IoT-Based Internal Fault Detection and Classification in a Solar Photovoltaic System

This research work introduces a hardware-based implementation of "IoT-Based Internal Fault Detection and Classification in a Solar Photovoltaic System," offering an innovative solution for the efficient management of solar PV systems by integrating Internet of Things (IoT) technology. The primary objective is to ensure sustained optimal performance and longevity of PV systems by promptly detecting and categorizing internal faults. The system employs a network of strategically positioned sensors that continuously monitor key parameters such as voltage, current, temperature, and sunlight levels in real-time. These sensors collect data transmitted wirelessly to a central unit using IoT communication protocols. What sets this system apart is its capacity to identify faults and deviations without relying on machine learning, instead categorizing them into predefined fault types like panel degradation and connection issues. Upon detection and classification, the system generates timely alerts, enabling swift intervention and minimizing downtime. Additionally, it provides remote monitoring capabilities through an intuitive user interface accessible via smartphones or computers. This comprehensive IoT-based approach enhances the reliability and lifespan of solar PV systems, promoting their wider adoption as sustainable energy sources.