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

Jonas Van Gompel, Domenico Spina and Chris Develder

Energy, 2023, vol. 266, issue C

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 24h. 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.

Keywords: Photovoltaics; Predictive maintenance; Fault detection; Graph neural network; Time series classification (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:266:y:2023:i:c:s0360544222033308

DOI: 10.1016/j.energy.2022.126444

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