, “A General Back propagation Algorithm for Feed forward Neural Networks

Learning,” IEEE Transactions on neural networks, vol.13, no.1, january2002.

2.4 WECS Grid Integration IssuesStudents Name: Snehaprava Swain (Ph.D)

Supervisor Name: Prof P.K. Ray

Objectives1)power system security and stability analysis

2) Power quality improvement

Work DoneMaintaining grid reliability by precise synchronization of voltage and current:

Here an ac system of 60amp, 120v is analysed before synchronisation as well as after synchronization. This system analysis is analogous to the WTCS analysis inter linked with the power system grid.

Case‐I :(Before Synchronization)Case‐II:(After Synchronization)

current is slightly out of phase with the voltage.current is in phase with the voltage.

8/29/2014

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Future ScopeReliability assessment of the integrated system by calculating different reliability indices.

References

[1].M. Moeini-Aghtaie , A. Abbaspour , and M. Fotuhi-Firuzabad, “Incorporating large-scale distant wind farms in

probabilistic transmission expansion planning—Part I: Theory and algorithm ” IEEE Trans Power Syst vol 27 no 3 ppprobabilistic transmission expansion planning Part I: Theory and algorithm, IEEE Trans. Power Syst., vol. 27, no. 3, pp.

1585–1593, Aug. 2012.

[2]T. Ackermann, Wind Power in Power Systems. Hoboken, NJ, USA: Wiley, 2008.

[3] R. Karki and R. Billinton, “Reliability/cost implications of PV and wind energy utilization in small isolated power

systems,” IEEE Trans. Energy Convers., vol. 16, no. 4, pp. 368–373, Dec. 2001.

[4] Y. Gao and R. Billinton, “Adequacy assessment of generating systems containing wind power considering wind speed

correlation,” IET Renew. Power Generat., vol. 3, no. 2, pp. 217–226, Jun. 2009.

[5] P. Hu, R. Karki, and R. Billinton, “Reliability evaluation of generating systems containing wind power and energy

storage,” IET Generat., Trans., Distrib., vol. 3, no. 8, pp. 783–791, Aug. 2009

3.Distributed Power Generation

3.1 Development of Control Techniques in Shunt Active Power FilterStudents Name: Rakhee Panigrahi(Ph.D)

Objectives

To develop different control strategies in Shunt Active Power Filter (SAPF) to provide an adequate dynamic behavior under

consideration of grid perturbations

Supervisor Name: Prof B Subudhi, Prof P.C. Panda

Control strategies :

Hysteresis controller (HC) and Dead beat (DB) Controller

Sliding Mode Current (SMC) controller

Model Predictive based current controller (MPC)

8/29/2014

41

Functional Structure of SAPF Hysteresis and

Dead beat Current Controller:

sV

sV Li ci DCV

sL sR

cR

cLci

si Li

DCV

DCV

'DCV

sV

Lisi

*refi

si ci1Ci

2Ci

1PV

Control Block Diagram using Hysteresis(HC)

Control Block Diagram using Dead Beat

control (DBC) method

Li

DCV +−I

DCVI

++

ˆ I

Ci

ˆ II

Ci

Ci + −Ci

ei

1PV

I

CV CV+ −

SV

LV

Ci

SV

Simulation Results

100

150

200

250

300300

Vol

tage

(V)

0

30

Am

plitu

de(A

)

0 0.02 0.04 0.060

50

Time(s)0 0.05 0.1 0.15 0.2 0.25 0.3-30

Time(s)

A

150

200

250

300300

age(

V)

0

30

litud

e(A

)

(a)

0 0.02 0.04 0.060

50

100

Time(s)

Vol

ta

0 0.05 0.1 0.15 0.2 0.25 0.3-30Time(s)

Am

pl

(a) Capacitor voltage and compensating waveforms in DB method (b) Capacitor voltage and compensating waveforms in HC method

(b)

8/29/2014

42

Sliding Mode Controller

Labciav

SVLOAD

bvcv

sabci

fL

R

sL sR

∗

dcvfR

⇑

⇑

−+

PI CONTROLLERdcv∗

i

Fabci

PWM

SLIDING MODECONTROLLERsabci

sabci∗ smi

abcUavbvcv

UNIT VECTOR GENERATIONUSING RECKF

FUNDAMENTAL ESTIMATIONAND

PROPOSED

MULTIPLIER

REFERENCE GENERATION

Structure of SMC based SAPF

Simulation Results

50

0

50

100

150

(V) a

nd C

urre

nt(A

)

Source CurrentSource Voltage

KF

-50

0

50

100

150

age(

V) a

nd C

urre

nt(A

)


EKF

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5-150

-100

-50

Time(s)

Vol

tage

(

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5

-150

-100

Time(s)

