In nonsolidly earthed distribution networks, single-phase-to-ground faults (SPGFs) significantly threaten the safety of people and equipment. Although selection and location techniques for the existing fault lines have made remarkable contributions in reducing the damage due to SPGFs, a certain amount of power loss still exists in the SPGF owing to its low efficiency in detection and maintenance. Multiple-dimensional classification of the SPGF May help reveal the nature of the fault from different perspectives; therefore, in this article, a multilabel classification model for recognizing the types of SPGF is proposed. In the proposed model, the SPGF is classified considering five dimensions, namely time-domain continuity, time-domain stability, volt-ampere characteristics of transition impedance, transition impedance size, and fault point medium. Subsequently, the corresponding features are determined. In addition, a multilabel classification model of the SPGF is constructed with an 8-D feature space and a 14-label fault-type space. Finally, a k-nearest neighbors Bayesian method is designed to solve the multilabel classification problem. The feasibility and advantages of the proposed model and methods are verified using field data and through comparison with the KNN method.

中文翻译：

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基于KNN-贝叶斯方法的单相接地故障类型识别的多标签分类模型

在非牢固接地的配电网络中，单相接地故障（SPGF）严重威胁着人员和设备的安全。尽管现有故障线的选择和定位技术在减少SPGF造成的损害方面做出了显着贡献，但由于SPGF的检测和维护效率低，因此在SPGF中仍然存在一定的功率损耗。SPGF的多维分类可能有助于从不同角度揭示断层的性质。因此，在本文中，提出了一种用于识别SPGF类型的多标签分类模型。在提出的模型中，考虑到五个维度来对SPGF进行分类，即时域连续性，时域稳定性，跃迁阻抗的伏安特性，跃迁阻抗大小和故障点介质。随后，确定相应的特征。此外，使用8D特征空间和14标签故障类型空间构建SPGF的多标签分类模型。最后，设计了一种k最近邻贝叶斯方法来解决多标签分类问题。利用现场数据并与KNN方法进行比较，验证了所提模型和方法的可行性和优势。