Real-time PV Fault Detection using Embedded Machine Learning

Real-time monitoring and control of individual solar panels in a photovoltaic (PV) array can improve efficiency and reduce the probability of hazardous situations. This paper proposes the design and implementation of a real-time Intelligent Monitoring and Control Device (IMCD) to measure PV parameters such as temperature, voltage, current, and irradiance from individual solar panels. The proposed IMCD uses an embedded machine-learning (ML) algorithm for detecting and classifying four key conditions of the solar panel: partial shading, soiling, extreme soiling, and the standard test condition (STC). IMCDs connect with solar panels and with a control center using an integrated data transceiver. The hardware consists of an Arduino UNO transmitter that is used for data collection, an Arduino Nano BLE 33 Sense receiver for PV measurement, and an integrated processor for real-time fault detection using embedded ML. The training of the embedded ML models and their real-world testing and validation are presented in this paper. Comparisons with cloud-based ML algorithms and the use of bagging ensemble techniques to increase fault detection accuracy are also discussed in this paper.