CN104933482B - The electric power apparatus examination optimization method to be retracted based on the fuzzy enlistment age - Google Patents
The electric power apparatus examination optimization method to be retracted based on the fuzzy enlistment age Download PDFInfo
- Publication number
- CN104933482B CN104933482B CN201510334913.1A CN201510334913A CN104933482B CN 104933482 B CN104933482 B CN 104933482B CN 201510334913 A CN201510334913 A CN 201510334913A CN 104933482 B CN104933482 B CN 104933482B
- Authority
- CN
- China
- Prior art keywords
- overhaul
- fuzzy
- maintenance
- equipment
- cost
- 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.)
- Active
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 43
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000012423 maintenance Methods 0.000 claims abstract description 93
- 238000005315 distribution function Methods 0.000 claims abstract description 27
- 230000008439 repair process Effects 0.000 claims abstract description 8
- 230000009467 reduction Effects 0.000 claims description 8
- 238000013024 troubleshooting Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 5
- 125000004122 cyclic group Chemical group 0.000 claims description 5
- 230000005611 electricity Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000004064 recycling Methods 0.000 claims description 3
- 239000013598 vector Substances 0.000 claims description 3
- 238000002595 magnetic resonance imaging Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 10
- 238000009826 distribution Methods 0.000 description 5
- 238000007726 management method Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
Classifications
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
-
- 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
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention provides a kind of electric power apparatus examination optimization methods to be retracted based on the fuzzy enlistment age, the uncertainty of effect is promoted to equipment failure rate according to maintenance activity, fuzzy fitting is carried out to failure rate in device history data, it proposes to obscure enlistment age rollback failure rate prediction model, and consider reliability and economy, establish elastic stage maintenance decision Optimized model.The failure rate distribution function of equipment after maintenance is calculated by using fuzzy enlistment age rollback model, and the solving-optimizing of electric power apparatus examination time is carried out with elastic stage maintenance decision Optimized model.Wherein, constraints failure rate limit value being most worth as equipment failure rate distribution function after maintenance so that the overall life cycle cost of equipment is more economic.This method has fully considered ambiguity and randomness of the maintenance to single transformer equipment failure probability and service life, contributes to power grid enterprises' reasonable arrangement repair schedule, reliably extends service life of equipment.
Description
Technical Field
The invention relates to the technical field of maintenance of electric power equipment, in particular to a maintenance optimization method of electric power equipment based on fuzzy service life regression.
Background
The power equipment is an important material guarantee for maintaining and improving the production capacity of power supply enterprises, the normal operation of a power system is often damaged once the power equipment fails, huge economic loss is caused, and the maintenance and repair work of the equipment is reasonably arranged to play an important role in ensuring the health level of the equipment, maintaining the safe and stable operation of a power grid and guaranteeing national economy.
The management concept and method of the Life Cycle Cost (LCC) are becoming the practical choice of the international power development, and the operation maintenance Cost and the fault Cost of the power grid equipment have a large proportion in the Life cycle Cost, and how to arrange the operation maintenance and overhaul work of the equipment on the basis of coordinating the relationship between economy and reliability is one of the important links for promoting the Life cycle management of the assets, so that the optimization of the overhaul plan of the power equipment is necessary.
The method for optimizing the maintenance plan of the power equipment mainly comprises the following steps: by a cost estimation method, assuming that equipment is repaired as new after each fault, a cost model under regular maintenance and state maintenance is constructed; discussing a maintenance strategy at a certain operation moment, and optimizing the maintenance strategy by comparing the influence of the transformer non-maintenance mode, the transformer major maintenance mode, the transformer minor maintenance mode and the transformer replacement mode on the whole life cycle cost; optimal maintenance strategies in two regular maintenance modes; and performing decision optimization on the basis of a regular maintenance strategy and a state maintenance strategy, performing state maintenance when a set constraint condition exceeds a certain threshold value, and the like. There are several problems with these methods: the influence of maintenance activities on the reliability is not considered, the established model is not perfect, the maintenance strategy is not optimized from the perspective of the whole life cycle, the requirement on the equipment reliability is not considered, and the real optimal maintenance strategy is difficult to obtain. Thus, the existing methods do not solve this problem well.
