提出一种基于边缘AI的光伏系统接地故障定位方法，提升故障排查效率。
String-Level Ground Fault Localization for TN-Earthed Three-Phase Photovoltaic Systems
- 通过仿真生成多种故障场景，提取逆变器四阶段关机过程中的相关特征。
- 采用轻量级VIB模型，在典型采样率下实现超93%故障定位准确率。
- 适合资源受限的光伏逆变器部署，尤其适用于三相TN接地系统。
直流侧接地故障对三相TN接地光伏系统构成严重威胁，故障电流可能直接损坏逆变器与光伏组件。故障发生后，人工逐串排查耗时且低效。本文通过故障电流分析与多故障位置的仿真案例研究，全面分析了接地故障特性。在此基础上，提出一种面向三相TN接地光伏系统的边缘AI故障定位方法。构建了包含光伏滞环效应的PLECS仿真模型，生成多样化的接地故障场景，并在逆变器四阶段关机过程中提取相关特征。利用仿真数据训练轻量级变分信息瓶颈（VIB）定位模型，在典型采样率下实现超过93%的定位准确率，计算成本低，展现出在资源受限的光伏逆变器上部署的强潜力。
原文摘要 · Abstract (English)
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.
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