Hybrid fuzzy control of wind turbine generator by pitch control using RNN
This article presents the design of a hybrid fuzzy sliding mode loss minimisation control for the speed of a permanent magnet synchronous generator (PMSG) and a high performance on line training recurrent neural network (RNN) for the turbine pitch angle control. The back propagation learning algorithm is used to regulate the RNN controller. The PMSG speed uses maximum power point tracking below the rated speed, which corresponds to low and high wind speeds, and the maximum energy can be captured from the wind. The sliding mode controller with an integral operation switching surface is designed, in which a fuzzy inference mechanism is utilised to estimate the upper bound of uncertainties.