Estimator based neuro-fuzzy control for maximum power extraction from wind electrical power generation system

The successfulness of wind energy is, its cost competitiveness, environmental clean, safeness and most importantly, it is a renewable energy. The Non linear control algorithms are used to maximize the system performance and optimize the control of wind turbine speed. This paper proposes wind speed estimation based neuro-fuzzy control to extract maximum power from the wind electrical power generating system. A fully-controlled wind turbine which consists of induction generator and back-to-back converter is under estimate. The induction generator is operated in the vector control mode, where the speed of the doubly fed induction generator (DFIG) is controlled with respect to the variation of the wind speed in order to produce the huge output power. The neuro fuzzy logic controller is efficient to track the maximum power point, especially in case of frequently changing wind conditions. The simulated system with the neuro-fuzzy control unit of wind turbine keeps the system stability and conforms to the active power to protect the DFIG over speeding and keeps the output power to the maximum power point.