AI-Based Fault Detection Systems for Solar Array Monitoring

Solar array monitoring systems currently process vast amounts of data—typically 144,000 measurements per day for a utility-scale 100MW installation—to detect performance anomalies and equipment failures. Traditional threshold-based monitoring struggles to distinguish between natural variations in solar output and actual faults, leading to both missed defects and false alarms that require costly manual investigation.

The fundamental challenge lies in developing fault detection algorithms that can maintain high accuracy across varying environmental conditions while processing streaming sensor data in real-time.

This page brings together solutions from recent research—including deep learning-based position monitoring, neural network weather analysis systems, and machine learning approaches for dynamic performance modeling. These and other approaches focus on practical implementation strategies that improve fault detection accuracy while reducing maintenance costs through automated diagnostics.