An efficient wind speed sensor-less MPPT controller using artificial neural network

Abstract

An artificial neural network (ANN) based maximum power point tracking (MPPT) algorithm has been developed. The proposed ANN based controller has the ability to estimate wind speed by tracking the maximum power point (MPP) and the optimal rotor speed with very low error compared to the conventional MPPT methods. The algorithm is based on two series neural networks, one for wind speed estimation and the other for tracking maximum power point. The method demonstrates remarkable performance in estimating wind speed under rapidly changing wind conditions. It can also predict MPP accurately avoiding 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 in terms of wind speed estimation and MPPT under different operating conditions. Simulation results confirm the effectiveness of the MPPT controller in tracking the maximum power point under rapidly changing wind conditions.