Robust Control and Kalman MPPT for Grid-Assimilated Wind Energy Conversion System

Abstract

A new robust control and Kalman maximum power point tracking (MPPT) is proposed for a grid-connected wind energy conversion system (WECS) to regulate the active and reactive power. During the operation, the wind speed and parameters of the WECS may vary; thus, the power control becomes a difficult task. The proposed Kalman-filter-based MPPT extracts the maximum available power effectively from the WECS during wind speed variations. The proposed robust control with a mixed $H_{2}/H_{\infty }$ optimal+state feedback recurrent neural network (MOSRNN) control scheme regulates the amount of power with an improved quality and stability even during the system uncertainties. The recurrent neural network controller is trained with an optimal variable step-size normalized least-mean-square algorithm. The main objectives of this work are as follows: developing the Kalman MPPT for a high-gain boost converter to extract maximum power from the WECS; designing the MOSRNN control scheme for an LCL -filtered voltage source inverter of the WECS; analyzing the outcomes of the WECS with the proposed Kalman MPPT and MOSRNN control scheme during the speed variation, load change, and parameter uncertainty; and testing the practical feasibility of the proposed distributed generation system in dSPACE environment.