Disclosure of Invention
The invention aims to provide a power equipment maintenance optimization method based on fuzzy service life regression so as to solve the problem that a real optimal maintenance strategy is difficult to obtain in the existing method for optimizing the maintenance plan of the power equipment.
In order to achieve the above object, the present invention provides a power equipment overhaul optimization method based on fuzzy service life reduction, comprising the following steps:
s1: carrying out fuzzy fitting on the fault rate of the power equipment according to the historical fault rate data of the power equipment, and obtaining a fitting result as follows:
the random fuzzy distribution function of the equipment failure rate Y (t) is as follows:
wherein,the median failure rate curve is obtained by taking the average failure rate curve,to be a failure rate right deviation value curve,a fault rate left deviation value curve is obtained;
s2: establishing a fuzzy service life rollback fault rate prediction model of the electric power equipment by combining the fitting result, and obtaining the equivalent service age of the electric power equipment after maintenance according to the maintenance time sequenceAnd failure of the electrical equipment after kth overhaulCenter value λ' (t) of rate curve triangular blur numberk) And a right deviation value c'1(tk) And left bias value c'2(tk)，
Wherein,is according to t'kCalculating a failure rate fuzzy number expected value of a backspacing point;
s3: and estimating the maintenance cost and the fault cost, and constructing an elastic state maintenance decision optimization model according to the estimated maintenance cost and the estimated fault cost, wherein the model takes the fault rate limit value as the constraint condition of the maximum value of the distribution function of the equipment fault rate after maintenance, and the model comprises the following steps:
s.t.λmin≤λ'(t)≤λmax
in the formula: using decision vectorsRepresenting the time of each overhaul;the total maintenance cost is calculated;to total failure cost;is a fuzzy estimation value of single overhaul cost;is a fuzzy estimate of the cost of a single failure; t is tkThe service age of the equipment at the kth overhaul; n is the total maintenance times in the service life cycle of the equipment; t isAThe service life of the equipment; r isdTo take account of the inflation rate and interest of the currencyDiscount rate after factors such as rate and exchange rate; λ' (t) is a fault rate distribution function of the repaired equipment; lambda [ alpha ]minIs the lower limit of the failure rate; lambda [ alpha ]maxIs the upper limit of the failure rate.
S4: and calculating a fault rate distribution function of the repaired equipment according to the fuzzy service life rollback fault rate prediction model of the electric power equipment, and then performing solving optimization on the overhaul time of the electric power equipment by using the elastic state overhaul decision optimization model of the step S3.
Preferably, the process of fuzzy fitting in step S1 specifically includes: the fuzzy fitting function is adopted to represent the fault rate of the banded power equipment in the historical data, and the random fuzzy distribution function of the fault rate Y (t) of the equipment is represented as follows:
thereby obtaining the right boundary value curve of the failure rateAnd the left boundary value curve of the failure rate
Preferably, said fuzzy service-life fallback fault rate prediction model is in particular: \ u
Correcting the equivalent age of service by fuzzy evaluation for each time of overhaulFirstly, the service age of the power equipment is returned to t 'after the kth overhaul'kAt a moment in time, wherein,α is a work age reduction factor;
secondly, according to t'kCalculating expected value of fault rate fuzzy number of backspacing pointComprises the following steps:wherein,the median failure rate curve is obtained by taking the average failure rate curve,is a curve of the right boundary value of the failure rate,a fault rate left boundary value curve is obtained;
finally, according to the expected valueMedian curve in failure rateUpper lookup corresponding run time ofAnd will teq,kThe service life of the power equipment after the kth overhaul is taken as the equivalent service age of the power equipment; simultaneously obtaining the central value of the triangular fuzzy number of the fault rate curve of the power equipment after the kth overhaul asA right deviation value ofLeft deviation value of
Preferably, the cost of repair estimate is specifically: and (3) according to statistical data, expressing fuzzy estimation values of single overhaul cost by triangular fuzzy numbers:wherein, CMLIs the lower limit of the single overhaul cost statistic, CMRIs the upper limit of the single overhaul cost statistic, CMThe value with the highest possibility in the single overhaul cost statistics is obtained;
