Proportional-integral-derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm

作     者：Civelek, Zafer Cam, Ertugrul Luy, Murat Mamur, Hayati 

作者机构：Cankiri Karatekin Univ Tech Business Coll Cankiri Turkey Kirikkale Univ Fac Engn Kirikkale Turkey Cankiri Karatekin Univ Fac Engn Cankiri Turkey 

出 版 物：《IET RENEWABLE POWER GENERATION》 (IET. Renew. Power Gener.)

年 卷 期：2016年第10卷第8期

页      面：1220-1228页

核心收录：

学科分类：0820[工学-石油与天然气工程] 0808[工学-电气工程] 08[工学] 

基　　金：Scientific and Technological Research Council of TURKEY (TUBITAK) [114E419] 

主　　题：three-term control genetic algorithms wind turbines power generation control proportional-integral-derivative parameter optimisation blade pitch controller wind turbines intelligent genetic algorithm PID controllers hot systems community-based optimisation methods PID parameter setting optimisation IGA controllers WT mutation rate MATLAB Simulink software 

摘      要：Output powers of wind turbines (WTs) with variable blade pitch over nominal wind speeds are controlled by means of blade pitch adjustment. While tuning the blade pitch, conventional proportional-integral-derivative (PID) controllers and some intelligent genetic algorithms (IGAs) are widely used in hot systems. Since IGAs are community-based optimisation methods, they have an ability to look for multi-point solutions. However, the PID parameter setting optimisation of the IGA controllers is important and quite difficult a step in WTs. To solve this problem, while the optimisation is carried out by regulating mutation rates in some IGA controllers, the optimisation is conducted by altering crossover point numbers in others. In this study, a new IGA algorithm approach has been suggested for the PID parameter setting optimisation of the blade pitch controller. The algorithm rearranging both the mutation rate and the crossover point number together according to the algorithm progress has been firstly used. The new IGA approach has also been tested and validated by using MATLAB/Simulink software. Then, its superiority has been proved by comparing the other genetic algorithm (GAs). Consequently, the new IGA approach has more successfully adjusted the blade pitch of a WT running at higher wind speeds than other GA methods.