A zero-sequence-enhanced 7-channel MTF-ResNet18-MSA framework for transmission line fault diagnosis
TL;DR
Results indicate that the proposed model provides an effective representation-learning approach for simulated transmission-line fault classification, and independent validation under a retraining protocol further supports the reproducibility of the framework on another simulated power-line fault dataset.
Abstract
Accurate transmission-line fault diagnosis is important for reliable power-system protection. Existing deep learning methods often rely on phase-domain voltages and currents, which may be insufficient to distinguish severe three-phase faults with and without a ground path. This paper proposes a domain-knowledge-enhanced 7-channel MTF-ResNet18-MSA framework for electrical fault diagnosis. The input consists of three-phase voltages, three-phase currents, and zero-sequence current. The zero-sequence current is introduced as an explicit grounding-related variable, while Markov Transition Field (MTF) encoding maps local voltage-current sample groups into two-dimensional transition maps. A ResNet18 backbone is adapted to 7-channel inputs, and a multi-head self-attention module is inserted after global average pooling to refine high-level feature dependencies. To avoid overlap-induced train-test leakage, the original sample records are split before local window generation. The proposed model achieves 100.00% accuracy under noiseless conditions and maintains 98.04% accuracy at 10 dB Gaussian noise. Compared with 1D-CNN, 1D-ResNet, 1D-Transformer, MTF-CNN, MTF-ResNet18, GAF-ResNet18-MSA, and STFT-ResNet18-MSA, it obtains the highest average accuracy over the tested noise levels. Channel ablation demonstrates that the 7-channel input is more effective than the 3-channel, 6-channel, and 8-channel configurations in this dataset. Sensitivity analysis shows stable performance under different MTF window lengths and bin numbers. Independent validation under a retraining protocol further supports the reproducibility of the framework on another simulated power-line fault dataset. The results indicate that the proposed model provides an effective representation-learning approach for simulated transmission-line fault classification. Further event-level validation, field-data testing, and lightweight deployment remain necessary.