Clustering Separation Method Based on Multi-Source Partial Discharge Signal Data Stream

• Published in: Shànghăi jiāotōng dàxué xuébào Vol. 56; no. 8; pp. 1014 - 1023
• Main Author: CHEN Changchuan, LIU Kai, LIU Renguang, FENG Xiaozong, QIN Yanjia, DAI Shaosheng, ZHANG Tianqi
• Format: Journal Article
• Language: Chinese
• Published: Editorial Office of Journal of Shanghai Jiao Tong University 01.08.2022
• Subjects: cluster separation; data stream; local adaptation; natural neighborhood; partial discharge (pd)
• ISSN: 1006-2467
• DOI: 10.16183/j.cnki.jsjtu.2021.195

In partial discharge(PD) detection, due to the simultaneous and constantly changing phenomenon of multiple discharge sources and on-site interference sources, it is difficult to effectively separate and identify multiple PD sources. An efficient adaptive efficient adaptive online data stream clustering algorithm (EAOStream) is proposed. The algorithm uses natural neighborhoods to create K-dimensional (KD) trees to improve the efficiency of querying neighbors. That is, the adaptive neighborhood radius and the area density are obtained through the characteristics of the flow data, which can search locally and form clusters, and realize the real-time online separation of multiple local discharge sources. The superiority of EAOStream is verified in the artificial data set and the real data set. After comparing EAOStream with the traditional DenStream and SE-Stream algorithms, it is applied to the pattern recognition of gas-insulated substation faults. Experimental test results show that the clustering accuracy of