CN115018100A - Operation maintenance and overhaul decision method based on health state of power transformation equipment - Google Patents

Operation maintenance and overhaul decision method based on health state of power transformation equipment Download PDF

Info

Publication number
CN115018100A
CN115018100A CN202210718471.0A CN202210718471A CN115018100A CN 115018100 A CN115018100 A CN 115018100A CN 202210718471 A CN202210718471 A CN 202210718471A CN 115018100 A CN115018100 A CN 115018100A
Authority
CN
China
Prior art keywords
power transformation
equipment
transformation equipment
overhaul
calculating
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210718471.0A
Other languages
Chinese (zh)
Inventor
王国彬
施广宇
林一泓
黄巍
佘剑锋
纪锡亮
吴涵
王康
魏登峰
吴达
游浩
卞志文
刘冰
曾静岚
叶兆平
陈晔
许晓林
钟锐
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Electric Power Research Institute of State Grid Fujian Electric Power Co Ltd
State Grid Fujian Electric Power Co Ltd
Original Assignee
Electric Power Research Institute of State Grid Fujian Electric Power Co Ltd
State Grid Fujian Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Electric Power Research Institute of State Grid Fujian Electric Power Co Ltd, State Grid Fujian Electric Power Co Ltd filed Critical Electric Power Research Institute of State Grid Fujian Electric Power Co Ltd
Priority to CN202210718471.0A priority Critical patent/CN115018100A/en
Publication of CN115018100A publication Critical patent/CN115018100A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02B—BOARDS, SUBSTATIONS OR SWITCHING ARRANGEMENTS FOR THE SUPPLY OR DISTRIBUTION OF ELECTRIC POWER
    • H02B3/00—Apparatus specially adapted for the manufacture, assembly, or maintenance of boards or switchgear
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Health & Medical Sciences (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Primary Health Care (AREA)
  • General Health & Medical Sciences (AREA)
  • Water Supply & Treatment (AREA)
  • Public Health (AREA)
  • Educational Administration (AREA)
  • Manufacturing & Machinery (AREA)
  • Power Engineering (AREA)
  • Alarm Systems (AREA)

Abstract

The invention discloses an operation maintenance and overhaul decision method based on the health state of power transformation equipment, which comprises the following steps: acquiring real-time operation state data of the power transformation equipment; calculating the health degree of the power transformation equipment according to the real-time running state data; calculating the predicted failure rate of the power transformation equipment by combining the failure rates of the power transformation equipment in the statistical period; calculating the importance of the power transformation equipment; calculating risk cost by combining the health degree, the predicted failure rate and the importance degree of the power transformation equipment; and determining the overhaul mode of the transformer equipment according to the risk cost result. The health degree, the importance degree and the fault rate of the power transformation equipment are comprehensively considered, the risk cost is calculated, the maintenance decision is made according to the risk cost of the power transformation equipment, the operation risk cost of a power grid is fully considered, and great contribution is made to the operation maintenance decision of the power transformation equipment; the normal state fault rate of the power distribution equipment is calculated, normal state risk analysis of the equipment can be carried out, and the predicted fault rate and the sudden risk are predicted by calculating the accidental fault rate increment in the existing statistical period.

