Open Access

ARTICLE

Single-Phase Grounding Fault Identification in Distribution Networks with Distributed Generation Considering Class Imbalance across Different Network Topologies

Lei Han^1,*, Wanyu Ye¹, Chunfang Liu², Shihua Huang¹, Chun Chen³, Luxin Zhan³, Siyuan Liang³

1 Guangdong Power Grid Co., Ltd. Qingyuan Power Supply Bureau, Qingyuan, 511500, China
2 Foshan Electric Power Design Institute Co., Ltd., Foshan, 528200, China
3 National Key Laboratory of Power Grid Disaster Prevention and Mitigation, Changsha University of Science and Technology, Changsha, 410114, China

* Corresponding Author: Lei Han. Email:

Energy Engineering 2025, 122(12), 4947-4969. https://doi.org/10.32604/ee.2025.069040

Received 12 June 2025; Accepted 24 July 2025; Issue published 27 November 2025

Abstract

In contemporary medium-voltage distribution networks heavily penetrated by distributed energy resources (DERs), the harmonic components injected by power-electronic interfacing converters, together with the inherently intermittent output of renewable generation, distort the zero-sequence current and continuously reshape its frequency spectrum. As a result, single-line-to-ground (SLG) faults exhibit a pronounced, strongly non-stationary behaviour that varies with operating point, load mix and DER dispatch. Under such circumstances the performance of traditional rule-based algorithms—or methods that rely solely on steady-state frequency-domain indicators—degrades sharply, and they no longer satisfy the accuracy and universality required by practical protection systems. To overcome these shortcomings, the present study develops an SLG-fault identification scheme that transforms the zero-sequence current waveform into two-dimensional image representations and processes them with a convolutional neural network (CNN). First, the causes of sample-distribution imbalance are analysed in detail by considering different neutral-grounding configurations, fault-inception mechanisms and the statistical probability of fault occurrence on each phase. Building on these insights, a discriminator network incorporating a Convolutional Block Attention Module (CBAM) is designed to autonomously extract multi-layer spatial-spectral features, while Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to visualise the contribution of every salient image region, thereby enhancing interpretability. A comprehensive simulation platform is subsequently established for a DER-rich distribution system encompassing several representative topologies, feeder lengths and DER penetration levels. Large numbers of realistic SLG-fault scenarios are generated—including noise and measurement uncertainty—and are used to train, validate and test the proposed model. Extensive simulation campaigns, corroborated by field measurements from an actual utility network, demonstrate that the proposed approach attains an SLG-fault identification accuracy approaching 100 percent and maintains robust performance under severe noise conditions, confirming its suitability for real-world engineering applications.

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Keywords

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