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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Abstract
The present invention discloses a kind of arrester evaluation of running status method based on big data, arrester related data is obtained including data collection layer, data are formatted processing by data storage layer, and each data parameter after processing is stored into database, Data Analysis Platform layer is modified according to data influence factor to the Various types of data of data storage layer, and revised data are carried out with aspect ratio analysis and is calculated, significant difference analysis calculates, alternate uneven analysis calculates, online monitoring data calculates with test data variance analysis, application layer is that abnormal appraisal of equipment is attention to analysis result, it is that normal equipment calculates score according to default evaluation criterion to analysis result, obtain arrester evaluation of running status result.The present invention carries out analysis corrections to the influence factor of the running state data of arrester, is analyzed by comparing, is effectively extracted arrester running status characteristic quantity, improves the accuracy of arrester running state analysis.
Description
Technical field
The present invention relates to power safety technique field, is commented more particularly to a kind of arrester running status based on big data
Valency method.
Background technology
The multiple production management operation systems of grid company have accumulated power transmission and transformation specialty for many years since build up comprehensively
The environment in service data and grid equipment location, meteorology, disaster data, but due to lacking to multi-specialized, multi-source data
Fusion, can't be to the diagnosis of network system and equipment state using and to go back Shortcomings in terms of network system evaluation meanses
Early warning, the aid decision etc. of production management provide support.Currently, for all kinds of state inspection phases of arrester equipment
To maturation, data accurate and effective is high, can make full use of the arrester multi-source multidimensional service data of accumulation for many years, effectively carries out setting
Standby state dynamic analysis and diagnosis, system risk assessment and disaster alarm are realized, and development O＆M pattern is excellent on this basis
Change and resource allocation Optimization Work has become very urgent and needs.
Big data is excavated in recent years presents significant advantage than conventional analytical model, and big data digging technology can integrate
The multiple information such as structural data and unstructured data, can more effective land productivity compared to traditional approach using more efficient algorithm
With the data for constantly increasing at this stage and complicating.Therefore big data analytical technology and the multi-source data of grid company production run
It is good for solve identification, fast searching defect and failure cause, the accurate evaluation equipment of arrester running status Correlative Influence Factors
Health situation provides Method means.By data mining technology can effectively explore it is related to arrester running status it is all kinds of because
Element, find potential rule between every factor and running status.Analyzed by data influence factor analysis, comparing, shape
The functional modules such as state analysis and assessment, operations staff is apparent from the current actual health status of equipment, make accurate judgment, protect
Demonstrate,prove electric power netting safe running.
The content of the invention
In view of the shortcomings of the prior art, the present invention provides a kind of arrester evaluation of running status method based on big data,
Merge arrester multi source status amount data and carry out systematic evaluation analysis, effectively extract arrester running status characteristic quantity, carry
The accuracy of high arrester running state analysis.
To achieve the above object, the technical scheme is that：A kind of arrester evaluation of running status based on big data
Method, 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.
Further, the basic account data includes device name, model name, date of putting into operation, voltage class, insulation
Body type, continuous running voltage, power frequency reference voltage, power-frequency sparkover voltage, nominal discharge current, lightning impulse residual voltage and climb electricity
Than away from.
Further, the arrester online monitoring data includes Leakage Current, current in resistance property and capacity current.
Further, the aspect ratio analysis meter is specially：If A, the current on-line monitoring of one group of arrester of B, C three-phase
Value is respectively a1, b1, c1, and the on-line monitoring value before one week is respectively a2, b2, c2, when a2/ (b2+c2) and a1/ (b1+c1) is inclined
When difference exceedes positive and negative 30%, aspect ratio analysis result is abnormal.
2. the arrester evaluation of running status method according to claim 1 based on big data, it is characterised in that institute
Significant difference analysis meter is stated 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.
Further, the alternate uneven analysis meter is specially：If A, one group of arrester equipment leakage electricity of B, C three-phase
Flow current on-line monitoring value difference a3, b3, c3, three-phase Leakage Current on-line monitoring average value be d=（a3+b3+c3）/ 3, then keep away
Thunder device Leakage Current three-phase imbalance COEFFICIENT K=max (| a3-d |, | b3-d |, | c3-d |)/d, if K>5%, arrester Leakage Current
Alternate uneven analysis result is abnormal.
Further, the online monitoring data calculates with test data variance analysis is specially：Arrester equipment is any
Currently on-line monitoring value is a4 to phase Leakage Current, and the mutually the last Leakage Current off-line testing data are a5, then monitor on-line
Data and test data deviation are p=(a4-a5)/a5, if p>50%, online monitoring data and test data variance analysis result
For exception.
Further, the data influence factor includes：Floor installation mode, ambient temperature and humidity, filthy situation, salt dirt feelings
Condition, testing current in resistance property method, the interference of electromagnetic field, live meter quality.
Further, when humidity is more than 75% in environmental data, current arrester Monitoring Data is invalid data.