Description

Operation maintenance and overhaul decision method based on health state of power transformation equipment
Technical Field
The invention belongs to the technical field of transformer equipment maintenance, and particularly relates to an operation maintenance decision method based on the health state of transformer equipment.
Background
With the increase of the operation time, the distribution equipment can have the problems that partial parts are damaged in the working process, even the whole equipment is deteriorated to different degrees, and under the background of equipment state monitoring, overhaul or replacement is selected according to the equipment state.
In the prior art, the detection can be only carried out according to the monitored equipment health degree or fault rate, the influence of major repair, minor repair or replacement of the equipment on the operation risk cost of the whole power grid is ignored, and the prediction analysis can not be carried out on the faults of the power distribution equipment.
Disclosure of Invention
The invention aims to provide an operation maintenance and overhaul decision method based on the health state of power transformation equipment, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: an operation maintenance and overhaul decision method based on the health state of power transformation equipment comprises the following steps:
1) acquiring real-time operation state data of the power transformation equipment;
2) calculating the health degree of the power transformation equipment according to the real-time running state data;
3) calculating the predicted failure rate of the power transformation equipment by combining the failure rates of the power transformation equipment in the statistical period;
4) calculating the importance of the power transformation equipment;
5) calculating risk cost by combining the health degree, the predicted failure rate and the importance degree of the power transformation equipment;
6) and determining the maintenance mode of the power transformation equipment according to the risk cost result.
Preferably, the specific step of calculating the real-time health degree of the substation equipment according to the real-time operation state data in step 2) is:
establishing a parameter correlation model of the power transformation equipment, wherein the model comprises the following steps:
Figure BDA0003709584900000021
wherein,
Figure BDA0003709584900000022
and
Figure BDA0003709584900000023
respectively representing the upper limit and the lower limit of the monitoring data, wherein f is a correlation function between the monitoring data; y is a constraint function; x is monitoring data; omega is a monitoring data set, and a plurality of parameters are independent from each other; s i For the allowed interval of the parameter constraint, S i min And S i max Respectively representing the upper and lower boundaries of the allowable interval;
the correlation model of the plurality of monitoring data is:
Figure BDA0003709584900000024
wherein, X n ∈X，n＝1，2，…；
Calculating a normalized value from a fault limit and an alarm limit of a power transformation device
Figure BDA0003709584900000025
And a value exceeding the alarm limit
Figure BDA0003709584900000026
Figure BDA0003709584900000027
Figure BDA0003709584900000031
Wherein A is n max 、A n min Upper and lower alarm limits for substation equipment monitoring data, F n max 、F n min Upper and lower fault limits, V, for substation equipment monitoring data n d Is the desired value of the monitoring parameter required by the device,
Figure BDA0003709584900000032
i.e. the difference between the fault limit and the alarm limit;
calculating the real-time health degree of the monitoring data of the power transformation equipment, wherein the formula is as follows:
Figure BDA0003709584900000033
wherein m is the number of device operations,
Figure BDA0003709584900000034
and
Figure BDA0003709584900000035
is composed of
Figure BDA00037095849000000310
The upper and lower limits of (a) and (b),
Figure BDA0003709584900000036
and
Figure BDA0003709584900000037
is composed of
Figure BDA0003709584900000038
Upper and lower limits of (3).
Preferably, when H n H for 1 hour that the device is healthy n > 2, the equipment fails, when H n Values between 1 and 2 limit values, an alarm condition is present.
Preferably, the failure rate in step 3) includes a normal failure rate and an occasional failure rate increment, and the normal failure rate calculation formula is as follows:
Figure BDA0003709584900000039
wherein, i is 1-m, m is the classification number of the power transformation equipment, N is the total number of the power transformation equipment, N is i The number of the fault transformer equipment in a certain classification is counted;
the formula for calculating the increment of the accidental fault rate is as follows:
Figure BDA0003709584900000041
wherein, F S For counting the ratio of the number of faulty devices to the total number of faulty devices in severe weather conditions within a period, W S The ratio of the duration of severe weather to the statistical time in the statistical period;
and calculating the predicted failure rate according to the counted accidental failure rate increment, wherein the predicted failure rate is as follows:
Figure BDA0003709584900000042
wherein, W e To predict the duration of inclement weather within a statistical period, W T The statistical time is obtained.
Preferably, the calculation formula of the importance of the substation equipment in the step 4) is as follows:
Figure BDA0003709584900000043
wherein M is z (E) For the z-th level of influence factor, E is the influence factor, ω z (E) And y is the total number of the importance influence factors.
Preferably, the calculation formula of the risk cost in the step 5) is as follows:
R＝K·I·P·H n
wherein K is a proportionality coefficient.
Preferably, the overhaul mode comprises minor overhaul, major overhaul and replacement, the minor overhaul is local overhaul, the major overhaul is global daily overhaul, and the replacement is to replace the equipment.
The invention has the technical effects and advantages that: according to the operation maintenance and overhaul decision method based on the health state of the power transformation equipment, the health degree, the importance degree and the fault rate of the power transformation equipment are comprehensively considered, the risk cost is calculated, overhaul workers carry out overhaul decisions through the risk cost of the power transformation equipment, the operation risk cost of a power grid is fully considered, and great contribution is made to the operation maintenance and overhaul decisions of the power transformation equipment;
