PHOTOVOLTAIC

Photovoltaic systems have drawn growing research interest in recent decades. PV generators show nonlinear current–voltage and power–voltage behavior, and their maximum power output changes with irradiance and temperature. Because PV arrays convert sunlight with relatively low efficiency, they require maximum power point tracking control to harvest as much energy as possible as light levels, shading, temperature, and module characteristics change. MPPT algorithms automatically adjust the power interface so the solar operating voltage stays near the maximum power point under varying atmospheric conditions. MPPT has become a key factor when evaluating PV system performance. This study reviews various MPPT techniques, summarizes background concepts, implementation topologies, grid interconnection issues, and solar microinverter requirements found in the literature, and offers comparative analysis with concise discussion. The review also covers MPPT advantages, disadvantages, and classification to serve as a reference for future research aimed at optimizing solar power generation. Conventional MPPT methods are simple to implement but suffer from oscillations around the maximum power point and slower tracking due to fixed perturbation steps. Intelligent methods perform better, producing smaller steady state oscillations and faster tracking compared with conventional approaches.

The efficiency and reliability of photovoltaic (PV) systems are largely determined by their ability to extract maximum power under varying environmental conditions. This project presents the implementation of an IoT-based investigative system for Maximum Power Point Tracking (MPPT) in photovoltaic arrays, focusing on the comparative performance of the MPPT and Pulse Width Modulation (PWM) charge controllers. The system integrates voltage and current sensors with an ESP32 microcontroller to measure and record PV parameters in real time. Through IoT connectivity, the collected data is transmitted to a cloud-based platform for remote monitoring, analysis, and visualization, enabling real-time tracking of PV performance. Experimental tests were conducted under different irradiance and temperature levels to evaluate the charging efficiency, dynamic response, and adaptability of both controllers. The MPPT controller dynamically adjusted the operating point of the PV module to maximize energy extraction, while the PWM controller maintained a simpler, fixed switching mechanism. Additionally, the system allowed for a detailed analysis of the relationship between light intensity, temperature, and PV output performance, with the readings interpreted from real-time graphical charts. These insights revealed how environmental variations affect energy generation and charge controller efficiency. This project develops a real-time, IoT-enabled system capable of monitoring and comparing the operational efficiency of MPPT and PWM charge controllers in photovoltaic applications. The results demonstrate that the MPPT controller achieves superior power utilization and battery charging efficiency compared to the PWM controller. Overall, the system provides a reliable, data-driven investigative platform for analyzing solar charge control strategies and
supports further optimization of PV energy systems through intelligent IoT integration.