The blade pitch angle (BPA) in wind turbine (WT) is controlled to maximize output power generation above the rated wind speed (WS). In this paper, four types of controllers are suggested and compared for BPA controller in WT: PID controller (PIDC), type-1 fuzzy logic controller (T1-FLC), type-2 fuzzy logic controller (T2-FLC), and hybrid fuzzy-PID controller (FPIDC). The Mamdani and Sugeno fuzzy inference systems (FIS) have been compared to find the best inference system used in FLC. Genetic algorithm (GA) and Particle swarm optimization algorithm (PSO) are used to find the optimal tuning of the PID parameter. The results of500-kw horizontal-axis wind turbine show that PIDC based on PSO can reduced 2.81% in summation error of power signal (EPS) than GA. T1-FLC based on Sugeno FIS performs 23.98% lower summation of EPS than T1-FLC based on Mamdani FIS and 27.63% lower than optimal PIDC when using PSO. Sugeno FIS in T2-FLC provides 21.81% lower summation of EPS than Mamdani FIS in T2-FLC and lower summation of EPS than T1-FLC based on Sugeno FIS. On the other hand, the hybrid type-2 fuzzy-PID controller (T2-FPIDC) based on PSO with the Sugeno FIS provides 21.93% lower summation of EPS than the Sugeno FIS in hybrid T2-FPIDC based on GA as well as 90% lower summation of EPS thanSugenoT2-FLC. Finally, the proposed optimal hybrid T2-FPIDC based on PSO and Sugeno FIS provides the best results in terms of consistent output power at fluctuating wind speeds and lowest in summation of EPS.
