Wireless Fault Detection in Solar Panels

Efficient monitoring of solar photovoltaic (PV) systems is essential to ensure sustained energy generation and long-term reliability. Conventional inverter-level monitoring methods are unable to detect and localize faults occurring at individual panel levels, resulting in unnoticed performance degradation. This paper presents the design and implementation of a distributed Internet of Things (IoT)-based monitoring architecture integrated with a Long Short-Term Memory (LSTM) neural network for intelligent panel-level fault detection. Voltage, current, and temperature parameters are continuously acquired using embedded sensors and transmitted wirelessly to a cloud-based database. The collected time-series data are processed using an LSTM model to identify abnormal operational patterns. Experimental evaluation under simulated fault conditions demonstrates effective detection performance with high accuracy and low latency. The proposed system provides a scalable, cost-effective, and practical approach for intelligent photovoltaic system monitoring.