Research on Low-Resistance Grounding Fault Line Selection Based on VMD with PE and K-Means Clustering Algorithm

• Published in: Mathematical problems in engineering 2022-02, Vol.2022, p.1-17
• Main Authors: Cao, Wensi, Li, Chen, Li, Zhaohui, Wang, Shuo, Pang, Heyuan
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Cluster analysis; Clustering; Decomposition; Entropy; Fault diagnosis; Feature extraction; Mathematical analysis; Mathematical problems; Noise; Permutations; Signal processing; Vector quantization; Wavelet transforms; Zero sequence current
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2022/5360302

Aiming at the fault line selection problem in the single-phase grounding system of the distribution network, a new fault line selection method based on VMD and permutation entropy feature extraction combined with K-means clustering algorithm is proposed. This method is a hybrid algorithm that can effectively identify fault line selection. Firstly, a simulation model is built and its zero sequence current is collected. The variational modal decomposition method is used to decompose the collected zero-sequence current into multiple intrinsic modal functions, which can not only effectively reduce the influence of harmonic components and noise in the characteristic signal but also facilitate the calculation. The extracted intrinsic mode function is calculated by permutation entropy (PE), and the calculated entropy value is constructed into a matrix to highlight the fault characteristics of the line; then, the matrix is subjected to K-means cluster analysis through the preprocessing algorithm and the faulty line is correctly distinguished. Then, regression verification is performed. Finally, it is verified by the recorded wave data of the real test site and then analyzed and compared with other algorithms. The proposed method shows that when a single-phase ground fault occurs, the ground fault line selection can be effectively identified under different transition resistances, grounding resistances, and fault distances. Therefore, this method can accurately identify the fault line selection, and the accuracy rate is 100%, which has a certain use value.