TRACK

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