计算机工程与应用2011，Vol.47Issue(9)：228-232,5.DOI:10.3778/j.issn.1002-8331.2011.09.066
风电系统最大风能追踪的智能模型预测控制
Intelligent model predictive control of wind power system to trace maximal wind energy.
摘要
Abstract
Based on the principle of maximum wind energy capture,the maximum wind energy tracing below the rated wind speed can be realized by controlling the speed of Double-Fed Induction Generator(DFIG) to track the optimal speed. The variable speed constant frequency double-fed wind power system is taken as the research object and the control problem of maximum wind energy tracing below the rated wind speed is studied. Firstly, according to the characteristics of strong coupling,strong non-linearity and the complexity of mechanism model for DFIG,the intelligent predictive model is built by using Support Vector Machine(SVM).Secondly, the predictive output is revised by feedback correction and the closed control loop is structured.Finally,due to the advantages that Particle Swarm Optimization(PSO) algorithm has the fewer regulated parameters,the small evolution groups and the quick calculation speed,the optimal control sequences are obtained easily and the "bottleneck problem" in the rolling optimization calculation is solved better. The simulation results validate that the adopted predictive model has better anti-disturbance and generalization abilities,and the predictive control algorithm can realize the control objects.关键词
非线性智能模型预测控制/最大风能捕获/双馈感应发电机(DFIG)/转速控制/粒子群优化/支持向量机(SVM)Key words
non-linear intelligent model predictive control/ maximal wind energy capture/ Double-Fed Induction Generator (DFIG)/speed control/particle swarm optimization/Support Vector Machine(SVM)分类
信息技术与安全科学引用本文复制引用
刘吉宏,吕跃刚,郭鹏,徐大平..风电系统最大风能追踪的智能模型预测控制[J].计算机工程与应用,2011,47(9):228-232,5.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.50677021) （the National Natural Science Foundation of China under Grant No.50677021）
教育部重点项目基金(the Ministry of Education Key Project No.105049). （the Ministry of Education Key Project No.105049）
