Developing an Intelligent System for Maximizing Photovoltaic Array Output Power Based on Varying Environmental Conditions

Abstract

Photovoltaic power generation is highly sensitive to environmental factors such as sunlight intensity and temperature, directly impacting output power. Maximum Power Point Tracking (MPPT) algorithms are employed to continuously adjust the operating conditions of the panel, such as the voltage or current, to optimize power with changing environmental factors. While traditional MPPT algorithms like perturb and observe (P&O) are computationally efficient, they struggle with oscillations and inefficiencies. More advanced MPPT algorithms can be more efficient but have increased computational complexity. This project developed a hybrid MPPT system that combines multiple algorithms and environmental sensors to dynamically select the most effective algorithm in real-time