and obtaining expected values of the overhaul cost as:
the fault cost estimation specifically comprises: cost of single failure of power equipment CFThe system consists of a troubleshooting fee and a failure loss fee:wherein, CFM,kFor a cost of k in a single troubleshooting session, PlossFor power failure and load loss, T is the single power failure duration, and p is the price difference of electricity purchased and sold;
and then according to the statistical data, obtaining a fuzzy estimation value of the single fault cost:wherein, CFLIs the lower limit of the single failure cost statistic, CFRIs the upper limit of the single failure cost statistic, CFThe value with the highest possibility in the single failure cost statistics is obtained;
and, obtaining the expected value of the single-failure cost as:
preferably, in step S3, the after-repair fault rate distribution function λ' (t) is obtained by:
is provided withThe corresponding repair time sequence is T1,T2,…,TnExamination ofThe repaired failure rate distribution function λ' (t) is expressed as:
preferably, when the solving optimization is executed in the step S3, the method specifically includes: after each state maintenance, the next maintenance year is set in the allowed limited range by combining the change of the equipment fault rate curve, the upper fault rate limit and the lower fault rate limit, the service age of the equipment is set in the allowed limited range, and the optimal solution of the maintenance time of the power equipment is obtained through the limited recycling solving operation.
Preferably, the solving step of step S4 is as follows:
after the kth overhaul, when the failure rate is reduced to the equivalent service age teq,kWhen the corresponding failure rate is limited by the limit value of the failure rate, the next maintenance year Tk+1Within a limited range:
service life T of equipmentACounting sequentially from the first maintenance time to obtain the next possible maintenance time under the time constraint of the maintenance year when the maintenance time is limited; obtaining a plurality of feasible sequences of overhaul times by means of an embedded loop operation, the maximum loop weight being set to TASecondly;
then for each time sequence of overhaulCalculating to obtain a fault rate distribution function lambda' (t) after maintenance, and obtaining a function value according to an elastic state maintenance decision optimization model
And (4) solving the optimal value of the elastic state overhaul decision optimization model in all feasible solutions through cyclic solving operation.
Because the overhaul work of the equipment can improve the performance of the equipment, the invention carries out fuzzy fitting on the fault rate in the historical data of the equipment according to the uncertainty of the overhaul activity on the fault rate improvement effect of the electric power equipment, and proposes a fuzzy service life rollback fault rate prediction model for quantifying the repair effect of the overhaul work on the performance of the equipment. On the basis of analyzing the life cycle cost of equipment, reliability and economy are comprehensively considered, and an elastic state overhaul decision optimization model based on fuzzy service life regression is proposed. The method comprises the steps of calculating a fault rate distribution function of the equipment after maintenance by adopting a fuzzy service life regression model, and solving and optimizing the maintenance type and time of the electric power equipment by using an elastic state maintenance decision optimization model. The fault rate limit value is used as a constraint condition of the maximum value of the fault rate distribution function of the repaired equipment, so that the whole life cycle cost of the equipment is more economic. The method fully considers the failure probability of maintenance activities to single power transformation equipment and the fuzziness and randomness of service life, comprehensively considers the reliability and the economical efficiency on the basis of analyzing the whole life cycle cost of the equipment, and provides a state overhaul optimization method based on fuzzy service life regression.
Drawings
FIG. 1 is a flow chart of a power equipment overhaul optimization method based on fuzzy service life reduction provided by the present invention;
FIG. 2 is a fault rate distribution curve after fuzzy fitting according to historical transformer fault rate data in accordance with a preferred embodiment of the present invention;
fig. 3 is a failure rate distribution curve under the optimal solution obtained by the preferred embodiment of the present invention.