the normal state fault rate of the power distribution equipment is calculated, normal state risk analysis of the equipment can be carried out, and the sudden risk can be predicted by calculating and predicting the fault rate through the accidental fault rate increment in the existing statistical period.
Drawings
FIG. 1 is a schematic structural diagram of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention provides an operation maintenance and overhaul decision method based on the health state of power transformation equipment, which comprises the following steps of:
the method comprises the following steps: acquiring real-time operation state data of the power transformation equipment;
step two: calculating the health degree of the power transformation equipment according to the real-time operation state data;
the method comprises the following specific steps:
establishing a parameter correlation model of the power transformation equipment, wherein the model comprises the following steps:
Figure BDA0003709584900000051
wherein,
Figure BDA0003709584900000052
and
Figure BDA0003709584900000053
respectively representing the upper limit and the lower limit of the monitoring data, wherein f is a correlation function between the monitoring data; y is a constraint function; x is monitoring data; omega is a monitoring data set, and a plurality of parameters are independent from each other; s i For the allowed interval of the parameter constraint, S i min And S i max Respectively representing the upper and lower boundaries of the allowable interval;
the correlation model of the plurality of monitoring data is:
Figure BDA0003709584900000061
wherein X n Representing any one of the monitored data, X n ∈X，n＝1，2，…；
Calculating a normalized value from a fault limit and an alarm limit of a power transformation device
Figure BDA0003709584900000062
And a value exceeding the alarm limit
Figure BDA0003709584900000063
Figure BDA0003709584900000064
Figure BDA0003709584900000065
Wherein A is n max 、A n min Monitoring data for power transformation equipmentUpper and lower alarm limit values of F n max 、F n min Upper and lower fault limits, V, for substation equipment monitoring data n d Is the desired value of the monitoring parameter required by the device,
Figure BDA0003709584900000066
i.e. the difference between the fault limit and the alarm limit;
calculating the real-time health degree of the monitoring data of the power transformation equipment, wherein the formula is as follows:
Figure BDA0003709584900000071
wherein m is the number of times the apparatus is operated,
Figure BDA0003709584900000072
and
Figure BDA0003709584900000073
is composed of
Figure BDA0003709584900000074
The upper and lower limits of (a) and (b),
Figure BDA0003709584900000075
and
Figure BDA0003709584900000076
is composed of
Figure BDA0003709584900000077
Upper and lower limits of (d);
when H is present n H for 1 hour that the device is healthy n > 2, the equipment fails, when H n Values between 1 and 2 limit values, an alarm condition is present.
Step three: calculating the predicted failure rate of the power transformation equipment by combining the failure rates of the power transformation equipment in the statistical period;
the failure rate comprises a normal failure rate and an accidental failure rate increment, and the normal failure rate calculation formula is as follows:
Figure BDA0003709584900000078
wherein, i is 1-m, m is the classification number of the power transformation equipment, N is the total number of the power transformation equipment, N is i The number of the fault transformer equipment in a certain classification is counted;
the formula for calculating the increment of the accidental fault rate is as follows:
Figure BDA0003709584900000079
wherein, F S For counting the ratio of the number of faulty devices to the total number of faulty devices in severe weather conditions within a period, W S The ratio of the duration of severe weather to the statistical time in the statistical period;
and calculating the predicted failure rate according to the counted accidental failure rate increment, wherein the predicted failure rate is as follows:
Figure BDA00037095849000000710
wherein, W e To predict the duration of inclement weather within a statistical period, W T The statistical time is obtained.
Step four: calculating the importance of the power transformation equipment;
the importance calculation formula is as follows:
Figure BDA0003709584900000081
wherein M is z (E) For the z-th impact factor level, E is a fault event, ω z (E) The weight corresponding to the z-th influence factor, and y is the total number of the importance influence factors;
the impact factors include a load quantity factor, a load level factor, a social impact factor, and an equipment factor.
Step five: calculating risk cost by combining the health degree, the predicted failure rate and the importance degree of the power transformation equipment;
the calculation formula is as follows:
R＝K·I·P·H n
wherein K is a proportionality coefficient.
Step six: determining the maintenance mode of the power transformation equipment according to the risk cost result, visually determining the contribution of different power distribution equipment to the risk according to the risk cost, and determining the priority level of equipment maintenance according to the risk cost when performing maintenance decision; the overhaul mode comprises minor overhaul, major overhaul and replacement, the minor overhaul is local overhaul, the major overhaul is global daily overhaul, and the replacement is to replace equipment.
The operation and maintenance decision method based on the health state of the power transformation equipment is provided for the development of operation and maintenance of the power equipment, the risk cost is used as an operation and maintenance decision basis, the risk cost is fully combined with the health degree, the predicted fault rate and the importance degree of the power transformation equipment, the operation state, the property and the predicted fault rate of the equipment are comprehensively considered, and the health state of the power transformation equipment can be fully evaluated to realize the operation and maintenance decision of the power transformation equipment.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (7)

