{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T07:41:35Z","timestamp":1777362095274,"version":"3.51.4"},"reference-count":18,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"name":"Civil Aerospace Technology Research Project","award":["D010103"],"award-info":[{"award-number":["D010103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p> The degree of insulation aging of power cables is closely related to their partial discharge (PD) level, so the analysis of PD signals can be used to realize the cable condition detection. However, after performing online detection of PDs on power cables, the collected signals always contain interference signals due to the influence of electromagnetic interference in the field. In order to identify each type of local discharge signal from the interference signal, this paper proposes a clustering identification algorithm for local discharge signals, which mainly involves pulse extraction, feature parameter extraction and clustering identification process. The algorithm first extracts the pulse signal by combining the amplitude\u2013time threshold method and the time domain energy method, then obtains the feature vector of the signal according to the synchronous multi-channel method, designs a fuzzy C-mean clustering algorithm based on subtractive clustering to determine the initial clustering center to cluster the samples and finally analyzes and checks the clustering results according to the phase resolved PD (PRPD) of a single class of signals and the fit of the two-parameter Weibull distribution function. The clustering results were analyzed and examined. The experimental results show that the proposed algorithm can extract pulse signals efficiently and accurately, and the synchronous multi-channel method can characterize pulse signals better. Meanwhile, the algorithm can determine the optimal number of classes adaptively according to the clustering effectiveness function and adopt subtractive clustering to initialize the clustering center, which can approach the optimal solution faster, and can effectively cluster a variety of discharge signals, so as to realize the type identification of single-class discharge signals. <\/jats:p>","DOI":"10.1142\/s0218001422580101","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T07:40:12Z","timestamp":1668411612000},"source":"Crossref","is-referenced-by-count":10,"title":["Feature Extraction and Pattern Recognition Algorithm of Power Cable Partial Discharge Signal"],"prefix":"10.1142","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6453-0754","authenticated-orcid":false,"given":"Jie","family":"Du","sequence":"first","affiliation":[{"name":"Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi\u2019an 710071, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6054-9327","authenticated-orcid":false,"given":"Jianwei","family":"Mi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi\u2019an 710071, P.\u00a0R.\u00a0China"}]},{"given":"Zhanpeng","family":"Jia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi\u2019an 710071, P.\u00a0R.\u00a0China"}]},{"given":"Jiaxiang","family":"Mei","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi\u2019an 710071, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"S0218001422580101BIB001","doi-asserted-by":"crossref","first-page":"102106","DOI":"10.1016\/j.displa.2021.102106","volume":"70","author":"Cai W.","year":"2021","journal-title":"Displays"},{"issue":"2","key":"S0218001422580101BIB002","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/TDEI.2005.1430391","volume":"12","author":"Cavallini A.","year":"2005","journal-title":"IEEE Trans. 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Insul."},{"issue":"4","key":"S0218001422580101BIB016","first-page":"1268","volume":"42","author":"Wang L.","year":"2016","journal-title":"High Volt. Eng."},{"issue":"6","key":"S0218001422580101BIB017","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/JSAC.2019.2904330","volume":"37","author":"Xu Y.","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"S0218001422580101BIB019","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/OJSP.2020.3036276","volume":"1","author":"Yin F.","year":"2020","journal-title":"IEEE Open J. Signal Process."},{"issue":"1","key":"S0218001422580101BIB020","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S0378-7796(99)00033-4","volume":"53","author":"Zhao W.","year":"2000","journal-title":"Electr. Power Syst. Res."}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001422580101","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T10:11:57Z","timestamp":1677924717000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218001422580101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":18,"journal-issue":{"issue":"01","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["10.1142\/S0218001422580101"],"URL":"https:\/\/doi.org\/10.1142\/s0218001422580101","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]},"article-number":"2258010"}}