Feature Selection for Effective Health Index Diagnoses of Power Transformers
This letter investigates an approach based on feature selection and classification techniques to reduce assessment complexities of power transformers. This approach decreases the number of features by extracting the most influential ones when determining the transformers health index (HI). Several f...
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| Veröffentlicht in: | IEEE transactions on power delivery 2018-12, Vol.33 (6), p.3223-3226 |
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| container_title | IEEE transactions on power delivery |
| container_volume | 33 |
| creator | Benhmed, Kamel Mooman, Abdelniser Younes, Abdunnaser Shaban, Khaled El-Hag, Ayman |
| description | This letter investigates an approach based on feature selection and classification techniques to reduce assessment complexities of power transformers. This approach decreases the number of features by extracting the most influential ones when determining the transformers health index (HI). Several filters and wrapper-based feature selection methods are investigated. The effectiveness of the selected features is validated through performance evaluations of various classification models. The experimental results demonstrate that water content, acidity, breakdown voltage, and FFA (Furan), are the most influential testing parameters in determining the transformer HI. |
| doi_str_mv | 10.1109/TPWRD.2017.2762920 |
| format | Article |
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This approach decreases the number of features by extracting the most influential ones when determining the transformers health index (HI). Several filters and wrapper-based feature selection methods are investigated. The effectiveness of the selected features is validated through performance evaluations of various classification models. The experimental results demonstrate that water content, acidity, breakdown voltage, and FFA (Furan), are the most influential testing parameters in determining the transformer HI.</description><identifier>ISSN: 0885-8977</identifier><identifier>EISSN: 1937-4208</identifier><identifier>DOI: 10.1109/TPWRD.2017.2762920</identifier><identifier>CODEN: ITPDE5</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial intelligence ; Classification ; condition monitoring ; Feature extraction ; Indexes ; Moisture content ; Oil insulation ; Oils ; Power transformer insulation ; Support vector machines ; transformer ; Transformers</subject><ispartof>IEEE transactions on power delivery, 2018-12, Vol.33 (6), p.3223-3226</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-675d42c96043c598e076b81e54b5f37932bd94671b3a7230c2f369e4b8698bb43</citedby><cites>FETCH-LOGICAL-c295t-675d42c96043c598e076b81e54b5f37932bd94671b3a7230c2f369e4b8698bb43</cites><orcidid>0000-0001-6707-3472 ; 0000-0003-3270-5358 ; 0000-0002-0217-2987 ; 0000-0002-5688-7515</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8068213$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8068213$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Benhmed, Kamel</creatorcontrib><creatorcontrib>Mooman, Abdelniser</creatorcontrib><creatorcontrib>Younes, Abdunnaser</creatorcontrib><creatorcontrib>Shaban, Khaled</creatorcontrib><creatorcontrib>El-Hag, Ayman</creatorcontrib><title>Feature Selection for Effective Health Index Diagnoses of Power Transformers</title><title>IEEE transactions on power delivery</title><addtitle>TPWRD</addtitle><description>This letter investigates an approach based on feature selection and classification techniques to reduce assessment complexities of power transformers. 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The experimental results demonstrate that water content, acidity, breakdown voltage, and FFA (Furan), are the most influential testing parameters in determining the transformer HI.</description><subject>Artificial intelligence</subject><subject>Classification</subject><subject>condition monitoring</subject><subject>Feature extraction</subject><subject>Indexes</subject><subject>Moisture content</subject><subject>Oil insulation</subject><subject>Oils</subject><subject>Power transformer insulation</subject><subject>Support vector machines</subject><subject>transformer</subject><subject>Transformers</subject><issn>0885-8977</issn><issn>1937-4208</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtLAzEUhYMoWKt_QDcB11Nv3slS-rCFgkUrLoeZ6Y22tJOaTH38e6e2uLocON-58BFyzaDHGLi7-ez1adDjwEyPG80dhxPSYU6YTHKwp6QD1qrMOmPOyUVKKwCQ4KBDpiMsml1E-oxrrJplqKkPkQ6936dPpGMs1s07ndQL_KaDZfFWh4SJBk9n4QsjnceiTi2ywZguyZkv1gmvjrdLXkbDeX-cTR8fJv37aVZxp5pMG7WQvHIapKiUswhGl5ahkqXywjjBy4WT2rBSFIYLqLgX2qEsrXa2LKXoktvD7jaGjx2mJl-FXazblzlnwiglFRNtix9aVQwpRfT5Ni43RfzJGeR7a_mftXxvLT9aa6GbA7RExH_AgrbtsvgFYbNnjw</recordid><startdate>20181201</startdate><enddate>20181201</enddate><creator>Benhmed, Kamel</creator><creator>Mooman, Abdelniser</creator><creator>Younes, Abdunnaser</creator><creator>Shaban, Khaled</creator><creator>El-Hag, Ayman</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE transactions on power delivery, 2018-12, Vol.33 (6), p.3223-3226 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Artificial intelligence Classification condition monitoring Feature extraction Indexes Moisture content Oil insulation Oils Power transformer insulation Support vector machines transformer Transformers |
| title | Feature Selection for Effective Health Index Diagnoses of Power Transformers |
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