Vol

ta

50

100

150

d C

urre

nt(A

)


ECKF

50

100

150

Cur

rent

(A)


RECKF

(a) (b)

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5-150

-100

-50

0

Time(s)

Vol

tage

(V) a

nd

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5-150

-100

-50

0

Time(s)

Vol

tage

(V) a

nd

Source voltage and source current waveforms in SMC based SAPF for (a) KF, (b) EKF, (c) ECKF, (d) RECKF

(c) (d)

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Structure of MPC based SAPF

PIController

dcv smidcv∗

S I

MPC

avbvcv

Unit vectorGeneration

usingKalman Filter

sabci∗ iα∗

iβ∗

Switchingstate

Selector

aSbScS

( 1)i kα +( 1)i k

FabcI +−LabcI

SabcI

dcvαβ

abcPredictive

Model

( )i kα

( )i kβ

( 1)i kβ +

Experimental Implementation with dSPACE1104 board

(a)

(b)

(a) Experimental Setup, (b) dSPACE1104 CLP board

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Future Plan1. The Shunt Active power filter applications can be extended to multiple actions such as hybrid filter, STATCOM, unified power

quality conditioner (UPQC).

2. The control strategy of SAPF can be extended in nonlinear framework such as nonlinear H-infinity filtering, nonlinear quadratic

Gaussian controller and the estimation approaches can be extended to Unscented Kalman filter Particle filterGaussian controller and the estimation approaches can be extended to Unscented Kalman filter, Particle filter.

References

[1] J. M. Kanieski, R. Cardoso, and H. A. Grundling, “Kalman filter based control system for power quality conditioning

devices”, IEEE Trans.on Industrial Electron., doi:10.1109/TIE, 2012.

[2] K. H. Kwan, P. L. So, and Y. C. Chu, “An output regulation-based unified power quality conditioner with Kalman filters”,[ ] , , , p g p q y ,

IEEE Trans. on Industrial Electron., vol. 59, pp.4248-4262, Nov. 2012.

[3] M. Rivera, V. Yaramasu, and J. Rodriguez, “Model Predictive current control of two-level four leg inverters-part II:

experimental implementation and validation,” IEEE Trans. Power Electron., vol. 28, no. 7, pp. 3469–3478, Jul. 2013.

[4] N. Gupta, S. P. Singh and R. C. Bansal, “A digital signal processor based performance evaluation of three-phase four-wire

shunt active filter for harmonic elimination, reactive power compensation and balancing of non-linear loads under non-ideal

mains voltages,” Elect. Power Compon. Syst., vol. 40, no. 10, pp. 1119-1148, 2012.

[5] A. Chandra, B. Singh, and B. N. Singh, “An Improved Control Algorithm of Shunt Active Filter for Voltage Regulation,

H i Eli i i P F C i d B l i f N li L d ” IEEE T P El l 15Harmonic Elimination, Power-Factor Correction, and Balancing of Nonlinear Loads,” IEEE Trans. Power Electron., vol. 15,

no. 3, pp. 495–507, May. 2000.

Publications[1] R.Panigrahi, P.C.Panda and B.Subudhi, A Robust Extended Complex Kalman Filter and Sliding Mode Control based Shunt

Active Power Filter, Electric Power Components and Systems, vol.42,no.5,pp.520–532, 2014.

[2] R Panigrahi P C Panda and B Subudhi Comparison of Performances of Hysteresis and Dead Beat Controllers in Active[2] R.Panigrahi, P.C.Panda and B.Subudhi, Comparison of Performances of Hysteresis and Dead Beat Controllers in Active

Power, 3rd IEEE International Conference on Sustainable Energy Technologies, IEEE ICSET 2012, Kathmandu, Nepal, 24-27

Sep 2012.Kathmandu.

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[3] R.Panigrahi, P.C.Panda and B.Subudhi, New strategy for generation of reference current in active power filters with

distortion in line voltage, IEEE Conf. Madras, 2012

[4] P.K.Ray and B.Subudhi, Ensemble Kalman Filtering Algorithm applied to Power System Harmonics Estimation, IEEE

Trans. on Instrumentation and Measurement , vol.61, no.12, pp.3216-3224, 2012

[5] B. Subudhi, P. K. Ray and S. Ghosh, “Variable Leaky LMS Algorithm Based Power System Frequency Estimation” IET

S i M t & T h l l 6 i 4 288 297 2012Science, Measurement & Technology, vol.6, issue 4, pp. 288-297, 2012

[6] P.K.Ray and B.Subudhi, BFO optimized RLS algorithm for Power System Harmonics Estimation, Applied Soft

Computing (Elsevier), 12 (2012) 1965–1977

3.2. Development of Real-time Estimation and Filtering Algorithms with Applications to Distributed Generation

Objectives and work layout

Student Name: Aurobinda Bag (PhD)

Supervisor Name: Prof B.D.Subudhi, Prof P.K. Ray

Objectives and work layoutTo develop efficient and robust nonlinear estimation algorithms in nonlinear framework for power system frequency and harmonics estimation of

an AC Micro-grid comprising of two Renewable Energy Sources such as PV and WES.

