An efficient wind speed sensor-less MPPT controller using adaptive neuro-fuzzy inference system

Abstract

An adaptive neuro-fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) algorithm has been proposed. The ANFIS based controller has the ability to track the maximum power point (MPP) and the corresponding rotor speed of the wind generator by estimating wind speed with very little error compared to the conventional ANN based MPPT techniques. The algorithm developed is based on two series ANFIS networks, one for wind speed estimation and the other to determine the maximum power point and the corresponding rotor speed. The method demonstrates remarkable performance in estimating wind speed and to predict MPP accurately without undesired oscillations around maximum power point. The algorithm does not require any mechanical sensor for wind speed measurement. Nonlinear time domain simulations have been carried out to validate the effectiveness of the proposed controllers under different operating conditions. Simulation results confirm the effectiveness of the proposed MPPT controller in tracking the maximum power point under rapidly changing wind conditions.