Open AccessProportional–integral–derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm
Author(s) -
Civelek Zafer,
Çam Ertuğrul,
Lüy Murat,
Mamur Hayati
Publication year - 2016
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2016.0029
Subject(s) - crossover , pid controller , control theory (sociology) , genetic algorithm , matlab , blade (archaeology) , blade pitch , controller (irrigation) , wind speed , wind power , computer science , algorithm , engineering , mathematics , control engineering , mathematical optimization , turbine , control (management) , structural engineering , artificial intelligence , mechanical engineering , agronomy , electrical engineering , biology , temperature control , physics , meteorology , operating system
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.