The Design and Evaluation of Performance of ANN MPPT-Based Hybrid Solar and Wind Power System

Abstract

This study proposes an integrated system that combines a diesel engine, a wind system, and a solar system to form a comprehensive hybrid system. To ensure prompt and reliable responses, an intelligent controller is developed. The solar system is characterized using a radial-based function network, while wind energy systems employ the Elman neural network. The integration of a neural fuzzy controller enhances Maximum Power Point Tracking (MPPT) performance. Comparative analysis reveals that the unit cost values for the PV-wind hybrid system are lower than those for standalone solar and wind systems, ensuring cost-effective maintenance. Moreover, the levelized energy cost for PV-wind hybrid systems, meeting all load demands, is consistently lower than that for individual solar PV or wind systems. The study leverages MATLAB and Simulink for comprehensive modeling and simulation. The amalgamation of these technologies presents a promising avenue for achieving sustainable and economical energy solutions.