{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T08:58:34Z","timestamp":1770281914707,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T00:00:00Z","timestamp":1608595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research and Innovation Fund of Kaunas University of Technology","award":["PP-91H\/19"],"award-info":[{"award-number":["PP-91H\/19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>According to the statistics, 40% of unplanned disruptions in electricity distribution grids are caused by failure of equipment in high voltage (HV) transformer substations. These damages in most cases are caused by partial discharge (PD) phenomenon which progressively leads to false operation of equipment. The detection and localization of PD at early stage can significantly reduce repair and maintenance expenses of HV assets. In this paper, a non-invasive PD detection and localization solution has been proposed, which uses three ultrasonic sensors arranged in an L shape to detect, identify and localize PD source. The solution uses a fusion of ultrasonic signal processing, machine learning (ML) and deep learning (DL) methods to classify and process PD signals. The research revealed that the support vector machines classifier performed best among two other classifiers in terms of sensitivity and specificity while classifying discharge and surrounding noise signals. The proposed ultrasonic signal processing methods based on binaural principles allowed us to achieve an experimental lateral source positioning error of 0.1 m by using 0.2 m spacing between L shaped sensors. Finally, an approach based on DL was suggested, which allowed us to detect a single PD source in optical images and, in such a way, to provide visual representation of PD location.<\/jats:p>","DOI":"10.3390\/s21010020","type":"journal-article","created":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T20:39:29Z","timestamp":1608669569000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9960-7619","authenticated-orcid":false,"given":"Vykintas","family":"Samaitis","sequence":"first","affiliation":[{"name":"Prof. K. Bar\u0161auskas Ultrasound Research Institute, Kaunas University of Technology, K. Bar\u0161ausko St. 59, LT-51423 Kaunas, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liudas","family":"Ma\u017eeika","sequence":"additional","affiliation":[{"name":"Prof. K. Bar\u0161auskas Ultrasound Research Institute, Kaunas University of Technology, K. Bar\u0161ausko St. 59, LT-51423 Kaunas, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3977-7549","authenticated-orcid":false,"given":"Audrius","family":"Jankauskas","sequence":"additional","affiliation":[{"name":"Prof. K. Bar\u0161auskas Ultrasound Research Institute, Kaunas University of Technology, K. Bar\u0161ausko St. 59, LT-51423 Kaunas, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Regina","family":"Rekuvien\u0117","sequence":"additional","affiliation":[{"name":"Prof. K. Bar\u0161auskas Ultrasound Research Institute, Kaunas University of Technology, K. Bar\u0161ausko St. 59, LT-51423 Kaunas, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,22]]},"reference":[{"key":"ref_1","unstructured":"European Commission (2020, November 16). Study on the Quality of Electricity Market. Available online: https:\/\/ec.europa.eu\/energy\/sites\/ener\/files\/documents\/dg_ener_electricity_market_data_-_final_report_-_22032018.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Luo, Y., Li, Z., Wang, H., Luo, Y., Li, Z., and Wang, H. (2017). A review of online partial discharge measurement of large generators. Energies, 10.","DOI":"10.3390\/en10111694"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"115108","DOI":"10.1088\/0957-0233\/20\/11\/115108","article-title":"Acoustic measuring of partial discharge in power transformers","volume":"20","author":"Pascacio","year":"2009","journal-title":"Meas. Sci. 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