Design and Implementation of a Performance Monitoring System for Off-Grid Solar Photovoltaic Systems

Abstract

Solar photovoltaic (PV) systems have gained significant attention as a sustainable and renewable energy solution in various settings. To ensure the optimal performance and efficient utilization of such systems, it is crucial to monitor their performance continuously. Solar PV system performance can significantly degrade due to high temperatures. Traditional monitoring systems often lack the capability to analyze this specific impact and often use Arduino platforms to build projects as a lower cost option and fail to provide comprehensive insights into the performance of PV systems. This gap in monitoring infrastructure impedes the ability to identify performance bottlenecks, optimize energy generation efficiency, and ensure the long-term reliability of PV installations. This research proposal aims to design and implement a laboratory prototype of a performance monitoring system for a solar PV system using an Internet of Things (IoT)-based monitoring system with an ESP32 microcontroller platform. The proposed system collects real-time data on voltage, current, and temperature. This data is then analyzed to assess the impact of high temperatures on system performance. The system was implemented and evaluated on a small-scale solar energy system in Baghdad during the summer. The test results showed that the proposed system is able to collect the monitored data accurately and transmit it to the cloud server in real-time. In addition, this study identified a negative correlation between temperature and the power output of the solar PV system. Performance exhibited a statistically significant decrease during periods of high temperature. This finding highlights the system's capability to quantify performance degradation as a function of temperature. An ESP32-based monitoring system offers a cost-effective and accurate approach, while the analysis of temperature-related performance degradation facilitates informed decision-making for performance optimization and maximized energy generation.