Detailed Description
To better illustrate the present invention, a preferred embodiment is described in detail with reference to the accompanying drawings, in which:
as shown in fig. 1, the power equipment overhaul optimization method based on fuzzy service life reduction provided by the present embodiment includes the following steps:
s1: carrying out fuzzy fitting on the fault rate of the power equipment according to the historical fault rate data of the power equipment to obtain a fitting result;
s2: establishing a fuzzy service life regression model of the power equipment by combining the fitting result, and obtaining the equivalent service age of the overhauled equipment and the change of the central value and the deviation value of the triangular fuzzy number of the fault rate curve of the power equipment after the kth overhaul according to the overhaul time sequence;
s3: carrying out maintenance cost estimation and fault cost estimation, and constructing an elastic state maintenance decision optimization model according to the maintenance cost estimation and the fault cost estimation;
s4: and calculating a fault rate distribution function of the repaired equipment according to the fuzzy service life rollback fault rate prediction model of the electric power equipment, and then performing solving optimization on the overhaul time of the electric power equipment by using the elastic state overhaul decision optimization model of the step S3.
Specifically, the present embodiment takes transformer maintenance as an example to describe in detail:
for a 110kV, 50MVA duplex winding oil-immersed transformer, assuming an operating life of 30 years, an optimal overhaul strategy is determined by applying a transformer overhaul optimization method based on fuzzy service life reduction, as follows:
s1: and carrying out fuzzy fitting according to the historical data of the fault rate of the transformer. The fuzzy fitting specifically comprises: the fuzzy fitting function is adopted to represent the banded equipment failure rate in the historical data, and the random fuzzy distribution function of the equipment failure rate Y (t) is represented as follows:
wherein, among others,the median failure rate curve is obtained by taking the average failure rate curve,is a curve of the right boundary value of the failure rate,the left boundary value curve of the failure rate. The fuzzy distribution curve of the fault rate after fuzzy fitting in the embodiment is shown in fig. 2, and a fault rate central value curve of the fitting result is obtainedRight boundary value curveLeft boundary value curve
S2: and establishing a fuzzy service life regression fault rate prediction model of the power equipment by combining the fitting result. The fuzzy service life rollback fault rate prediction model is as follows:
time series for overhaulFirst, the working age of the electrical power plant is returned to t 'after the k-th overhaul'kThe time of day. For the first overhaul, backing off a time point t'1＝(1-α)T1α is a work-age reduction factor, p k 2,3, …, n, t'k＝teq,k-1+(1-α)(Tk-Tk-1) Associated with the first few service activities.
Secondly, according to t'kCalculating expected values of the fault rate fuzzy numbers of the backspacing points:wherein,the median failure rate curve is obtained by taking the average failure rate curve,is a curve of the right boundary value of the failure rate,the left boundary value curve of the failure rate.
Finally, according to the expected valueMedian curve in failure rateOn-lookup corresponding runtimeNamely the equivalent service age t of the power equipment after the kth overhauleq,k. And obtaining the central value of the triangular fuzzy number of the fault rate curve of the power equipment after the kth overhaul asRight deviation valueLeft deviation value
S3: and estimating the maintenance cost and the fault cost.
The evaluation of the overhaul cost specifically comprises the following steps: and (3) according to statistical data, expressing fuzzy estimation values of single overhaul cost by triangular fuzzy numbers:wherein, CMLLower limit equal to single overhaul cost statistic, CMREqual to the upper limit of the single overhaul cost statistic, CMThe most probable value in the single overhaul cost statistics.
And calculating to obtain an expected value of the overhaul cost as follows:
the fault cost estimation specifically comprises the following steps: cost of single fault of equipment and substation CFConsists of a trouble-shooting fee and a trouble-loss fee, and then:
in the formula: cFM,kFor a cost of k in a single troubleshooting session, PlossFor power failure and load loss, T is the time length of single power failure, and p is the price difference of electricity purchased and sold.
And then according to the statistical data, obtaining a fuzzy estimation value of the single fault cost:wherein, CFLLower bound, C, equal to single failure cost statisticFREqual to the upper limit of the single failure cost statistic, CFThe most probable value in the single-failure cost statistics.
