CN107870275A - Arrester evaluation of running status method based on big data - Google Patents
Arrester evaluation of running status method based on big data Download PDFInfo
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- CN107870275A CN107870275A CN201711068600.1A CN201711068600A CN107870275A CN 107870275 A CN107870275 A CN 107870275A CN 201711068600 A CN201711068600 A CN 201711068600A CN 107870275 A CN107870275 A CN 107870275A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/003—Environmental or reliability tests
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- Environmental & Geological Engineering (AREA)
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- General Physics & Mathematics (AREA)
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
Abstract
Description
Claims (9)
- A kind of 1. arrester evaluation of running status method based on big data, it is characterised in that comprise the following steps:Data collection layer obtain arrester online monitoring data, live detection data, basic account data, off-line testing data, Service data and environmental data, data storage layer, will using distributed computing framework, internal memory Computational frame, streaming computing framework Structural data, unstructured data, the real-time monitoring data of acquisition are formatted processing, and each data after processing are joined Into database, Data Analysis Platform layer is repaiied according to data influence factor to the Various types of data of data storage layer for amount storage Just, and to revised data carry out aspect ratio analysis calculates, significant difference analysis calculates, alternate uneven analysis calculates, Online monitoring data calculates with test data variance analysis, and application layer is that abnormal appraisal of equipment is note to analysis result Meaning, it is that normal equipment calculates score according to default evaluation criterion to analysis result, obtains arrester running status and comment Valency result.
- 2. the arrester evaluation of running status method according to claim 1 based on big data, it is characterised in that the base Plinth account data includes device name, model name, date of putting into operation, voltage class, insulator types, continuous running voltage, work Frequency reference voltage, power-frequency sparkover voltage, nominal discharge current, lightning impulse residual voltage and retting-flax wastewater.
- 3. the arrester evaluation of running status method according to claim 1 based on big data, it is characterised in that described to keep away Thunder device online monitoring data includes Leakage Current, current in resistance property and capacity current.
- 4. the arrester evaluation of running status method according to claim 1 based on big data, it is characterised in that described vertical It is horizontal to be specially than analysis calculating:If A, the current on-line monitoring value of one group of arrester of B, C three-phase is respectively a1, b1, c1, before one week On-line monitoring value be respectively a2, b2, c2, when a2/ (b2+c2) and a1/ (b1+c1) deviation exceed positive and negative 30%, aspect ratio Analysis result is abnormal.
- 5. the arrester evaluation of running status method according to claim 1 based on big data, it is characterised in that described aobvious Sex differernce analysis meter is write to specifically include:If the online monitoring data of arrester one average value of nearly 2 days is X, Monitoring Data sample Standard deviation is S, and the currently monitored value of arrester is x, then significant difference is:Deterioration shows as x when monitor value is reduced<X- KS, deterioration show as x during monitor value increase>X+kS, X-kS when deviateing initial value<x<X+kS, wherein k are constant.
- 6. the arrester evaluation of running status method according to claim 1 based on big data, it is characterised in that the phase Between uneven analysis meter be specially:If A, one group of arrester equipment Leakage Current of B, C three-phase currently on-line monitoring value difference a3, B3, c3, three-phase Leakage Current on-line monitoring average value be d=(a3+b3+c3)/ 3, then arrester Leakage Current three-phase imbalance system Number K=max (| a3-d |, | b3-d |, | c3-d |)/d, if K>5%, the alternate uneven analysis result of arrester Leakage Current is different Often.
- 7. the arrester evaluation of running status method according to claim 1 based on big data, it is characterised in that it is described Line Monitoring Data calculates with test data variance analysis:The current on-line monitoring value of any phase Leakage Current of arrester equipment For a4, the mutually the last Leakage Current off-line testing data are a5, then online monitoring data and test data deviation be p= (a4-a5)/a5, if p>50%, online monitoring data is abnormal with test data variance analysis result.
- 8. the arrester evaluation of running status method according to claim 1 based on big data, it is characterised in that the number Include according to influence factor:Floor installation mode, ambient temperature and humidity, filthy situation, salt dirt situation, testing current in resistance property method, electricity Magnetic interference, live meter quality.
- 9. the arrester evaluation of running status method according to claim 1 based on big data, it is characterised in that work as environment When humidity is more than 75% in data, current arrester Monitoring Data is invalid data.
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Cited By (8)
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CN108763304A (en) * | 2018-04-20 | 2018-11-06 | 国家电网有限公司 | A kind of electric power account data verification method and device based on genetic connection |
CN108805412A (en) * | 2018-05-18 | 2018-11-13 | 广东电网有限责任公司 | Lightning arrester evaluation device and method based on big data analysis |
CN110929751A (en) * | 2019-10-16 | 2020-03-27 | 福建和盛高科技产业有限公司 | Current transformer unbalance warning method based on multi-source data fusion |
CN112255484A (en) * | 2020-10-19 | 2021-01-22 | 国网河南省电力公司电力科学研究院 | Lightning arrester operation state online monitoring and assessment method and system |
CN112348697A (en) * | 2020-10-21 | 2021-02-09 | 国网天津市电力公司 | Power grid running state comprehensive evaluation method and device based on big data |
CN113281674A (en) * | 2021-01-25 | 2021-08-20 | 国网河南省电力公司邓州市供电公司 | Lightning arrester defect assessment system based on big data analysis and use method thereof |
EP3747100B1 (en) * | 2018-01-30 | 2022-03-16 | Hitachi Energy Switzerland AG | Surge arrestor dimensioning in a dc power transmission system |
CN117493953A (en) * | 2023-10-31 | 2024-02-02 | 国网青海省电力公司海北供电公司 | Lightning arrester state evaluation method based on defect data mining |
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Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP3747100B1 (en) * | 2018-01-30 | 2022-03-16 | Hitachi Energy Switzerland AG | Surge arrestor dimensioning in a dc power transmission system |
CN108763304A (en) * | 2018-04-20 | 2018-11-06 | 国家电网有限公司 | A kind of electric power account data verification method and device based on genetic connection |
CN108763304B (en) * | 2018-04-20 | 2020-12-29 | 国家电网有限公司 | Blood-cause relationship-based power standing book data verification method and device |
CN108805412A (en) * | 2018-05-18 | 2018-11-13 | 广东电网有限责任公司 | Lightning arrester evaluation device and method based on big data analysis |
CN110929751A (en) * | 2019-10-16 | 2020-03-27 | 福建和盛高科技产业有限公司 | Current transformer unbalance warning method based on multi-source data fusion |
CN112255484A (en) * | 2020-10-19 | 2021-01-22 | 国网河南省电力公司电力科学研究院 | Lightning arrester operation state online monitoring and assessment method and system |
CN112255484B (en) * | 2020-10-19 | 2022-03-25 | 国网河南省电力公司电力科学研究院 | Lightning arrester operation state online monitoring and assessment method and system |
CN112348697A (en) * | 2020-10-21 | 2021-02-09 | 国网天津市电力公司 | Power grid running state comprehensive evaluation method and device based on big data |
CN113281674A (en) * | 2021-01-25 | 2021-08-20 | 国网河南省电力公司邓州市供电公司 | Lightning arrester defect assessment system based on big data analysis and use method thereof |
CN117493953A (en) * | 2023-10-31 | 2024-02-02 | 国网青海省电力公司海北供电公司 | Lightning arrester state evaluation method based on defect data mining |
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