Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks

Abstract

The energy losses and costs associated with faults in photovoltaic (PV) systems significantly limit the efficiency and reliability of solar power. Since existing methods for automatic fault diagnosis require expensive sensors, they are only cost-effective for large-scale systems. To address these drawbacks, we propose a fault diagnosis model based on graph neural networks (GNNs), which monitors a group of PV systems by comparing their current and voltage production over the last 24 h. This methodology allows for monitoring PV systems without sensors, as hourly measurements of the produced current and voltage are obtained via the PV systems' inverters. Comprehensive experiments are conducted by simulating 6 different PV systems in Colorado using 6 years of real weather measurements. Despite large variations in number of modules, module type, orientation, location, etc., the GNN can accurately detect and identify early occurrences of 6 common faults. Specifically, the GNN reaches 84.6% +/- 2.1% accuracy without weather data and 87.5% +/- 1.6% when satellite weather estimates are provided, significantly outperforming two state-of-the-art PV fault diagnosis models. Moreover, the results suggest that GNN can generalize to PV systems it was not trained on and retains high accuracy when multiple PV systems are simultaneously affected by faults.