And, obtaining the expected value of the single-failure cost as:
in this embodiment, the estimated value of the single-state overhaul cost is obtained according to the statistical dataIs [8.1,8,9.9 ]]Ten thousand yuan, one-time failure cost estimateIs [144,160,176 ]]Ten thousand yuan.
Constructing an elastic state maintenance decision optimization model according to maintenance cost estimation and fault cost estimation, wherein the model takes the fault rate as the constraint condition of the maximum value of the distribution function of the fault rate of the maintained equipment, and the model is as follows:
s.t.λmin≤λ'(t)≤λmax
in the formula: using decision vectorsRepresenting the time of each overhaul;the total maintenance cost is calculated;to total failure cost;is a fuzzy estimation value of single overhaul cost;is a fuzzy estimate of the cost of a single failure; t is tkThe age of the equipment in service at the kth overhaul; n is the total maintenance times in the service life cycle of the equipment; t isAThe service life of the equipment; r isdThe discount rate is obtained by considering the factors such as the inflation rate, interest rate, exchange rate and the like of the currency; λ' (t) is a distribution function of the failure rate of the equipment after maintenance; lambda [ alpha ]minIs the lower limit of the failure rate; lambda [ alpha ]maxIs the upper limit of the failure rate.
When the solution optimization is executed through the optimization model, the method specifically comprises the following steps: after each state maintenance, the next maintenance year is set in the allowed limited range and the service time of the equipment is set in the allowed limited range by combining the change of the fault rate curve and the upper and lower limits of the fault rate, and the optimal solution of the maintenance type and time of the transformer can be obtained through the limited recycling solving operation.
S4: calculating a fault rate distribution function of the post-overhaul equipment from the fuzzy service life fallback fault rate prediction model of the electrical equipment:
after the kth overhaul, when the failure rate is reduced to the equivalent service age teq,kWhen the corresponding failure rate is limited by the limit value of the failure rate, the next maintenance year Tk+1Within a limited range:
service life T of equipmentACounting sequentially from the first maintenance time to obtain the next possible maintenance time under the time constraint of the maintenance year when the maintenance time is limited; obtaining a sequence of a plurality of feasible repair times by cyclic operation, the maximum cyclic weight being set to TASecondly; then for each time sequence of overhaulAnd calculating to obtain a fault rate distribution function lambda' (t) after maintenance.
Then, the elastic state maintenance decision optimization model of the step S3 is used for obtaining a function valueAnd (4) solving the optimal value of the elastic state overhaul decision optimization model in all feasible solutions through cyclic solving operation.
In the embodiment, a state overhaul service life rollback factor α is set to be 0.4, a fuzzy service life rollback fault rate prediction model is adopted to calculate a fault rate distribution function of the overhauled equipment, and an elastic state overhaul model is used for decision optimization to obtain the fault rate distribution functionThe optimal solution isI.e. the condition overhaul was performed in 12 th, 20 th and 26 th years, respectively, and fig. 4 shows the fault rate distribution curve under the corresponding overhaul strategy. Corresponding optimal total cost of service and failureTen thousand yuan, the expected value of the total cost of overhaul and failure is: 70.694 ten thousand yuan.
