Hai-ying DONG(董海鹰), Chuan-hua SUN(孙传华)
School of Automation & Electrical Engineering, Lanzhou Jiaotong U niversity, Lanzhou 730070, China
Abstract-For the characteristics of wind power generation syst em is multivariable, nonlinear and random, in this paper the neural network PID adaptive control is adopted.The size of pitch angle is adjusted in time to impro ve the performance of power control. The PID parameters are corrected by the gra dient descent method, and Radial Basis Function (RBF) neural network is used as the system identifier in this method. Simulation results show that by using neur al network adaptive PID controller the generator power control can inhibit effec tively the speed and affect the output power of generator. The dynamic performan ce and robustness of the controlled system is good, and the performance of wind power system is improved.
Key words-wind power generation; power control; PID ada ptive control; neural network
Manuscript Number: 1674-8042(2010)02-0173-05
dio: 10.3969/j.issn.1674-8042.2010.02.18
References
[1] M. Aminian, F. Aminian, 2000. Neural-network based analogcircuit fau lt diagnosis using wavelet transform as preprocessor. IEEE Trans Circu its Syst-Ⅱ, 44(3): 151- 156.
[2]Xin-fu Song, Bo Liang, 2009. Wind power system pitch control based on fuzzy self-learning emendation control theory. Power System Protec tion and Control, 37(16): 50-53.
[3]Hui Li, K. L. Shi, P. G. McLaren, 2005. Neural-network-based senso rless maximum wind energy capture wth compensated power coefficient. I EEE Transactions on Industry Applications, 41(6): 1548-1556.
[4]Bang-sheng Fu, Bing-xin Wu, Xiao-lei Wang, 2009. Design of pitch -controlled system based on neural network. Journal of Zhongyuan Univ ersity of Technology, 20 (5): 56-59.
[5]Chang-liang Xia, Zhan-feng Song, 2007. Pitch Control of Variable S peed Constant Frequency Wind Turbines Based on Active-disturbance-rejection Co ntroller. Proceedings of the CSEE, p. 91-95.
[6]T. Senjyu, R. Sakamoto, N. Urasaki, et al, 2006. Output Power Leveli ng of Wind Farm Using Pitch Angle Control with Fuzzy Neural Network. Power Engin eering Society General Meeting, p.18-22.
[7]Kun Qian, Shou-sheng Xie, Mei-yan Gao, 2004. Applications of adapt ive control to variable speed wind turbine system. Control Engineering of China, 11(1): 76-78.
[8]Yao-jie Sun, Long-yun Kong, Wei-xiang Shi, 2004. Robust sliding m ode control of variable-speed wind power system. Power Electronics an d Motion Control Conference, 3: 1712-1716．
[9]Abdin Ezzeldin S, Xu Winlsonm, 2000. Control design and dynamic perf ormance analysis of a wind turbine induction generator unit. IEEE Tran s. on EC, 3(15): 91-96.
[10]Da-zhi Wang, Zhen-lei Wang, 2001. Identification and Control of I nduction Motor Using Artificial Neural Network. Proceedings of the Fifth Interna tional Conference on Electrical Machines and Systems. Shenyang, p.751-754.
[11]Chao Yang, Huai-chun Zhou, Xu-sheng Zhou, 2008. PID cascade contr ol system of power plant combustion with RBF network self-tuning. J. Huazhong Univ. of Sci. & Tech., 36(7): 57-59.
[full text view]
