{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T23:31:14Z","timestamp":1773271874712,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T00:00:00Z","timestamp":1569369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China under Grant","award":["51577046"],"award-info":[{"award-number":["51577046"]}]},{"name":"the State Key Program of National Natural Science Foundation of China under Grant","award":["51637004"],"award-info":[{"award-number":["51637004"]}]},{"name":"the National Key Research and Development Plan &quot;important scientific instruments and equipment development&quot;","award":["2016YFF0102200"],"award-info":[{"award-number":["2016YFF0102200"]}]},{"name":"Equipment Research Project in Advance Grant","award":["41402040301"],"award-info":[{"award-number":["41402040301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The idea of Ubiquitous Power Internet of Things (UPIoTs) accelerates the development of intelligent monitoring and diagnostic technologies. In this paper, a diagnostic method suitable for power equipment in an interference environment was proposed based on the deep Convolutional Neural Network (CNN): MobileNet-V2 and Digital Image Processing (DIP) methods to conduct fault identification process: including fault type classification and fault localization. A data visualization theory was put forward in this paper, which was applied in frequency response (FR) curves of transformer to obtain dataset. After the image augmentation process, the dataset was input into the deep CNN: MobileNet-V2 for training procedures. Then a spatial-probabilistic mapping relationship was established based on traditional Frequency Response Analysis (FRA) fault diagnostic method. Each image in the dataset was compared with the fingerprint values to get traditional diagnosing results. Next, the anti-interference abilities of the proposed CNN-DIP method were compared with that of the traditional one while the magnitude of the interference gradually increased. Finally, the fault tolerance of the proposed method was verified by further analyzing the deviations between the wrong diagnosing results with the corresponding actual labels. Experimental results showed that the proposed deep visual identification (CNN-DIP) method has a higher diagnosing accuracy, a stronger anti-interference ability and a better fault tolerance.<\/jats:p>","DOI":"10.3390\/s19194153","type":"journal-article","created":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T03:06:51Z","timestamp":1569467211000},"page":"4153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Anti-Interference Deep Visual Identification Method for Fault Localization of Transformer Using a Winding Model"],"prefix":"10.3390","volume":"19","author":[{"given":"Jiajun","family":"Duan","sequence":"first","affiliation":[{"name":"School of electrical engineering and automation, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yigang","family":"He","sequence":"additional","affiliation":[{"name":"School of electrical engineering and automation, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoxin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of electrical engineering and automation, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of electrical engineering and automation, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Wu","sequence":"additional","affiliation":[{"name":"School of electrical engineering and automation, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/JIOT.2018.2802704","article-title":"Review of Internet of Things (IoT) in Electric Power and Energy Systems","volume":"5","author":"Bedi","year":"2018","journal-title":"IEEE Internet Things"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TDEI.2018.007191","article-title":"Assessment of Computational Intelligence and Conventional Dissolved Gas Analysis Methods for Transformer Fault Diagnosis","volume":"25","author":"Faiz","year":"2018","journal-title":"IEEE Trans. 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