Moisture Diagnosis of Transformer Oil-Immersed Insulation With Intelligent Technique and Frequency-Domain Spectroscopy
Moisture is one of the critical factors to determine the service life of transformers. The moisture inside the transformer oil-immersed insulation could be quantified with feature parameters. This article proposes and develops a genetic algorithm support vector machine (GA-SVM) model to carry out th...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2021-07, Vol.17 (7), p.4624-4634 |
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| container_title | IEEE transactions on industrial informatics |
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| creator | Liu, Jiefeng Fan, Xianhao Zhang, Chaohai Lai, Chun Sing Zhang, Yiyi Zheng, Hanbo Lai, Loi Lei Zhang, Enze |
| description | Moisture is one of the critical factors to determine the service life of transformers. The moisture inside the transformer oil-immersed insulation could be quantified with feature parameters. This article proposes and develops a genetic algorithm support vector machine (GA-SVM) model to carry out the moisture diagnosis. Present findings reveal that these feature parameters can be obtained by using frequency-domain spectroscopy. Therefore, a novel model for predicting the frequency-domain spectroscopy curves is first reported based on a small number of samples, which could be utilized to obtain the feature parameters database to develop GA-SVM. Then, the moisture diagnosis in the lab and field conditions is presented to verify its feasibility and accuracy. The novelty of this article is in an exploration of the reported model as an intelligent based moisture diagnosis tool for power transformers. |
| doi_str_mv | 10.1109/TII.2020.3014224 |
| format | Article |
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The moisture inside the transformer oil-immersed insulation could be quantified with feature parameters. This article proposes and develops a genetic algorithm support vector machine (GA-SVM) model to carry out the moisture diagnosis. Present findings reveal that these feature parameters can be obtained by using frequency-domain spectroscopy. Therefore, a novel model for predicting the frequency-domain spectroscopy curves is first reported based on a small number of samples, which could be utilized to obtain the feature parameters database to develop GA-SVM. Then, the moisture diagnosis in the lab and field conditions is presented to verify its feasibility and accuracy. The novelty of this article is in an exploration of the reported model as an intelligent based moisture diagnosis tool for power transformers.</description><identifier>ISSN: 1551-3203</identifier><identifier>EISSN: 1941-0050</identifier><identifier>DOI: 10.1109/TII.2020.3014224</identifier><identifier>CODEN: ITIICH</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Diagnosis ; Dielectrics ; Frequency domain analysis ; Frequency-domain spectroscopy (FDS) ; genetic algorithm support vector machine (GA-SVM) ; Genetic algorithms ; Insulation ; Mathematical models ; Moisture ; moisture diagnosis ; Oil insulation ; oil-immersed insulation ; Parameters ; power transformer ; Power transformer insulation ; Service life ; Spectroscopy ; Spectrum analysis ; Support vector machines ; Transformers</subject><ispartof>IEEE transactions on industrial informatics, 2021-07, Vol.17 (7), p.4624-4634</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c380t-5cc920ed612f332c22af602ca0d860c9b0f0cb79acea7bb3d9fad76ac82ac3883</citedby><cites>FETCH-LOGICAL-c380t-5cc920ed612f332c22af602ca0d860c9b0f0cb79acea7bb3d9fad76ac82ac3883</cites><orcidid>0000-0002-4169-4438 ; 0000-0003-0129-1252 ; 0000-0002-7660-7293 ; 0000-0003-4786-7931 ; 0000-0002-2283-8328 ; 0000-0001-8394-6659 ; 0000-0001-8785-126X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9157956$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9157956$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Liu, Jiefeng</creatorcontrib><creatorcontrib>Fan, Xianhao</creatorcontrib><creatorcontrib>Zhang, Chaohai</creatorcontrib><creatorcontrib>Lai, Chun Sing</creatorcontrib><creatorcontrib>Zhang, Yiyi</creatorcontrib><creatorcontrib>Zheng, Hanbo</creatorcontrib><creatorcontrib>Lai, Loi Lei</creatorcontrib><creatorcontrib>Zhang, Enze</creatorcontrib><title>Moisture Diagnosis of Transformer Oil-Immersed Insulation With Intelligent Technique and Frequency-Domain Spectroscopy</title><title>IEEE transactions on industrial informatics</title><addtitle>TII</addtitle><description>Moisture is one of the critical factors to determine the service life of transformers. The moisture inside the transformer oil-immersed insulation could be quantified with feature parameters. This article proposes and develops a genetic algorithm support vector machine (GA-SVM) model to carry out the moisture diagnosis. Present findings reveal that these feature parameters can be obtained by using frequency-domain spectroscopy. Therefore, a novel model for predicting the frequency-domain spectroscopy curves is first reported based on a small number of samples, which could be utilized to obtain the feature parameters database to develop GA-SVM. Then, the moisture diagnosis in the lab and field conditions is presented to verify its feasibility and accuracy. The novelty of this article is in an exploration of the reported model as an intelligent based moisture diagnosis tool for power transformers.</description><subject>Diagnosis</subject><subject>Dielectrics</subject><subject>Frequency domain analysis</subject><subject>Frequency-domain spectroscopy (FDS)</subject><subject>genetic algorithm support vector machine (GA-SVM)</subject><subject>Genetic algorithms</subject><subject>Insulation</subject><subject>Mathematical models</subject><subject>Moisture</subject><subject>moisture diagnosis</subject><subject>Oil insulation</subject><subject>oil-immersed insulation</subject><subject>Parameters</subject><subject>power transformer</subject><subject>Power transformer insulation</subject><subject>Service life</subject><subject>Spectroscopy</subject><subject>Spectrum analysis</subject><subject>Support vector machines</subject><subject>Transformers</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kN1LwzAUxYsoOKfvgi8Bnztvkn7lUabTwmQPVnwsaZpuGW1Sk1TYf2_Ghk_3XDjn3sMviu4xLDAG9lSV5YIAgQUFnBCSXEQzzBIcA6RwGXSa4pgSoNfRjXN7AJoDZbPo98Mo5ycr0YviW22ccsh0qLJcu87YQVq0UX1cDkE52aJSu6nnXhmNvpXfhd3LvldbqT2qpNhp9TNJxHWLVlYGqcUhfjEDVxp9jlJ4a5ww4-E2uup47-Tdec6jr9VrtXyP15u3cvm8jgUtwMepEIyAbDNMOkqJIIR3GRDBoS0yEKyBDkSTMy4kz5uGtqzjbZ5xURAeLhR0Hj2e7o7WhDbO13szWR1e1iTFkFDIaBJccHKJUM9Z2dWjVQO3hxpDfaRbB7r1kW59phsiD6eIklL-2xlOc5Zm9A8OAHiO</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Liu, Jiefeng</creator><creator>Fan, Xianhao</creator><creator>Zhang, Chaohai</creator><creator>Lai, Chun Sing</creator><creator>Zhang, Yiyi</creator><creator>Zheng, Hanbo</creator><creator>Lai, Loi Lei</creator><creator>Zhang, Enze</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Diagnosis Dielectrics Frequency domain analysis Frequency-domain spectroscopy (FDS) genetic algorithm support vector machine (GA-SVM) Genetic algorithms Insulation Mathematical models Moisture moisture diagnosis Oil insulation oil-immersed insulation Parameters power transformer Power transformer insulation Service life Spectroscopy Spectrum analysis Support vector machines Transformers |
| title | Moisture Diagnosis of Transformer Oil-Immersed Insulation With Intelligent Technique and Frequency-Domain Spectroscopy |
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