The method of the invention considers the uncertainty of repair effect of overhaul activities on power equipment such as transformers and the like, proposes a fuzzy service life rollback method, and performs prediction analysis on the failure rate of the equipment by combining a fuzzy theory. Based on the prediction of the equipment reliability change trend, an elastic state maintenance method is provided, the maintenance period is more flexible, and the established power equipment maintenance decision optimization model improves the reliability and the economy.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to make modifications or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (7)
1. A power equipment overhaul optimization method based on fuzzy service life reduction is characterized by comprising the following steps:
s1: carrying out fuzzy fitting on the fault rate of the power equipment according to the historical fault rate data of the power equipment, and obtaining a fitting result as follows:
the random fuzzy distribution function of the equipment failure rate Y (t) is as follows:
wherein,the median failure rate curve is obtained by taking the average failure rate curve,to be a failure rate right deviation value curve,a fault rate left deviation value curve is obtained;
s2: establishing a fuzzy service life rollback fault rate prediction model of the electric power equipment by combining the fitting result, and obtaining the equivalent service age of the electric power equipment after maintenance according to the maintenance time sequenceAnd the central value lambda' (t) of the triangular fuzzy number of the fault rate curve of the power equipment after the kth overhaulk) And a right deviation value c'1(tk) And left bias value c'2(tk)，
Wherein,is according to t'kCalculating a failure rate fuzzy number expected value of a backspacing point;
s3: and estimating the maintenance cost and the fault cost, and constructing an elastic state maintenance decision optimization model according to the estimated maintenance cost and the estimated fault cost, wherein the model takes the fault rate limit value as the constraint condition of the maximum value of the distribution function of the equipment fault rate after maintenance, and the model comprises the following steps:
wherein the decision vector is usedRepresenting the time of each overhaul;the total maintenance cost is calculated;to total failure cost;is a fuzzy estimation value of single overhaul cost;is a fuzzy estimate of the cost of a single failure; t is tkThe service age of the equipment at the kth overhaul; n is the total maintenance times in the service life cycle of the equipment; t isAThe service life of the equipment; r isdThe discount rate after the factors of the inflation rate, interest rate and exchange rate of the currency are considered; λ' (t) is a fault rate distribution function of the repaired equipment; lambda [ alpha ]minIs the lower limit of the failure rate; lambda [ alpha ]maxIs the upper limit of the failure rate;
s4: and calculating a fault rate distribution function of the repaired equipment according to the fuzzy service life rollback fault rate prediction model of the electric power equipment, and then performing solving optimization on the overhaul time of the electric power equipment by using the elastic state overhaul decision optimization model of the step S3.
2. Method for the overhaul optimization of electric power equipments based on fuzzy service-age regression according to claim 1, characterized in that the procedure of fuzzy fitting in step S1 is in particular: the fuzzy fitting function is adopted to represent the fault rate of the banded power equipment in the historical data, and the random fuzzy distribution function of the fault rate Y (t) of the equipment is represented as follows:
thereby obtaining the right boundary value curve of the failure rateAnd failure rate left boundary curveThread
3. Fuzzy service life-back based power equipment overhaul optimization method according to claim 1, wherein said fuzzy service life-back failure rate prediction model is in particular:
correcting the equivalent age of service by fuzzy evaluation for each time of overhaulFirstly, the service age of the power equipment is returned to t 'after the kth overhaul'kAt a moment in time, wherein,α is a work age reduction factor;
secondly, according to t'kCalculating the expected value of the fault rate fuzzy number of the backspacing point as follows:wherein,the median failure rate curve is obtained by taking the average failure rate curve,is a curve of the right boundary value of the failure rate,a fault rate left boundary value curve is obtained;
finally, according to the expected valueMedian curve in failure rateUpper lookup corresponding run time ofAnd will teq,kThe service life of the power equipment after the kth overhaul is taken as the equivalent service age of the power equipment; simultaneously obtaining the central value of the triangular fuzzy number of the fault rate curve of the power equipment after the kth overhaul asA right deviation value ofLeft deviation value of
4. Method for the overhaul optimization of electric power equipments based on fuzzy service-age retrofits according to claim 1,
the overhaul cost estimation specifically comprises: and (3) according to statistical data, expressing fuzzy estimation values of single overhaul cost by triangular fuzzy numbers:wherein, CMLIs the lower limit of the single overhaul cost statistic, CMRIs the upper limit of the single overhaul cost statistic, CMThe value with the highest possibility in the single overhaul cost statistics is obtained;
and obtaining expected values of the overhaul cost as:
the fault cost estimation specifically comprises: cost of single failure of power equipment CFThe system consists of a troubleshooting fee and a failure loss fee:wherein, CFM,kFor a cost of k in a single troubleshooting session, PlossFor power failure and load loss, T is the single power failure duration, and p is the price difference of electricity purchased and sold;
and then according to the statistical data, obtaining a fuzzy estimation value of the single fault cost:wherein, CFLIs the lower limit of the single failure cost statistic, CFRIs the upper limit of the single failure cost statistic, CFThe value with the highest possibility in the single failure cost statistics is obtained;
and, obtaining the expected value of the single-failure cost as:
5. method for the overhaul optimization of electric power plants based on fuzzy service-age regression according to claim 1 or 3, characterized in that in said step S3, the post-overhaul failure rate distribution function λ' (t) is obtained by:
is provided withThe corresponding repair time sequence is T1,T2,…,TnThe after-overhaul fault rate distribution function λ' (t) is expressed as:
6. method for the overhaul optimization of electrical equipments based on fuzzy service life regression, according to claim 1, characterized in that said step S3, when performing the solution optimization, is in particular: after each state maintenance, the next maintenance year is set in the allowed limited range by combining the change of the equipment fault rate curve, the upper fault rate limit and the lower fault rate limit, the service age of the equipment is set in the allowed limited range, and the optimal solution of the maintenance time of the power equipment is obtained through the limited recycling solving operation.
