Neuron-Adaptive PID Based Speed Control of SCSG Wind Turbine System

Abstract and Applied Analysis

Research Article

Zheng Zhou³  Lei Wang¹  Yongduan Song¹  Shan Zuo² 

[1] Institute of Intelligent System and Renewable Energy Technology, University of Electronic Science and Technology of China, Chengdu 611731, China, uestc.edu.cn;Intelligent Systems and New Energy Technology Research Institute, Chongqing University, Chongqing 400044, China, cqu.edu.cn;Institute of Intelligent System and Renewable Energy Technology, University of Electronic Science and Technology of China, Chengdu 611731, China, uestc.edu.cn;Web Science Center, University of Electronic Science and Technology of China, Chengdu 611731, China, uestc.edu.cn

Others  :  1319896
DOI  :  10.1155/2014/376259

 received in 2014-03-11, accepted in 2014-04-14,  发布年份 2014

【 摘 要 】

In searching for methods to increase the power capacity of wind power generation system, superconducting synchronous generator (SCSG) has appeared to be an attractive candidate to develop large-scale wind turbine due to its high energy density and unprecedented advantages in weight and size. In this paper, a high-temperature superconducting technology based large-scale wind turbine is considered and its physical structure and characteristics are analyzed. A simple yet effective single neuron-adaptive PID control scheme with Delta learning mechanism is proposed for the speed control of SCSG based wind power system, in which the RBF neural network (NN) is employed to estimate the uncertain but continuous functions. Compared with the conventional PID control method, the simulation results of the proposed approach show a better performance in tracking the wind speed and maintaining a stable tip-speed ratio, therefore, achieving the maximum wind energy utilization.

【 授权许可】

CC BY   
Copyright © 2014 Shan Zuo et al. 2014

【 图 表 】

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