GCN-Based Model for Fault Classification in Distribution Networks under Harmonic Distortion

Abstract

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.