Title :
Intelligent inverse control to maximum power point tracking control strategy of wind energy conversion system
Author :
Li, Tai ; Ji, Z.C.
Author_Institution :
Inst. of Electr. Autom., Jiangnan Univ., Wuxi, China
Abstract :
When the wind speed is below the rated value, the RBF neural network inverse controller is designed to achieve maximum power point tracking (MPPT) for wind energy conversion system, the simulation model is built based on the Matlab / Simulink. The results show that power coefficient and tip speed ratio has high accuracy of tracking the optimal power value, what´s more, the neural network inverse control method is compared with conventional pi control, and the wind energy conversion system has better dynamic with the ann´s controller than that with pi´s.
Keywords :
control system synthesis; direct energy conversion; maximum power point trackers; neurocontrollers; radial basis function networks; wind power plants; Matlab; RBF neural network inverse controller design; Simulink; intelligent inverse control; maximum power point tracking control strategy; power coefficient; tip speed ratio; wind energy conversion; Artificial neural networks; Generators; Mathematical model; Wind energy; Wind power generation; Wind speed; Wind turbines; Maximum Power Point Tracking; Neural Network Inverse Control; Wind Energy Conversion System;
Conference_Titel :
Control and Decision Conference (CCDC), 2011 Chinese
Conference_Location :
Mianyang
Print_ISBN :
978-1-4244-8737-0
DOI :
10.1109/CCDC.2011.5968324