MPPT Control for Standalone Wind Energy Conversion System: Integrating Hybrid P&O and ANN with Voltage-Lift Boost Converter

This study proposes an approach to enhance the performance of wind energy conversion systems (WECS) through the integration of a hybrid Perturb and Observe (P&O) and Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) control technique. The application of this technique is explored in the context of WECS utilizing voltage lift-based DC/DC converters. The utilization of an artificial neural network control method aims to optimize power extraction from varying wind velocities effectively. The system incorporates a voltage lift-boost converter to achieve stable voltage and a higher power rating. Additionally, a standard boost converter is employed for comparative analysis, serving as a benchmark to validate the proposed converter's performance. The study systematically assesses and compares the output power, convergence time, and stability of three configurations: the traditional P&O technique, the hybrid MPPT method with a conventional boost converter, and the hybrid MPPT method with a voltage lift-boost converter. The study conducts a thorough analysis of the MPPT technique and DC/DC converter performance using MATLAB/Simulink.