ADAPTIVE OPTIMIZATION APPROACH FOR MPPT IN SOLAR-PV SYSTEM

Abstract

This research details a completed study utilizing sophisticated Maximum Power Point Tracking (MPPT) methods to optimize the efficiency of photovoltaic (PV) systems. The primary focus of the project was the implementation and comparative analysis of a progressive Quantum-Inspired Evolutionary Algorithm (QIEA) alongside the standard Perturb and Observe (P&O) methodology. Although P&O is frequently adopted for its simplicity, it inherently suffers from steady-state fluctuations, sluggish dynamic reactions, and compromised precision during rapid climatic variations. Such limitations prompted the evaluation of QIEA, a method that merges evolutionary optimization logic with the probabilistic superposition of quantum mechanics to isolate the maximum power point with greater speed, stability, and exactness. Both MPPT controllers were completely modelled and tested via MATLAB/Simulink. Extensive simulation benchmarks demonstrate that QIEA yields superior tracking execution, accelerated convergence speeds, and drastically reduced steady-state volatility compared to the industry-standard P&O algorithm. Furthermore, analysis under stepped irradiance changes highlights QIEA's robustness in capturing and maintaining the Global Maximum Power Point (GMPP) under restrictive conditions where traditional deterministic approaches fail. Following this comparative phase, the optimized PV array was successfully synchronized with a microgrid network, yielding highly stable integration and operational results. Ultimately, this work outlines the theoretical foundations, mathematical modeling, algorithmic structuring, and empirical findings that validate QIEA as an exceptionally capable framework for modern PV inverters and microgrid synchronization.