Study on Power Consumption and Stealing Law of Large-Scale Low Voltage Power Supply Area
There are few studies on power consumption in low-voltage regions with 220 V and 380 V supply voltage at home and abroad, so it is difficult to explore the problem of real-time line loss. In this paper, data mining was done in the four aspects of power supply, power utilization, line loss, and elect...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Journal of Taiyuan University of Technology
2022-01-01
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| Series: | Taiyuan Ligong Daxue xuebao |
| Subjects: | |
| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-1665.html |
| _version_ | 1797208379560558592 |
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| author | Xi CHEN Chunhe SONG Tianran WANG |
| author_facet | Xi CHEN Chunhe SONG Tianran WANG |
| author_sort | Xi CHEN |
| collection | DOAJ |
| description | There are few studies on power consumption in low-voltage regions with 220 V and 380 V supply voltage at home and abroad, so it is difficult to explore the problem of real-time line loss. In this paper, data mining was done in the four aspects of power supply, power utilization, line loss, and electricity stealing, statistical model and prediction model were established, electric power consumption and stealing law was explored. The statistical model of time series analysis, frequency spectrum analysis, probability density functions and complementary cumulative distribution function was used to analyze the results, showing that the power cycle of the users stealing electricity is twice as long as that of the normal users. The average daily electricity consumption of electricity-stealing users is higher than that of normal users. When the line loss rate in the station area is greater than 40%, the number of power-stealing users increases rapidly, while when the liue loss rate reaches 65%, the number flattens, so the area in this range can be prioritized. On the basis of the clustering model combining the time series similarity measure and k-means clustering, the high-dimensional time clustering was used to obtain 6 types of health degree platform, and the distribution differences were compared by kernel density estimation. It can help quickly identify key power stealing areas and monitor the improvement of line loss rate. |
| first_indexed | 2024-04-24T09:37:52Z |
| format | Article |
| id | doaj.art-a664e244796148fd8086553989d33df9 |
| institution | Directory Open Access Journal |
| issn | 1007-9432 |
| language | English |
| last_indexed | 2024-04-24T09:37:52Z |
| publishDate | 2022-01-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj.art-a664e244796148fd8086553989d33df92024-04-15T08:41:31ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322022-01-01531717910.16355/j.cnki.issn1007-9432tyut.2022.01.0091007-9432(2022)01-0071-09Study on Power Consumption and Stealing Law of Large-Scale Low Voltage Power Supply AreaXi CHEN0Chunhe SONG1Tianran WANG2Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang 110016, ChinaKey Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang 110016, ChinaShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaThere are few studies on power consumption in low-voltage regions with 220 V and 380 V supply voltage at home and abroad, so it is difficult to explore the problem of real-time line loss. In this paper, data mining was done in the four aspects of power supply, power utilization, line loss, and electricity stealing, statistical model and prediction model were established, electric power consumption and stealing law was explored. The statistical model of time series analysis, frequency spectrum analysis, probability density functions and complementary cumulative distribution function was used to analyze the results, showing that the power cycle of the users stealing electricity is twice as long as that of the normal users. The average daily electricity consumption of electricity-stealing users is higher than that of normal users. When the line loss rate in the station area is greater than 40%, the number of power-stealing users increases rapidly, while when the liue loss rate reaches 65%, the number flattens, so the area in this range can be prioritized. On the basis of the clustering model combining the time series similarity measure and k-means clustering, the high-dimensional time clustering was used to obtain 6 types of health degree platform, and the distribution differences were compared by kernel density estimation. It can help quickly identify key power stealing areas and monitor the improvement of line loss rate.https://tyutjournal.tyut.edu.cn/englishpaper/show-1665.htmldata miningelectricity consumptionstealing electricitystatistical modelsclustering |
| spellingShingle | Xi CHEN Chunhe SONG Tianran WANG Study on Power Consumption and Stealing Law of Large-Scale Low Voltage Power Supply Area Taiyuan Ligong Daxue xuebao data mining electricity consumption stealing electricity statistical models clustering |
| title | Study on Power Consumption and Stealing Law of Large-Scale Low Voltage Power Supply Area |
| title_full | Study on Power Consumption and Stealing Law of Large-Scale Low Voltage Power Supply Area |
| title_fullStr | Study on Power Consumption and Stealing Law of Large-Scale Low Voltage Power Supply Area |
| title_full_unstemmed | Study on Power Consumption and Stealing Law of Large-Scale Low Voltage Power Supply Area |
| title_short | Study on Power Consumption and Stealing Law of Large-Scale Low Voltage Power Supply Area |
| title_sort | study on power consumption and stealing law of large scale low voltage power supply area |
| topic | data mining electricity consumption stealing electricity statistical models clustering |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-1665.html |
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