Author

Zhengyang Li

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Open access Aug 2026

Fault Line Selection Strategy for Distribution Networks Based on Dynamic and Accurate Measurement of Zero-Sequence Current—A Data–Model Hybrid-Driven Method

The measurement accuracy issue of zero-sequence current transformers (CTs) has long been a critical factor restricting the accuracy of fault line selection in distribution networks. Although existing research methods are relatively mature in theory, their on-site application is limited by the measurement precision of zero-sequence CTs. To address this problem, this paper proposes a fault line selection strategy for distribution networks based on dynamic and accurate measurement of zero-sequence current. Firstly, from the data perspective, this paper analyzes the fault characteristics of various electrical quantities in different operation stages of distribution networks. Combined with system characteristics, an accurate measurement method for zero-sequence current amplitude is subsequently put forward. Afterwards, a distribution network fault line selection algorithm optimized by an attention mechanism-based multi-scale convolutional neural network is constructed. Finally, verification results based on the IEEE standard test system demonstrate that the proposed method enhances the capabilities of feature extraction and side information aggregation, realizes efficient and accurate localization of faulty lines, and exhibits strong robustness under noisy conditions.

Ruihao Zhou, Penghui Liu, Wenxiang Li et al. · Processes · 0 citations

Open access Aug 2026

Complex Fault Diagnosis Strategy for Distribution Networks Based on Dynamic Variation Characteristics of Zero-Sequence Current

Large-scale integration of high-proportion new energy sources and continuous expansion of network scale complicate the transient characteristics of distribution networks. Conventional fault diagnosis methods suffer from insufficient feature extraction and weak capture of topological correlation, which degrade diagnosis accuracy. To tackle this issue, this paper proposes a complex fault diagnosis strategy for distribution networks based on analysis of the dynamic variation law of zero-sequence current. First, multivariate variational mode decomposition (MVMD) is adopted to process zero-sequence current signals, which effectively fuses multi-dimensional zero-sequence current data and fully excavates fault features. Moreover, the zebra optimization algorithm is utilized to optimize the parameters of MVMD for further improving feature extraction performance. Subsequently, a graph convolutional neural network is employed to extract temporal features from the processed waveforms, enhancing the model’s recognition capability under high-resistance faults and typical disturbance conditions. Finally, multiple IEEE test systems are used for verification, which demonstrates the effectiveness and feasibility of the proposed method.

Ruihao Zhou, Penghui Liu, Wenxiang Li et al. · Processes · 0 citations