1. An operation maintenance and overhaul decision method based on the health state of a power transformation device is characterized by comprising the following steps:
1) acquiring real-time operation state data of the power transformation equipment;
2) calculating the health degree of the power transformation equipment according to the real-time running state data;
3) calculating the predicted failure rate of the power transformation equipment by combining the failure rates of the power transformation equipment in the statistical period;
4) calculating the importance of the power transformation equipment;
5) calculating risk cost by combining the health degree, the predicted failure rate and the importance degree of the power transformation equipment;
6) and determining the maintenance mode of the power transformation equipment according to the risk cost result.
2. The operation, maintenance and overhaul decision method based on the health state of the power transformation equipment as claimed in claim 1, wherein: the specific steps of calculating the real-time health degree of the power transformation equipment according to the real-time running state data in the step 2) are as follows:
establishing a parameter correlation model of the power transformation equipment, wherein the model comprises the following steps:
Figure FDA0003709584890000011
wherein,
Figure FDA0003709584890000012
and
Figure FDA0003709584890000013
respectively representing the upper limit and the lower limit of the monitoring data, wherein f is a correlation function between the monitoring data; y is a constraint function; x is monitoring data; omega is a monitoring data set, and a plurality of parameters are mutually independent; s i For the allowed interval of the parameter constraint, S i min And S i max Respectively representing the upper and lower boundaries of the allowable interval;
the correlation model of the plurality of monitoring data is:
Figure FDA0003709584890000021
wherein, X n Representing any one of the monitored data, X n ∈X，n＝1，2，…；
Calculating a normalized value from a fault limit and an alarm limit of a power transformation device
Figure FDA0003709584890000022
And a value exceeding the alarm limit
Figure FDA0003709584890000023
Figure FDA0003709584890000024
Figure FDA0003709584890000025
Wherein A is n max 、A n min Upper and lower alarm limits for substation equipment monitoring data, F n max 、F n min Upper and lower fault limits, V, for substation equipment monitoring data n d Is the desired value of the monitoring parameter required by the device,
Figure FDA0003709584890000026
i.e. the difference between the fault limit and the alarm limit;
calculating the real-time health degree of the monitoring data of the power transformation equipment, wherein the formula is as follows:
Figure FDA0003709584890000027
wherein m is the number of device operations,
Figure FDA0003709584890000031
and
Figure FDA0003709584890000032
is composed of
Figure FDA0003709584890000033
The upper and lower limits of (a) and (b),
Figure FDA0003709584890000034
and
Figure FDA0003709584890000035
is composed of
Figure FDA0003709584890000036
Upper and lower limits of (3).
3. The operation, maintenance and overhaul decision method based on the health state of the power transformation equipment as claimed in claim 2, wherein: when H is present n H for 1 hour that the device is healthy n > 2, the equipment fails, when H n Values between 1 and 2 limit values, an alarm condition is present.
4. The operation, maintenance and overhaul decision method based on the health state of the power transformation equipment as claimed in claim 1, wherein: the failure rate in the step 3) comprises a normal failure rate and an accidental failure rate increment, and the normal failure rate calculation formula is as follows:
Figure FDA0003709584890000037
wherein, i is 1-m, m is the number of classification of the transformer equipment, N is the total number of the transformer equipment, N i The number of the fault transformer equipment in a certain classification is counted;
the formula for calculating the increment of the accidental fault rate is as follows:
Figure FDA0003709584890000038
wherein, F S For counting the ratio of the number of faulty devices to the total number of faulty devices in severe weather conditions within a period, W S The ratio of the duration of severe weather to the statistical time in the statistical period;
and calculating the predicted failure rate according to the counted accidental failure rate increment, wherein the predicted failure rate is as follows:
Figure FDA0003709584890000039
wherein, W e To predict the duration of inclement weather within a statistical period, W T The statistical time is obtained.
5. The operation, maintenance and overhaul decision method based on the health state of the power transformation equipment as claimed in claim 1, wherein: the calculation formula of the importance degree of the substation equipment in the step 4) is as follows:
Figure FDA0003709584890000041
wherein M is z (E) For the z-th level of influence factor, E is the influence factor, ω z (E) The weight corresponding to the z-th influence factor, and y is the total number of the importance influence factors.
6. The operation, maintenance and overhaul decision method based on the health state of the power transformation equipment as claimed in claim 1, wherein: the calculation formula of the risk cost in the step 5) is as follows:
R＝K·I·P·H n
wherein K is a proportionality coefficient.
7. The operation, maintenance and overhaul decision method based on the health state of the power transformation equipment as claimed in claim 1, wherein: the overhaul mode comprises minor overhaul, major overhaul and replacement, the minor overhaul is local overhaul, the major overhaul is global daily overhaul, and the replacement is to replace the equipment.
CN202210718471.0A 2022-06-23 2022-06-23 Operation maintenance and overhaul decision method based on health state of power transformation equipment Pending CN115018100A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210718471.0A CN115018100A (en) 2022-06-23 2022-06-23 Operation maintenance and overhaul decision method based on health state of power transformation equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210718471.0A CN115018100A (en) 2022-06-23 2022-06-23 Operation maintenance and overhaul decision method based on health state of power transformation equipment

