I. A. Edeoghon

The integration of renewable energy systems, particularly photovoltaic (PV) technology, has become increasingly essential in addressing global energy demands and environmental concerns. However, the nonlinear and time-varying characteristics of solar irradiation and temperature significantly affect the efficiency of PV systems. Maximum Power Point Tracking (MPPT) techniques are employed to ensure that PV modules operate at their optimal power point under varying conditions. Traditional MPPT methods such as Perturb and Observe (P&O) and Incremental Conductance (IncCond) offer simplicity but often suffer from oscillations, slow convergence, and reduced performance under dynamic conditions.
This research investigates the application of reinforcement learning (RL) algorithms for MPPT control in PV systems, aiming to enhance tracking speed, stability, and adaptability. By formulating the MPPT problem as a sequential decision-making process, RL agents learn optimal control policies through continuous interaction with the PV environment without requiring explicit system modeling.
Various RL approaches—such as Q-learning, Deep Q-Networks (DQN), and Policy Gradient methods—are analyzed and compared with conventional techniques through simulation studies. The results demonstrate that RL-based MPPT controllers can effectively handle rapidly changing environmental conditions, minimize steady-state oscillations, and achieve superior energy harvesting efficiency. This study highlights the potential of reinforcement learning as a robust and intelligent solution for real-time PV power optimization

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.

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.