基于图卷积网络的谐波畸变下配电网故障分类模型
GCN-Based Model for Fault Classification in Distribution Networks under Harmonic Distortion
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中文总结 AI 辅助
提出拓扑感知图卷积网络模型，利用故障前后电压电流相量及谐波分量映射至IEEE 34节点拓扑，在多种光伏渗透率和谐波畸变下实现配电网故障分类，F1分数达0.98，优于CNN、LSTM和MLP。
中文摘要 AI 辅助
分布式能源的日益普及给配电网中的精确故障检测与分类带来了新的挑战。本文提出了一种基于拓扑感知图卷积网络的模型，用于在逆变器引起的谐波畸变条件下进行故障分类。该模型利用故障前和故障后的电压和电流相量（包括谐波分量），并将其映射到IEEE 34节点系统拓扑上。在OpenDSS中生成的仿真数据涵盖了不同水平的太阳能光伏渗透率（30%、50%、70%）和电流总谐波畸变率（0%、1%、3%、5%），涉及多种故障类型：单相接地、线间、双相接地和三相接地故障。采用差分进化算法对网络的超参数进行优化，最终得到一个三层模型，其F1分数达到0.98。跨场景的压力测试证实了该模型具有较高的鲁棒性和极小的类别混淆。结果表明，基于图卷积网络的模型能够在不同的光伏渗透水平和谐波畸变条件下有效分类配电网故障。与卷积神经网络、长短期记忆网络和多层感知机架构的比较分析表明，所提出的模型在分类性能上更为优越，尤其是在70%光伏渗透率和5%谐波畸变的最严苛条件下。
英文摘要
The increasing integration of distributed energy resources introduces new challenges for accurate fault detection and classification in distribution networks. This paper presents a topology-aware graph convolutional network-based model for fault classification under inverter-induced harmonic distortion. The model uses pre-fault and post-fault voltage and current phasors, including harmonic components, mapped to the IEEE 34-bus system topology. The simulation data generated in OpenDSS encompass varying levels of penetration of solar photovoltaics (30%, 50%, 70%) and current total harmonic distortion (0%, 1%, 3%, 5%) across multiple fault types: single line-to-ground, line-to-line, double line-to-ground, and three-phase-to-ground. A differential evolution algorithm is employed to optimize the hyperparameters of the network, resulting in a three-layer model that achieves an F1-score of 0.98. Stress testing across scenarios confirms high robustness and minimal class confusion. The results demonstrate that a graph convolutional network-based model can effectively classify distribution faults under varying photovoltaic penetration levels and harmonically distorted conditions. Comparative analyses with convolutional neural network, long short-term memory, and multi-layer perceptron architectures demonstrate that the proposed model delivers superior classification performance, particularly under the most stressed condition with 70% photovoltaic penetration and 5% harmonic distortion.
发表机构
- University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。
