Reactive power control of grid-connected wind farm based on adaptive dynamic programming

Neurocomputing

Volumn 125, Issue , 2014, Pages 125-133

  Tang, Yufei ^a   He, Haibo ^a   Ni, Zhen ^a   Wen, Jinyu ^b   Sui, Xianchao ^b  

^a University of Rhode Island   (United States)

^b HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Adaptive control; Adaptive dynamic programming; Computational intelligence; Doubly fed induction generator; Power system; Wind farm

Indexed keywords

ADAPTIVE CONTROL; ADAPTIVE DYNAMIC PROGRAMMING; DOUBLY FED INDUCTION GENERATOR (DFIG); POWER SYSTEM; WIND FARM;

ARTIFICIAL INTELLIGENCE; ASYNCHRONOUS GENERATORS; CONTROLLERS; DYNAMIC PROGRAMMING; ELECTRIC FAULT CURRENTS; ELECTRIC UTILITIES; REACTIVE POWER; WIND POWER;

ELECTRIC POWER TRANSMISSION NETWORKS;

ADAPTIVE DYNAMIC PROGRAMMING; ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; BIOENERGY; COMPUTATIONAL INTELLIGENCE; DOUBLY FED INDUCTION GENERATOR; ELECTRIC POTENTIAL; ELECTRICITY; FREQUENCY MODULATION; GENERATOR; LEARNING ALGORITHM; OSCILLATION; PRIORITY JOURNAL; PROCESS CONTROL; SIMULATION; WIND TURBINE;

EID: 84888052243     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.07.046     Document Type: Article

References (32)

1. U.S. Department of Energy, The smart grid: an introduction, 2008.
2. A. Ipakchi, F. Albuyeh, Grid of the future, IEEE Power Energy Mag. (2009) 52-62.
3. IEEE Smart Grid Initiative. http://smartgrid.ieee.org/.
4. European Commission, European smart grids technology platform, 2006.
5. Grauers A. Efficiency of three wind energy generator system. IEEE Trans. Energy Convers. 1996, 11(3):650-657.
6. Pena R., Clear J.C., Asher G.M. Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind energy generation. IEE Proc.: Electr. Power Appl. 1996, 143(3):231-241.
7. V. Akhmatov, Analysis of Dynamic Behavior of Electric Power Systems with Large Amount of Wind Power, Ph.D. Dissertation, Technical University of Denmark, Kgs. Lyngby, Denmark, April 2003.
8. Wu F., Zhang X.P., Godferey K., Ju P. Small signal stability analysis and optimal control of a wind turbine with doubly fed induction generator. IET Gener. Transm. Distrib. 2007, 1(5):751-760.
9. Wu F., Zhang X.P., Ju P., Sterling M.J.H. Decentralized nonlinear control of wind turbine with doubly fed induction generator. IEEE Trans. Power Syst. 2008, 23(2):613-621.
10. Y. Tang, P. Ju, H. He, C. Qin, F. Wu, Optimized control of DFIG-based wind generation using sensitivity analysis and particle swarm optimization, IEEE Trans. Smart Grid, 4 (1), (2013) 509-520,. http://dx.doi.org/10.1109/TSG.2013.2237795.
11. El Mokadem M., Courtecuisse V., Saudemont C., Robyns B., Deuse J. Fuzzy logic supervisor-based primary frequency control experiments of a variable-speed wind generator. IEEE Trans. Power Syst. 2009, 24(1):407-417.
12. Jabr H.M., Lu D., Kar N.C. Design and implementation of neuro-fuzzy vector control for wind-driven doubly-fed induction generator. IEEE Trans. Sustainable Energy 2011, 2(4):404-413.
13. Yang L., Xu Z., Ostergaard J., Dong Z.Y., Wong K.P. Advanced control strategy of DFIG wind turbines for power system fault ride through. IEEE Trans. Power Syst. 2011, 27(2):713-722.
14. Hansen A.D., Sørensen P., Iov F., Blaabjerg F. Control of variable speed wind turbines with doubly-fed induction generators. Wind Eng. 2004, 28(4):411-434.
15. Mullane A., Lightbody G., Yacamini R. Wind-turbine fault ride-through enhancement. IEEE Trans. Power Syst. 2005, 20(4):1929-1937.
16. Han C., Huang A.Q., Baran M.E., Bhattacharya S., Litzenberger W., Anderson L., Johnson A.L., Edris A.-A. STATCOM impact study on the integration of a large wind farm into a weak loop power system. IEEE Trans. Energy Convers. 2008, 23(1):226-233.
17. Werbos P.J. Computational intelligence for the smart grid-history, challenges, and opportunities. IEEE Comput. Intell. Mag. 2011, 6(3):14-21.
18. Venayagamoorthy G.K. Dynamic, stochastic, computational, and scalable technologies for smart grids. IEEE Comput. Intell. Mag. 2011, 6(3):22-35.
19. Qiao W., Harley R.G., Venayagamoorthy G.K. Coordinated reactive power control of a large wind farm and a STATCOM using heuristic dynamic programming. IEEE Trans. Energy Convers. 2009, 24(2):493-503.
20. Qiao W., Venayagamoorthy G.K., Harley R.G. Real-time implementation of a STATCOM on a wind farm equipped with doubly fed induction generators. IEEE Trans. Ind. Appl. 2009, 45(1):98-107.
21. Fu J., He H., Zhou X. Adaptive learning and control for mimo system based on adaptive dynamic programming. IEEE Trans. Neural Networks 2011, 22(7):1133-1148.
22. Si J., Wang Y.T. Online learning control by association and reinforcement. IEEE Trans. Neural Networks 2001, 12(2):264-276.
23. Enns R., Si J. Apache helicopter stabilization using neuro-dynamic programming. J. Guid. Control Dyn. 2002, 25(1):19-25.
24. Enns R., Si J. Helicopter trimming and tracking control using direct neural dynamic programming. IEEE Trans. Neural Networks 2003, 14(4):929-939.
25. Lu C., Si J., Xie X. Direct heuristic dynamic programming for damping oscillations in a large power system. IEEE Trans. Syst. Man Cybern. Part B. Cybern. 2008, 38(4):1008-1013.
26. X. Fang, H. He, Z. Ni, Y. Tang, Learning and control in virtual reality for machine intelligence, in: 2012 Third International Conference on Intelligent Control and Information Processing (ICICIP), July 2012, pp. 63-67.
27. Y. Tang, H. He, J. Wen, Adaptive control for an HVDC transmission link with FACTs and a wind farm, in: Proceedings of the IEEE Innovative Smart Grid Technologies Conference (ISGT'13), Washington, February, 2013.
28. Y. Tang, H. He, J. Wen, Comparative study between HDP and PSS on DFIG damping control, in: IEEE Symposium Series on Computational Intelligence, May 2013.
29. H. He, Self-Adaptive Systems for Machine Intelligence, Wiley, 2011, ISBN: 978-0-470-34396-8.
30. He H., Ni Z., Fu J. A three-network architecture for on-line learning and optimization based on adaptive dynamic programming. Neurocomputing 2012, 78(1):3-13.
31. Z. Ni, H. He, J. Wen, Adaptive learning in tracking control based on the dual critic network design, IEEE Trans. Neural Networks Learn. Syst., PP (99), 1,1, 0. http://dx.doi.org/10.1109/TNNLS.2013.2247627.
32. H. He, Z. Ni, D. Zhao, Learning and optimization in hierarchical adaptive critic design, in: Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, Wiley-IEEE Press, 2013, pp. 78-95.