Adaptive control of wind turbine generator system based on RBF-PID neural network

Wind turbine generator system (WTGS) is a complex multi-varies nonlinear system with the characteristics of time - varying, strong coupling and multi - interference. Wind speed is affected by many factors, the magnitude and direction are random. There are numerous of factors such as temperature, weather, environment and so on that affect wind turbine generator system, result in the WTGS cannot be guaranteed safety operation and constant output power. With the characteristic of strongly nonlinear, delay and uncertainty, the WTGS cannot be given an ideal control using traditional PID controller. Although the neural network control can solve the problems of nonlinear and uncertainty, it belongs to nonlinear approximation in essentially and cannot eliminate the error in steady state. In order to improve wind turbines behavior, an adaptive control method based on RBF-PID neural network is presented in this paper. This algorithm synthesizes the mechanical and electrical characteristics. System identification is part of the controller. At high wind speed, pitch angle is adjusted to keep rated output power using neural network adaptive controller. The RBFNN is used as the identifier of the WTGS. According to the identification information and the given learning speed, PID parameters are modified on line. The Matlab simulation results at random wind speeds show the controller can effectively improve the performance of variable pitch control.

[1]  Y. D. Song,et al.  Variable speed control of wind turbines using nonlinear and adaptive algorithms , 2000 .

[2]  H. Siguerdidjane,et al.  Nonlinear Control of Variable Speed Wind Turbines without wind speed measurement , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[3]  Feng Hai-ying.  Data-driven Predictive Control for Wind Turbine Pitch Angle , 2013 .

[4]  P. S. Leung,et al.  Predictive control of wind turbines by considering wind speed forecasting techniques , 2009, 2009 44th International Universities Power Engineering Conference (UPEC).

[5]  Wang Fan.  Adaptive Fuzzy Control Based on the Neural Network for the Wind Turbines Variable Pitch System , 2008 .

[6]  Ghanim Putrus,et al.  Optimal control of wind turbine using neural networks , 2010, 45th International Universities Power Engineering Conference UPEC2010.

[7]  Bin Song,et al.  Study on the wind energy resources assessment in wind power generation , 2011, 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC).

[8]  Liu Yibing.  Control strategy for variable speed fixed pitch wind turbines , 2004 .

[9]  Dong Hai-ying.  Research on wind speed prediction based on fuzzy neural network , 2012 .

[10]  Bhu Dev Sharma,et al.  Bounds on Distortion Due to Error for Rates Below and Above the Channel Capacity , 1974, Inf. Control..

[11]  Guohong Wu,et al.  A basic study on construction and control of offshore wind power generation system , 2009, 2009 Transmission & Distribution Conference & Exposition: Asia and Pacific.

[12]  A. Doroudi,et al.  A new method to mitigate voltage fluctuation of a fixed speed wind farm using DFIG wind turbine , 2012, 2012 Proceedings of 17th Conference on Electrical Power Distribution.

[13]  K. R. Kosoric,et al.  Wind changes influence on control of power systems with high percentage of wind power , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[14]  J. G. Slootweg,et al.  Aggregated modelling of wind parks in power system dynamics simulations , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[15]  Jin Lin,et al.  A review on analysis and control of small signal stability of power systems with large scale integration of wind power , 2010, 2010 International Conference on Power System Technology.

[16]  Janaka Ekanayake,et al.  Dynamic modeling of doubly fed induction generator wind turbines , 2003 .

[17]  Liu Yibing.  Adaptive Fuzzy Control for Large-scale Variable Speed Wind Turbines , 2004 .

[18]  Sun yaojie,et al.  Robust sliding mode control of variable-speed wind power system , 2004, The 4th International Power Electronics and Motion Control Conference, 2004. IPEMC 2004..

[19]  Y. D. Song,et al.  Control of wind turbines using nonlinear adaptive field excitation algorithms , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[20]  Wang Xudong.  THE THEORY OF RBF NEURAL NETWORK AND ITS APPLICATION IN CONTROL , 1997 .

[21]  Wang Hai-yun.  Application of Fuzzy Neural Network Control on the Variable Speed Pitch Regulated Generator , 2004 .

[22]  Wu Jie.  Feedback linearization control of constant output power for variable pitch wind turbine , 2012 .

[23]  Huang Ge.  Variable Pitch Control of Wind Power Generation Based on RBF Neural Network Tuning PID Control , 2009 .

[24]  O. Anaya-Lara,et al.  Control of DFIG-based wind generation for power network support , 2005, IEEE Transactions on Power Systems.

[25]  Rastko R. Selmic,et al.  Neural network control of a class of nonlinear systems with actuator saturation , 2004 .

[26]  Xiaoyan Ma,et al.  RBF neural network based self-tuning PID pitch control strategy for wind power generation system , 2010, 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering.