{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T09:13:51Z","timestamp":1770282831104,"version":"3.49.0"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2023,12,12]]},"abstract":"<jats:p>Different types of partial discharge (PD) cause different damages to gas-insulated substation (GIS), so it is very important to correctly identify the type of PD for evaluating the GIS insulation condition. The traditional PD pattern recognition algorithm has the limitations of low recognition accuracy and slow recognition speed in engineering applications. To effectively diagnose the GIS PD type and safeguard the safe and reliable operation of the distribution network, a GIS PD method based on improved CBAM-ResNet was proposed in this paper. And the improved CBAM-ResNet takes advantage of the residual neural network and attention mechanism. In particular, the channel attention module and the spatial attention module are connected in parallel in the improved CBAM. The experimental results showed that the GIS PD pattern recognition method proposed herein has a recognition rate of 93.58%, 95.00%, 93.55%, and 93.88% against the four PD types. Compared with the traditional PD pattern recognition algorithm, the algorithm has the advantages of a lightweight model and more accurate recognition results, which carry better engineering application values.<\/jats:p>","DOI":"10.1155\/2023\/9948438","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T23:50:19Z","timestamp":1702425019000},"page":"1-10","source":"Crossref","is-referenced-by-count":3,"title":["A GIS Partial Discharge Pattern Recognition Method Based on Improved CBAM-ResNet"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5807-0138","authenticated-orcid":true,"given":"Di","family":"Hu","sequence":"first","affiliation":[{"name":"State Grid Anhui Electric Power Research Institute, Hefei 230601, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7815-290X","authenticated-orcid":true,"given":"Zhong","family":"Chen","sequence":"additional","affiliation":[{"name":"State Grid Anhui Electric Power Research Institute, Hefei 230601, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1671-6797","authenticated-orcid":true,"given":"Wei","family":"Yang","sequence":"additional","affiliation":[{"name":"State Grid Anhui Electric Power Research Institute, Hefei 230601, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5773-7087","authenticated-orcid":true,"given":"Taiyun","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Grid Anhui Electric Power Research Institute, Hefei 230601, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3356-7703","authenticated-orcid":true,"given":"Yanguo","family":"Ke","sequence":"additional","affiliation":[{"name":"State Grid Anhui Electric Power Research Institute, Hefei 230601, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1718-9551","authenticated-orcid":true,"given":"Kaiyang","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3084950"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.3390\/en11051202"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/tdei.2022.3198715"},{"issue":"11","key":"4","first-page":"93","article-title":"Research on partial discharge fusion diagnosis and intelligent early warning system of electrical equipment","volume":"57","author":"X. Wang","year":"2021","journal-title":"High Voltage Apparatus"},{"issue":"3","key":"5","first-page":"99","article-title":"Feature extraction method of PRPD data based on deep learning","volume":"57","author":"J. Yang","year":"2020","journal-title":"Electrical Measurement & Instrumentation"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tdei.2018.006930"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/mei.2018.8430041"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/tdei.2015.005300"},{"issue":"13","key":"9","first-page":"55","article-title":"Identification of transformer partial discharge types based on statistical features and probabilistic neural networks","volume":"46","author":"Z. Li","year":"2018","journal-title":"Power System Protection and Control"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/tdei.2018.006996"},{"key":"11","first-page":"30","article-title":"Feature parameters extraction method of partial discharge UHF signal based on textural features in time-frequency representation image","author":"T. Yan","year":"2018"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1049\/hve2.12135"},{"issue":"10","key":"13","first-page":"3283","article-title":"Research of partial discharge recognition based on deep belief nets","volume":"40","author":"X. B. Zhang","year":"2016","journal-title":"Power System Technology"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.3390\/sym14112464"},{"issue":"4","key":"15","first-page":"1122","article-title":"Partial discharge pattern recognition of transformer based on OS-ELM","volume":"44","author":"Q. Zhang","year":"2018","journal-title":"High Voltage Engineering"},{"key":"16","first-page":"57","article-title":"ReBNet: residual binarized neural network","author":"M. Ghasemzadeh"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8532714"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109220"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00444-8"},{"key":"20","first-page":"1026","article-title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","author":"K. He"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.3390\/buildings12101561"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2019.02.008"},{"issue":"1","key":"23","article-title":"Svm-based partial discharge pattern classification for gis Journal of Physics: conference Series","volume":"960","author":"L. Yin","year":"2018","journal-title":"IOP Publishing"},{"key":"24","first-page":"548","article-title":"Partial discharge pattern recognition of GIS based on CBAM-ResNet","author":"D. Hu"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2023\/9948438.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2023\/9948438.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2023\/9948438.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T23:50:25Z","timestamp":1702425025000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jece\/2023\/9948438\/"}},"subtitle":[],"editor":[{"given":"Gurvinder S.","family":"Virk","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"references-count":24,"alternative-id":["9948438","9948438"],"URL":"https:\/\/doi.org\/10.1155\/2023\/9948438","relation":{},"ISSN":["2090-0155","2090-0147"],"issn-type":[{"value":"2090-0155","type":"electronic"},{"value":"2090-0147","type":"print"}],"subject":[],"published":{"date-parts":[[2023,12,12]]}}}