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This paper presents a novel pitch angle controller for wind turbines based on a gain-scheduled reinforcement learning recurrent ANFIS type 2 (GS-RL-RANFIST2) approach, which utilizes passive reinforcement learning and particle swarm optimization. The proposed controller aims to maintain rotor speed and smooth output power, demonstrating improved energy capture and response compared to traditional gain corrected proportional integral (GC-PI) controllers. Evaluations on a 600 kW wind turbine indicate that the

Author

Mariam Al charkawi

Language

EN