Publications (1)

Publication Number Publication Date
CN115018100A true CN115018100A (en) 2022-09-06

Family

ID=83076284

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210718471.0A Pending CN115018100A (en) 2022-06-23 2022-06-23 Operation maintenance and overhaul decision method based on health state of power transformation equipment

Country Status (1)

Country Link
CN (1) CN115018100A (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107516170A (en) * 2017-08-30 2017-12-26 东北大学 A kind of difference self-healing control method based on probability of equipment failure and power networks risk
CN107563536A (en) * 2016-06-30 2018-01-09 中国电力科学研究院 A kind of 10kV distribution transformer Optimal Maintenance methods for considering power networks risk
CN109559043A (en) * 2018-11-30 2019-04-02 天津大学 A kind of power distribution system equipment Decision-making of Condition-based Maintenance method based on risk assessment
CN112215480A (en) * 2020-09-29 2021-01-12 国网江苏省电力有限公司南通供电分公司 Power equipment risk assessment method and device and storage medium
KR20210048844A (en) * 2019-10-24 2021-05-04 한국전력공사 Apparatus and method establishing maintenance plan based on health index of equipment asset

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107563536A (en) * 2016-06-30 2018-01-09 中国电力科学研究院 A kind of 10kV distribution transformer Optimal Maintenance methods for considering power networks risk
CN107516170A (en) * 2017-08-30 2017-12-26 东北大学 A kind of difference self-healing control method based on probability of equipment failure and power networks risk
CN109559043A (en) * 2018-11-30 2019-04-02 天津大学 A kind of power distribution system equipment Decision-making of Condition-based Maintenance method based on risk assessment
KR20210048844A (en) * 2019-10-24 2021-05-04 한국전력공사 Apparatus and method establishing maintenance plan based on health index of equipment asset
CN112215480A (en) * 2020-09-29 2021-01-12 国网江苏省电力有限公司南通供电分公司 Power equipment risk assessment method and device and storage medium

Similar Documents

Publication Publication Date Title
KR101683256B1 (en) Asset management system and method for electric power apparatus
CN112365100B (en) Disaster risk comprehensive assessment-based power grid disaster early warning and response method
CN103793854B (en) The overhead transmission line operation risk informatization evaluation method that Multiple Combination is optimized
CN108320043A (en) A kind of distribution network equipment state diagnosis prediction method based on electric power big data
CN103793859B (en) A kind of wind power plant operation monitoring and event integrated evaluating method
CN102708411A (en) Method for evaluating risk of regional grid on line
CN103400310A (en) Method for evaluating power distribution network electrical equipment state based on historical data trend prediction
CN104462718A (en) Method for evaluating economic operation year range of transformer substation
CN107271829A (en) A kind of controller switching equipment running state analysis method and device
CN105225020A (en) A kind of running status Forecasting Methodology based on BP neural network algorithm and system
CN103699668B (en) Electric distribution network electrical equipment assembled state appraisal procedure based on data section uniformity
CN109510205B (en) Power distribution network load transfer auxiliary decision analysis method
CN106779317A (en) A kind of grid equipment method for evaluating quality
CN110287543B (en) Method for predicting service life of relay protection device
CN103942735A (en) Method for evaluating relay protection states
CN104166788A (en) Overhead transmission line optimal economic life range assessment method
CN104218570A (en) Method and system for online evaluating overall measuring errors of electric energy measuring device
CN113052473B (en) Power grid risk analysis method based on fault rate and static safety analysis
CN102737287A (en) Regional power grid on-line power supply risk assessment system
CN104021304A (en) Installation priority level evaluation method for on-line monitoring devices of transformers
CN107895068A (en) GIS gas-insulated state evaluating methods based on variable weight and combination membership function
CN107563620A (en) A kind of integrated evaluating method based on equipment life-cycle information
CN107657391A (en) A kind of dispatching of power netwoks monitoring defect management and assessment system
Feng et al. Optimization method with prediction-based maintenance strategy for traction power supply equipment based on risk quantification
CN103440410A (en) Main variable individual defect probability forecasting method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination