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[2602.12289] String-Level Ground Fault Localization for TN-Earthed Three-Phase Photovoltaic Systems
Summary
This article presents a novel edge-AI approach for localizing ground faults in TN-earthed three-phase photovoltaic systems, enhancing efficiency and accuracy in fault detection.
Why It Matters
Ground faults in photovoltaic systems can lead to significant damage and safety risks. This research addresses the inefficiencies of manual fault detection by proposing an AI-driven solution that improves localization accuracy, potentially transforming maintenance practices in the renewable energy sector.
Key Takeaways
- Ground faults pose risks to three-phase TN-earthed PV systems.
- The proposed edge-AI model achieves over 93% localization accuracy.
- Simulation-based analysis aids in understanding fault characteristics.
- The lightweight model is suitable for deployment on resource-constrained inverters.
- This approach could significantly reduce maintenance time and costs.
Electrical Engineering and Systems Science > Systems and Control arXiv:2602.12289 (eess) [Submitted on 28 Jan 2026] Title:String-Level Ground Fault Localization for TN-Earthed Three-Phase Photovoltaic Systems Authors:Yuanliang Li, Xun Gong, Reza Iravani, Bo Cao, Heng Liu, Ziming Chen View a PDF of the paper titled String-Level Ground Fault Localization for TN-Earthed Three-Phase Photovoltaic Systems, by Yuanliang Li and 5 other authors View PDF HTML (experimental) Abstract:The DC-side ground fault (GF) poses significant risks to three-phase TN-earthed photovoltaic (PV) systems, as the resulting high fault current can directly damage both PV inverters and PV modules. Once a fault occurs, locating the faulty string through manual string-by-string inspection is highly time-consuming and inefficient. This work presents a comprehensive analysis of GF characteristics through fault-current analysis and a simulation-based case study covering multiple fault locations. Building on these insights, we propose an edge-AI-based GF localization approach tailored for three-phase TN-earthed PV systems. A PLECS-based simulation model that incorporates PV hysteresis effects is developed to generate diverse GF scenarios, from which correlation-based features are extracted throughout the inverter's four-stage shutdown sequence. Using the simulated dataset, a lightweight Variational Information Bottleneck (VIB)-based localization model is designed and trained, achieving over 93% localization ac...