7. Method for the overhaul optimization of electric power equipments based on fuzzy service-age regression according to claim 1, characterized in that said step S4 solves the steps of:
after the kth overhaul, when the failure rate is reduced to the equivalent service age teq,kWhen the corresponding failure rate is limited by the limit value of the failure rate, the next maintenance year Tk+1Within a limited range:
service life T of equipmentACounting sequentially from the first maintenance time to obtain the next possible maintenance time under the time constraint of the maintenance year when the maintenance time is limited; obtaining a plurality of feasible sequences of overhaul times by means of an embedded loop operation, the maximum loop weight being set to TASecondly;
then for each time sequence of overhaulCalculating to obtain a fault rate distribution function lambda' (t) after maintenance, and obtaining a function value according to an elastic state maintenance decision optimization model
And (4) solving the optimal value of the elastic state overhaul decision optimization model in all feasible solutions through cyclic solving operation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510334913.1A CN104933482B (en) | 2015-06-16 | 2015-06-16 | The electric power apparatus examination optimization method to be retracted based on the fuzzy enlistment age |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510334913.1A CN104933482B (en) | 2015-06-16 | 2015-06-16 | The electric power apparatus examination optimization method to be retracted based on the fuzzy enlistment age |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104933482A CN104933482A (en) | 2015-09-23 |
| CN104933482B true CN104933482B (en) | 2018-08-10 |
Family
ID=54120642
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510334913.1A Active CN104933482B (en) | 2015-06-16 | 2015-06-16 | The electric power apparatus examination optimization method to be retracted based on the fuzzy enlistment age |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104933482B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105868830B (en) * | 2016-03-23 | 2020-12-22 | 国家电网公司 | Method for determining maintenance sequence of multiple transformers |
| CN105956727A (en) * | 2016-04-11 | 2016-09-21 | 重庆大学 | Failure rate calculation method of improved electric power device |
| CN106647263B (en) * | 2016-12-01 | 2019-11-26 | 贵州电网有限责任公司电力科学研究院 | A kind of electric power apparatus examination decision-making technique Degradation Theories and equipment Risk such as utilizing |
| CN108169560A (en) * | 2017-12-21 | 2018-06-15 | 哈尔滨工程大学 | A kind of segmentation Sine-Fitting decomposition method |
| CN110654949B (en) * | 2018-06-29 | 2021-02-19 | 哈尔滨工业大学 | Method for determining safe remaining service life of elevator under maintenance condition |
| CN109376451B (en) * | 2018-11-09 | 2023-05-02 | 广东电网有限责任公司 | Automatic equipment failure rate calculation method based on fitting association |
| CN109711635B (en) * | 2019-01-08 | 2020-10-27 | 北京交通大学 | Equipment maintenance strategy optimization method based on station capacity maintenance |
| CN110263981B (en) * | 2019-05-30 | 2023-04-07 | 天津大学 | Gas-electric coupling comprehensive energy system planning method considering flexible scheduling strategy |
| CN110889495B (en) * | 2019-12-04 | 2023-06-23 | 河南中烟工业有限责任公司 | State maintenance analysis method of filament making equipment based on active parameters |
| CN111985816A (en) * | 2020-08-21 | 2020-11-24 | 广东电网有限责任公司电力调度控制中心 | Power equipment maintenance mode risk cost assessment method, device, terminal and medium |
| CN113379120B (en) * | 2021-06-08 | 2024-02-23 | 华北电力大学 | Power distribution network equipment fault prediction method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104166788A (en) * | 2014-07-22 | 2014-11-26 | 国家电网公司 | Overhead transmission line optimal economic life range assessment method |
