Early Detection of Solar Panel Faults Using Embedded Machine Learning

Abstract

The global shift to renewable energy depends critically on the dependability of photovoltaic (PV) systems. Although current methods for detecting PV faults, such as thermal imaging, drone inspections, or artificial neural networks, have proven to be highly accurate, they frequently depend on expensive equipment, intricate setups, or cloud-based computing that impede real-time and embedded deployment. By presenting a unique Embedded Machine Learning (EML) framework for proactive, low-power, on-site PV failure detection, this study gets beyond these restrictions. Our Support Vector Machine (SVM) classifier, in contrast to ANN-based models, is designed for lightweight deployment on an ESP32 microcontroller, guaranteeing reliable performance with low computational overhead. When combined with high-precision sensors, data preprocessing, and IoT interaction with Adafruit IO and MQTT, the system attains a 96.5% classification accuracy while using just 250 mw significantly reducing the energy footprint compared to existing solutions. This work demonstrated the possibility of deploying scalable, autonomous, and energy-efficient diagnostic systems in resource-constrained environments with real-time monitoring, which had not been taken into consideration in prior papers in the literature, in addition to early identification of common faults like shading, short circuits, open circuits, and hot spots. This work makes an exceptional contribution to solar energy predictive maintenance by bridging the gap between cutting-edge AI methods and real-world field application, opening the door for more intelligent and greener PV installations.