Neural network based wind speed sensorless MPPT controller for variable speed wind energy conversion systems

Abstract

A wind speed sensorless neural network (NN) based maximum power point tracking (MPPT) control algorithm for variable speed wind energy conversion system (WECS) is proposed. The proposed method is developed using Jordan type recurrent NN which is trained online using back-propagation. The algorithm, without requiring the knowledge of wind speed, air density or turbine parameters, generates at its output the optimum speed command for the speed control loop of the vector controlled machine side converter control system using only the instantaneous power as its input. The output of the NN is fed into a state input after a unit step delay completing the Jordan type recurrent neural network. The proposed concept is analyzed in a grid connected direct drive variable speed permanent magnet synchronous generator (PMSG) WECS with a back-to-back frequency converter. Vector control of the grid side converter is realized in the grid voltage vector reference frame. Simulation is carried out in order to verify the performance of the proposed controller.