Compared with prior art, the present invention has beneficial effect：
Analyzed by comparing, be effectively extracted arrester running status characteristic quantity；
Analysis corrections are carried out to the influence factor of the running state data of arrester, improve arrester running state data can
By property；
Gathered using multi-source data, streaming computing framework, internal memory Computational frame, comparing analysis, equipment state evaluation etc. work(
Can, the acquisition of arrester running status amount is realized, and data are uploaded and stored to platform, realize based on multidimensional total state amount
Arrester running status analysis, improve the accuracy of arrester running state analysis.
Brief description of the drawings
Fig. 1 is the arrester evaluation of running status method flow schematic diagram of the invention based on big data.
Embodiment
Below in conjunction with the accompanying drawings and embodiment the present invention will be further described.
As shown in figure 1, a kind of arrester evaluation of running status method based on big data, comprises 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.
Data collection layer is the multidimensional total state perception parameter that system obtains equipment by data acquisition components from data source,
Its data acquisition components obtains the perception parameter of reflection equipment health status using the multi-thread mechanism based on multitask in real time,
In terms of data model, specification is built using IEC61970, as the universal model of Various types of data storage, to meet various kinds of equipment number
According to the needs of tissue.
Data storage layer is stored as E formatted files, CIM formatted files to each data parameter after processing, stores and arrive relation
In type database and non-relational database.
Data Analysis Platform layer is also by data factory, data mining, Data Analysis Model, word cloud analysis model to data
The Various types of data of accumulation layer carries out analysis calculating.
In an embodiment of the present invention, basic account data includes device name, model name, date of putting into operation, voltage etc.
Level, insulator types, continuous running voltage, power frequency reference voltage, power-frequency sparkover voltage, nominal discharge current, lightning impulse are residual
Pressure and retting-flax wastewater.
In an embodiment of the present invention, arrester online monitoring data includes Leakage Current, current in resistance property and capacity current.
Each arrester on-line monitoring hour historical data, humidity data in nearest one month are extracted daily, can be calculated according to data fusion
Method calculates monitoring parameters a reference value, then in conjunction with alarm threshold, ambient temperature and humidity, calculates the alarm shape of each monitoring parameters
State.
In an embodiment of the present invention, aspect ratio analysis meter is specially：If A, one group of arrester of B, C three-phase it is current
Line monitor value is respectively a1, b1, c1, and the on-line monitoring value before one week is respectively a2, b2, c2, as a2/ (b2+c2) and a1/ (b1
+ c1) for deviation when exceeding positive and negative 30%, aspect ratio analysis result is abnormal.
Wherein aspect ratio analysis calculating is included in line number evidence and test data, and computational methods are identical, if A, B, C three-phase are same
One is spaced similarly hereinafter voltage class same category of device, and a2/ (b2+c2+O2) and a1/ (b1+c1+ are contrasted when being calculated if having neutral point
O1 deviation).
In an embodiment of the present invention, significant difference analysis meter specifically includes：If the online monitoring data of arrester one
The average value of nearly 2 days is X, and Monitoring Data sample standard deviation is S, and the currently monitored value of arrester is x, then significant difference
For：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.
When there is n>During=5 family's equipment, k values are chosen according to n size, are shown in Table 1.
Table 1
In an embodiment of the present invention, alternate uneven analysis meter is specially：If A, one group of arrester equipment leakage of B, C three-phase
Electric current 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 COEFFICIENT K=max (| a3-d |, | b3-d |, | c3-d |)/d, if K>5%, arrester leakage electricity
Alternate uneven analysis result is flowed for exception.
In an embodiment of the present invention, online monitoring data calculates with test data variance analysis is specially：Arrester is set
Currently on-line monitoring value is a4 to standby any phase Leakage Current, and the mutually the last Leakage Current off-line testing data are a5, then exist
Line Monitoring Data and test data deviation are p=(a4-a5)/a5, if p>50%, online monitoring data and test data deviation are divided
Result is analysed as exception.
In an embodiment of the present invention, data influence factor includes：Floor installation mode, ambient temperature and humidity, filthy situation,
Salt dirt situation, testing current in resistance property method, the interference of electromagnetic field, live meter quality.
In an embodiment of the present invention, when humidity is more than 75% in environmental data, current arrester Monitoring Data is invalid
Data.
In an embodiment of the present invention, extracting effective arrester quantity of state includes online monitoring data, outer thermal image detection number
According to, high frequency partial discharge examination data, operating follow current data, corrosion situation, bulk temperature rising data, evaluation criterion is such as
Shown in table 2.
Table 2
Although the present invention is disclosed as above with preferred embodiment, it is not for limiting the present invention, any art technology
Personnel without departing from the spirit and scope of the present invention, may be by the methods and technical content of the disclosure above to skill of the present invention
Art scheme makes possible variation and modification, therefore, every content without departing from technical solution of the present invention, the skill according to the present invention
Art essence belongs to the guarantor of technical solution of the present invention to any simple modifications, equivalents, and modifications made for any of the above embodiments
Protect scope.The foregoing is only presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification, it should all belong to the covering scope of the present invention.
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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| 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 |
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| 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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| EP3747100B1 (en) * | 2018-01-30 | 2022-03-16 | Hitachi Energy Switzerland AG | Surge arrestor dimensioning in a dc power transmission system |
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| 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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