A Data-Driven Combined Algorithm for Abnormal Power Loss Detection in the Distribution Network
Power loss, consisting of technical loss (TL) and non-technical loss (NTL), reflects the effective utilization rate of energy and the management level of power grids. This paper proposes a data-driven combined algorithm to systematically identify anomalies of power loss in the distribution network,...
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| Veröffentlicht in: | IEEE access 2020, Vol.8, p.24675-24686 |
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| creator | Long, Huan Chen, Chang Gu, Wei Xie, Jihua Wang, Zheng Li, Guodong |
| description | Power loss, consisting of technical loss (TL) and non-technical loss (NTL), reflects the effective utilization rate of energy and the management level of power grids. This paper proposes a data-driven combined algorithm to systematically identify anomalies of power loss in the distribution network, including the abnormal type, time, and position. The detection process contains three stages: abnormal feeder detection, abnormal time detection, and abnormal position detection. The suspected abnormal feeders are first detected from all feeders in the distribution network by the data-driven algorithm based on the daily power supply and electricity sales data. Then, the control chart is employed to further monitor the fluctuation of the power loss of each suspected abnormal feeder and discover its abnormal time. Based on the detected abnormal time, its abnormal position is finally located through the risk assessment technology. Numerous experiments based on the real data show that the proposed data-driven combined algorithm can effectively detect and analyze abnormal power loss in the distribution network. |
| doi_str_mv | 10.1109/ACCESS.2020.2970548 |
| format | Article |
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This paper proposes a data-driven combined algorithm to systematically identify anomalies of power loss in the distribution network, including the abnormal type, time, and position. The detection process contains three stages: abnormal feeder detection, abnormal time detection, and abnormal position detection. The suspected abnormal feeders are first detected from all feeders in the distribution network by the data-driven algorithm based on the daily power supply and electricity sales data. Then, the control chart is employed to further monitor the fluctuation of the power loss of each suspected abnormal feeder and discover its abnormal time. Based on the detected abnormal time, its abnormal position is finally located through the risk assessment technology. 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(IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-b78dc307b50e9fb1a678636aae03749406e503d670a19037c5ede7b1ef1536a43</citedby><cites>FETCH-LOGICAL-c408t-b78dc307b50e9fb1a678636aae03749406e503d670a19037c5ede7b1ef1536a43</cites><orcidid>0000-0003-2701-0009 ; 0000-0002-6578-9140</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8976185$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,4010,27612,27902,27903,27904,54911</link.rule.ids></links><search><creatorcontrib>Long, Huan</creatorcontrib><creatorcontrib>Chen, Chang</creatorcontrib><creatorcontrib>Gu, Wei</creatorcontrib><creatorcontrib>Xie, Jihua</creatorcontrib><creatorcontrib>Wang, Zheng</creatorcontrib><creatorcontrib>Li, Guodong</creatorcontrib><title>A Data-Driven Combined Algorithm for Abnormal Power Loss Detection in the Distribution Network</title><title>IEEE access</title><addtitle>Access</addtitle><description>Power loss, consisting of technical loss (TL) and non-technical loss (NTL), reflects the effective utilization rate of energy and the management level of power grids. This paper proposes a data-driven combined algorithm to systematically identify anomalies of power loss in the distribution network, including the abnormal type, time, and position. The detection process contains three stages: abnormal feeder detection, abnormal time detection, and abnormal position detection. The suspected abnormal feeders are first detected from all feeders in the distribution network by the data-driven algorithm based on the daily power supply and electricity sales data. Then, the control chart is employed to further monitor the fluctuation of the power loss of each suspected abnormal feeder and discover its abnormal time. Based on the detected abnormal time, its abnormal position is finally located through the risk assessment technology. Numerous experiments based on the real data show that the proposed data-driven combined algorithm can effectively detect and analyze abnormal power loss in the distribution network.</description><subject>abnormal detection</subject><subject>Algorithms</subject><subject>Anomalies</subject><subject>Anomaly detection</subject><subject>control chart</subject><subject>Control charts</subject><subject>data-driven algorithm</subject><subject>Distribution networks</subject><subject>Electric power distribution</subject><subject>Electric power grids</subject><subject>Feeders</subject><subject>Indexes</subject><subject>Power loss</subject><subject>Power supplies</subject><subject>Risk assessment</subject><subject>Risk management</subject><subject>Technology assessment</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1P3DAQjSqQQJRfwMVSz1nGX7FzjLK0IK3aSpQrlh1PwNvdmDpeUP99DUGoc5nR07w3H6-qLiisKIX2suv7q9vbFQMGK9YqkEJ_qk4ZbdqaS94c_VefVOfzvIUSukBSnVb3HVnbbOt1Cs84kT7uXZjQk273EFPIj3syxkQ6N8W0tzvyM75gIps4z2SNGYcc4kTCRPIjknWYcwru8IZ9x_wS0-_P1fFodzOev-ez6u7r1a_-ut78-HbTd5t6EKBz7ZT2AwflJGA7OmobpRveWIvAlWgFNCiB-0aBpW2BBokelaM40nKVFfysull0fbRb85TC3qa_Jtpg3oCYHoxNOQw7NOBazSTIgUsqNPcOwdvBMa-5HK3wRevLovWU4p8Dztls4yFNZX3DhBRKMMlU6eJL15DKNxKOH1MpmFdfzOKLefXFvPtSWBcLKyDiB0O3qqFa8n-u64gw</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Long, Huan</creator><creator>Chen, Chang</creator><creator>Gu, Wei</creator><creator>Xie, Jihua</creator><creator>Wang, Zheng</creator><creator>Li, Guodong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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This paper proposes a data-driven combined algorithm to systematically identify anomalies of power loss in the distribution network, including the abnormal type, time, and position. The detection process contains three stages: abnormal feeder detection, abnormal time detection, and abnormal position detection. The suspected abnormal feeders are first detected from all feeders in the distribution network by the data-driven algorithm based on the daily power supply and electricity sales data. Then, the control chart is employed to further monitor the fluctuation of the power loss of each suspected abnormal feeder and discover its abnormal time. Based on the detected abnormal time, its abnormal position is finally located through the risk assessment technology. Numerous experiments based on the real data show that the proposed data-driven combined algorithm can effectively detect and analyze abnormal power loss in the distribution network.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.2970548</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-2701-0009</orcidid><orcidid>https://orcid.org/0000-0002-6578-9140</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | abnormal detection Algorithms Anomalies Anomaly detection control chart Control charts data-driven algorithm Distribution networks Electric power distribution Electric power grids Feeders Indexes Power loss Power supplies Risk assessment Risk management Technology assessment |
| title | A Data-Driven Combined Algorithm for Abnormal Power Loss Detection in the Distribution Network |
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