Comparison of Advanced MPPT Techniques & Introduction of Incremental Conductance MPPT Controller Based on Adaptive Neuro Fuzzy Inference Systems (ANFIS) for PV system

Abstract

Efficient usage of renewable energy systems like photovoltaic (PV) and wind energy systems greatly benefit from Maximum Power Point Tracking (MPPT) techniques. The primary objectives of these advanced MPPT algorithms are addressing slow response times, inefficiency in partial shading, and other issues such as undulated behavior around the Maximum Power Point (MPP). This paper provides a comparative analysis of various advanced MPPT techniques, namely P&O with adaptive step size, fuzzy logic based Incremental Conductance (INC), Artificial Neural Networks (AAN), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Sliding Mode Control (SMC) techniques. Key metrics considered for evaluating the techniques included tracking speed, overall efficiency, the complexity of the algorithms, and performance under varying conditions. The study showed that higher utilization of fuzzy logic in conjunction with hybrid integrated intelligent control mechanisms was more effective. The proposed method will demonstrate that the strategy performs noticeably better than conventional techniques in terms of responsiveness, stability, and efficiency.