| CN104462718A (en) * | 2014-12-25 | 2015-03-25 | 国家电网公司 | Method for evaluating economic operation year range of transformer substation |
-
2015
- 2015-06-16 CN CN201510334913.1A patent/CN104933482B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104166788A (en) * | 2014-07-22 | 2014-11-26 | 国家电网公司 | Overhead transmission line optimal economic life range assessment method |
| CN104462718A (en) * | 2014-12-25 | 2015-03-25 | 国家电网公司 | Method for evaluating economic operation year range of transformer substation |
Non-Patent Citations (2)
| Title |
|---|
| 基于全寿命周期成本的变电站设备检修模糊规划模型;顾洁 等;《电力***自动化》;20140110;第38卷(第1期);全文 * |
| 基于模糊响应的阶梯电价机制研究;李媛 等;《广东电力》;20120331;第40卷(第3期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104933482A (en) | 2015-09-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104933482B (en) | The electric power apparatus examination optimization method to be retracted based on the fuzzy enlistment age | |
| CN102509018B (en) | System and method for evaluating importance of power system facilities | |
| US20210224755A1 (en) | Decision method of condition-based maintenance to power grid risk | |
| CN107909253A (en) | Intelligent distribution network scheduling controlling effect evaluation method based on interval based AHP | |
| Abbasghorbani et al. | Reliability‐centred maintenance for circuit breakers in transmission networks | |
| EP3072200B1 (en) | Holistic optimization of distribution automation using survivability modeling to support storm hardening | |
| Aldhubaib et al. | A novel approach to investigate the effect of maintenance on the replacement time for transformers | |
| CN109949178B (en) | Method for judging and complementing power failure event of medium-voltage distribution network based on support vector machine | |
| CN106485596A (en) | A kind of controller switching equipment Strategies of Maintenance optimization method | |
| CN112907078A (en) | Market clearing method, system, equipment and readable storage medium for water-electricity coupling | |
| CN103198364A (en) | Nuclear power station main transformer service life management strategy optimization method based on genetic algorithm | |
| CN105488572A (en) | Health state evaluation method of power distribution equipment | |
| Sugier et al. | Modelling and evaluation of deterioration process with maintenance activities | |
| RU2754484C2 (en) | Method and device for ensuring the balance of electricity consumption | |
| CN114035087A (en) | Method, device, equipment and medium for evaluating residual life of energy storage battery | |
| Anders et al. | A comprehensive study of outage rates of air blast breakers | |
| CN108649573B (en) | Calculation method for power grid loss change caused by maintenance of power transmission and transformation equipment | |
| CN115828489A (en) | Sensing equipment deployment method and system based on key measurement stationing position search | |
| Avritzer et al. | A scalable approach to the assessment of storm impact in distributed automation power grids | |
| Wu et al. | Research on condition-based maintenance approach of power system considering equipment imperfect maintenance model | |
| CN115329995A (en) | Optimization method and system for state maintenance decision of power system | |
| Fan et al. | Research on optimal load shedding for active distribution network based on genetic algorithm | |
| Jayaweera | Security enhancement with nodal criticality-based integration of strategic micro grids | |
| CN114169648A (en) | Power load prediction method, system and equipment based on NARNN model | |
| CN113947251A (en) | Method, device, equipment and medium for predicting daily maximum load rate of transformer |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |