Extended Kalman filter used to estimate speed rotation for sensorless MPPT of wind conversion chain based on a PMSG

This paper presents an innovative method of determining the value of the optimum wind power employed to design a sensorless Maximum Power Point Tracking (MPPT) algorithm of a wind conversion chain with a Permanent Magnet Synchronous Generator (PMSG) using two estimators: the Extended Kalman Filter (EKF) to estimate rotation speed and the extremum seeking to estimate the coefficient including turbine parameters. The model of Wind Energy Conversion System (WECS) consists of a wind turbine, two-mass drive train, PMSG, and power converter supplying a DC load. The proposed method based on sensorless wind speed, air density and turbine parameters, generates the outputs to be used in the Field Oriented Control (FOC) requiring implementation of an active rectifier and also in the MPPT block. At first, we show the advantage of using a FOC of the PMSG. Then we describe the EKF and the extremum seeking method. Simulations on Matlab-Simulink can be found at the end of the paper, confirming the performance of the proposed approach.