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String-Level Ground Fault Localization for TN-Earthed Three-Phase Photovoltaic Systems

Yuanliang Li, Xun Gong, Reza Iravani, Bo Cao, Heng Liu, Ziming Chen

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 accuracy at typical sampling rates with low computational cost, demonstrating strong potential for deployment on resource-constrained PV inverters.

String-Level Ground Fault Localization for TN-Earthed Three-Phase Photovoltaic Systems

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 accuracy at typical sampling rates with low computational cost, demonstrating strong potential for deployment on resource-constrained PV inverters.
Paper Structure (20 sections, 8 equations, 13 figures, 5 tables)

This paper contains 20 sections, 8 equations, 13 figures, 5 tables.

Table of Contents

  1. Introduction
  2. System Model and Fault-Current Analysis
  3. Fault-Current Analysis
  4. Lower-Leg Phase-Fault-Current
  5. Upper-Leg Phase-Fault-Current
  6. Zero Phase-Fault-Current
  7. Dynamic PV Model
  8. Four-Stage Shutdown Procedure
  9. Case Study
  10. At Negative Terminal $(N_{\rm sc}=0)$
  11. Within String and Close to Negative Terminal $(N_{\rm sc}=1, 6)$
  12. In the Middle $(N_{\rm sc}=9)$
  13. Within String and Close to Positive Terminal $(N_{\rm sc}=12)$
  14. At Positive Terminal $(N_{\rm sc}=18)$
  15. Ground Fault Localization Model
  16. ...and 5 more sections

Figures (13)

  • Figure 1: Ground faults in three-phase PV systems under three types of earthing arrangements, i.e., TN, TT, and IT.
  • Figure 2: Diagram of the 3$\phi$-TN multi-MPPT PV inverter under GFs.
  • Figure 3: Dynamic PV cell model (single diode model with diffusion capacitor).
  • Figure 4: PV simulation model implemented by PLECS.
  • Figure 5: Dynamic characteristics of PV modules.
  • ...and 8 more figures