To utilize the developed estimation algorithms for designing active power filters of the Micro-grid system and to verify the techniques using

MATLAB and OPAL -RT.

To implement the developed estimation and filtering algorithms on the hardware (Microgrid, Active Filter).

WORK LAYOUTThe project intends to analyse and develop advanced filter solutions based on combinations of passive filters and active filters. The use of

passive filters is almost standard for harmonic mitigation; therefore, the primary focus is to develop active filter solutions. For the active

power filters, the power electronic converters already present in the hybrid energy system.

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The objective is to have an optimized combination of active and passive filter. At present the harmonic mitigation is mainly

achieved by large passive filters in the wind turbines and at the grid connection substation. In addition to reviewing this

approach, use of active filters either in combination with the existing and/or new passive filters will be investigated.

References

[1] Damoun Ahmadi, Jin Wang,” Online Selective Harmonic Compensation and Power Generation With Distributed Energy Resources”

IEEE Trans. on Power Electronics, Vol. 29, no. 7, pp.3738-3747, July 2014.

[2] Koen J. P. Macken, Koen Vanthournout, Jeroen Van den Keybus, Geert Deconinck, and Ronnie J. M. Belmans,“Distributed Control of

Renewable Generation Units With Integrated Active Filter” IEEE Trans. on Power Electronics, Vol. 19, no. 5, pp.1551-1561, Sep 2004.

[3] R. Chudamani, K. Vasudevan and C.S.Ramalingam “ Real time Estimation of Power System Frequency using nonlinear Least Squares” IEEE Trans. on

Power Delivery, vol. 24, no.3, pp. 1021-1028, 2009.

[4] H. Akagi, A. Nabae and S. Atoh “Control Strategy of Active Power Filters Using Multiple- Voltage Source PWM Converters", IEEE Trans. on Ind.Applicat.

Vol.1, no.3, pp. 460-465, May/June 1986.

Laboratory Development

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Laboratory DevelopmentRenewable Power Generation Control LabWind Energy Conversion System(WECS)PV SystemMNRE10 KW PV ModuleSpecification:(A) Wind Energy Control System1. Wind Turbine and Generator

2kW DFIG generator with a PMSM based wind turbine emulator2. Voltage source inverters:

(i) To feed the PMSM(ii) To be connected to the output of DFIG for grid connection

3. Programmable Power Source:(i) dsPIC based PWM Controller(ii) Voltage Source Inverter

4. RTDS (Real Time Simulator)

5. Sensors:

(i) Temperature sensor

(ii) Air speed sensor

(iii) Speed sensor(iii) Speed sensor

3. DC-DC Converter 2kW:

(i) dsPIC Based PWM Converter

(ii) Voltage Source Inverter

(iii) DC to DC Bidirectional buck boost converter for charging the battery

4. Bi-directional Converters:

(i) 4 Nos of high speed IGBT or MOSFET semiconductor devices are to be used

(ii) 4 Nos of isolated high speed driver circuits are used

(iii) 2 Nos of hall effect current tranducers used for sensing the input and output of the converter current

(iv) 2 Nos of hall effect voltage tranducers used for sensing the input and output of the converter voltage

(v) In boost mode input is 100v and output is 300v

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(vi) In buck mode input is 300v and output is 100v(vii) Power rating is 200w(viii) dv/dt protection is available for all IGBT (snubber circuit)(ix) dsPIC 4011 digital controller is used for generating the gate signal for IGBT with over load protection

5. VSI inverters:(i)VPE SPARTAN 3A/3A DSP BOARD(ii) Voltage source inverter 2 nos

6. Data acquisitionData acquisition systemFPGA Based high speed data acquisition system with anti-aliasing filter

7. Non-linear load(i)Diode rectifier(1200V, 60A)(ii)3 KW resistive load with 9 selector switch

8. Batterynos. of 12V, 200A per hour battery is connected in sensing to get 100V DC output

9. Wind Energy Emulator(i)Energy Emulator using DC motor coupled with 1kW slip ring induction motor(ii)Dc shunt moor coupled with 1kW slip ring IM with sensor set up.(iii)VPE Spartan 3A/3A DSP Board(iv)Power module for DFIG(v)Shunt active filter

10. Voltage source inverter

Under Process100 Kw PV SystemMNRE Solar Insolation recording setup

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Thank You

coe nit rourkela

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