Open AccessOptimal blade pitch control for enhancing the dynamic performance of wind power plants via metaheuristic optimisers
Author(s) -
Soued Salah,
Ebrahim Mohamed A.,
Ramadan Haitham S.,
Becherif Mohamed
Publication year - 2017
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 97
eISSN - 1751-8679
pISSN - 1751-8660
DOI - 10.1049/iet-epa.2017.0214
Subject(s) - control theory (sociology) , pid controller , settling time , wind power , turbine , overshoot (microwave communication) , wind speed , pitch control , gain scheduling , differential evolution , robustness (evolution) , computer science , engineering , step response , control engineering , control system , temperature control , algorithm , mechanical engineering , telecommunications , biochemistry , chemistry , physics , control (management) , electrical engineering , artificial intelligence , meteorology , gene
Wind energy (WE) as an environment‐friendly resource is green, clean, and innovative solution for the globe energy dilemma. For the blade pitch control (BPC) of the WE, the gain scheduling of the BPC becomes important to cope with the wind intermittency. The innovative idea of this study is to employ the novel advanced MOTs for enhancing the dynamic behaviour of the wind power plant. For this purpose, the genetic algorithm (GA), artificial bee colony (ABC) algorithm and grey Wolf optimiser (GWO) are used for the optimal tuning of the BPC system parameters. A comprehensive comparative study is presented to verify the effectiveness of the proposed MOTs over different conventional methods such as the conventional Zeigler–Nichols, and the simplex algorithm). This comparative analysis is based on the time response specifications such as maximum overshoot, settling time and steady‐state error. The proportional‐integral‐differential (PID) controller is applied to the multivariable BPC for a wind turbine generating system connected to a large power system. The simulation results show that the GWO‐PID is more effective than the ABC, GA and the conventional methods. Moreover,the GWO‐PID controller robustness is verified in the presence of system parameter variations, an abrupt change in the mechanical torque and the wind speed variation.