MPPT of Doubly-Fed Induction Generator in Wind Farm Using SPSA Algorithm

Abstract:

In order to make wind power generation system be free from outside interference, and make doubly-fed induction generator (DFIG) easily operate at maximum power point under variable wind speed, simultaneous perturbation stochastic approximation (SPSA) algorithm for tracking maximum power of wind power generation system is proposed in this paper. SPSA algorithm indirectly controls DFIG speed through adjusting duty cycle of power converter, makes system operate at maximum power point under any wind speed, thus the maximum power output is transmitted to the load. SPSA algorithm not only does not need to set up PID three parameter values, but also does not anemometer and tachometer in practical applications. Simulation results show no matter how wind speed changes, SPSA algorithm can effectively improve power output of wind power generation system, and make tip speed ratio and power coefficient be near optimum value, reduce system cost.

• Periodical: Advanced Materials Research (Volumes 608-609)
• Pages: 662-667
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.608-609.662
• Online since: December 2012
• Authors: Hong Hai Kuang, Zheng Qiu Wu, Sheng Qing Li
• Keywords: Double Fed Induction Generation (DFIG), MPPT, SPSA, Wind Power Generation

References

[1] Wu Wenkai,N.Pongratananukul,et al.DSP-based multiple peak power tracking for expandable power system[C].IEEE Applied Power Electronics conference and Exposition,2003.

DOI: 10.1109/apec.2003.1179263

[2] Eftichios Koutroulis,Kostas kalaitzakis.Design of a Maximum Power Tracking System for Wind Energy Conversion Applications[J].IEEE Transactions on Industrial Electronics,2006,53(2): 486-494.

DOI: 10.1109/tie.2006.870658

[3] Wang Jianbin. Maximum power point tracking methods for wind energy conversion system[D]. Taiwan: Chung Yuan Christian University, 2005.

[4] Wen Zongxiu. Hybrid method of maximum power tracking for wind power generator[D]. Taiwan: Tatung University, 2009.

[5] Huang Yiming.Neural networks basked MPPT for wind power generation[D].Taiwan: Chung Yuan Christian University,2006.

[6] Adam Mirecki, Xavier Roboam and Frederic Richardiau. Architecture Complexity and Energy Efficiency of Small Wind Turbines[J]. IEEE Transactions on Industrial Electronics, 2007,54( 1):641-645.

DOI: 10.1109/tie.2006.885456

[7] M. Veerachary, T. Senjyu, and K. Uezato. Neural Network Based Maximum Power Point Tracking of Coupled Inductor Interleaved Boost Converter Supplied PV System using Fuzzy Controller[I]. IEEE Transactions on Industrial Electronics, 2003,50( 4): 749-758.

DOI: 10.1109/tie.2003.814762

[8] T.tanaka and T.Toumiya,Output control by hill Climbing method for a small wind power generating system, Rrnewable Enery, 1997,12(4): 387-400.

DOI: 10.1016/s0960-1481(97)00055-4

[9] Jia Yaoqing, Cao Binggang, YangZhongqing. A fast response control mothod for wind generation [J]. Acta Energiae Solaris Sinica, 2004, 9(2): 171–176.

[10] Lv Xuede.MPPT control for permanent magnet synchronous generator in wind farm using simultaneous perturbation stochastic approximation algorithm[D].Taiwan:Chung Yuan Christian University, 2006.

[11] Wei Yili, Han Suxian, Shi Shengzhi.The modeling and simulation of the combined wind speed in the wind power system[J].Renewable Energy Resources, 2010, 28(2):19-20.