Ann-Based Maximum Power Point Tracking of a Variable-Speed Wind Energy Conversion System using Sepic Converter

Abstract

The concerns for environment due to the ever-increasing use of fossil fuels and rapid depletion of this resources have led to the development of renewable energy sources such as wind energy. However, wind energy conversion systems face some challenges which make it less efficient compared to non-renewable energies. Some of these problems include efficiency trade-off and divergence from peak power under rapid wind speed variation. Many researchers have sought to mitigate these problems in order to improve the efficiency and maintain the output power even under such conditions. Hence the idea of Maximum Power Point Tracking (MPPT) emerged, yet most of these researches are based on conventional MPPT methods which have many drawbacks like power oscillations. This paper proposes an Artificial Neural Network (ANN) based Maximum Power Point Tracking (MPPT) control strategy for Wind Energy Conversion System (WECS) implemented with a DC/DC converter. The proposed topology utilizes a Nonlinear Autoregressive with External (Exogenous) Input (NARX) based neural network control strategy to extract the maximum available power from different wind velocities. To validate the performance of this method, the results were compared with Perturb and Observe (P&O) method. In other to stabilize the output, the system is implemented with Single Ended Primary Inductance Converter (SEPIC). The simulation of the MPPT technique along with a DC/DC converter is demonstrated using MATLAB/Simulink.