Comparative Analysis of ANN and ANFIS-Based MPPT for Photovoltaic System

Abstract

Eco-friendly and sustainable energy sources, such as solar and wind power, have rapidly integrated into the grid in recent years. Solar power generation is influenced by factors such as radiation intensity and temperature. However, the intermittent nature of solar energy poses challenges for consistent power generation. Intelligent techniques are essential for maximizing power extraction. This study focuses on developing Artificial Intelligence-based Maximum Power Point Tracking (MPPT) methods in solar photovoltaic (PV) systems. Two AI techniques were implemented, and their performance was compared to achieve more efficient results. Real-time data from micro-nano grids in China were utilized to design a feed-forward neural network and an adaptive neural network-based MPPT system. These systems aim to optimize solar energy harvesting and enable integration into nano-grid systems, offering affordable and eco-friendly energy solutions for families in Thailand. The algorithms were developed using MATLAB/Simulink, and both techniques demonstrated excellent performance. Notably, the ANFIS-based MPPT system outperformed, achieving a tracking efficiency of 93% in PV systems.