CN107194476A - The pre- anti-aging maintenance policy formulating method of transformer based on semi-Markov chain - Google Patents
The pre- anti-aging maintenance policy formulating method of transformer based on semi-Markov chain Download PDFInfo
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- CN107194476A CN107194476A CN201710385491.XA CN201710385491A CN107194476A CN 107194476 A CN107194476 A CN 107194476A CN 201710385491 A CN201710385491 A CN 201710385491A CN 107194476 A CN107194476 A CN 107194476A
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- 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
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- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- 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
Abstract
A kind of pre- anti-aging maintenance policy formulating method of transformer based on semi-Markov chain, it comprises the following steps：(1) Ageing Model of transformer preventative maintenance is set up；(2) index of transformer ageing state is obtained, index parameter is Z={ 1,2,3 ... L }；(3) confidence level of transformer ageing state is set up；(4) according to the confidence level of transformer ageing state, iterate and draw the totle drilling cost of optimal maintenance policy and the maintenance policy.The present invention can optimize existing transformer preventive maintenance decision strategy, the economic loss that equipment out of use is brought during in view of overhaul of the equipments, set up Ageing Model of the transformer based on random failure, and assess transformer state using semi-Markovian decision, the confidence level of observation index is set up simultaneously, passes through the condition evaluation results of bayesian theory amendment transformer equipment；Model and evaluation decision method that the present invention is set up, can optimize transformer preventive maintenance decision strategy, improve the service efficiency of transformer, while reducing maintenance cost.
Description
Technical field
The present invention relates to power system device repair and maintenance field, and in particular to a kind of based on semi-Markov chain
The pre- anti-aging maintenance policy formulating method of transformer.
Background technology
In HVDC transmission system, transformer is one of most important equipment, and it is in alternating current and direct current is mutual
The core position of phase inversion.When transformer station high-voltage side bus was to 30 years, transformer detection project is opened by preventive trial and Maintenance Significant Items
Exhibition determines the actual useful year of transformer, it is necessary to be estimated to transformer life.
The age of transformer can be divided into natural age and insulation age.Natural age refers to the reality that transformer is normally run
The border age.The insulation age refers to the corresponding basal age of insulation ag(e)ing status monitoring amount of transformer.When the calendar year of transformer
When age is more than the insulation age, indication transformer ageing process is slower, and running status is preferable.When the natural age of transformer is less than
During the insulation age, indication transformer ageing process is very fast, and running status is poor.According to the evaluation status of transformer equipment, to protect
Hinder the health operation of equipment, it should be checked, detected, the work of maintenance and repair, the maintenance of equipment is divided into A, B, C tri-
Class.The maintenance of A classes should include all B classes overhauling projects in principle, and the maintenance of B classes should include C class overhauling projects.
The maintenance of A classes refers to overall inspection, maintenance, the experiment work that the power failure of equipment needs is carried out.
The maintenance of B classes refers to toposcopy, maintenance, replacing, the experiment work that the power failure of equipment needs is carried out.
The maintenance of C classes refers to that equipment need not have a power failure inspection, maintenance, replacing, the experiment work of progress.
C1, which is overhauled, to be referred to typically patrol dimension, i.e., the inspection, experiment, maintenance work carried out to equipment are needed during daily tour.
C2 maintenance refers to that specialty is patrolled under dimension, i.e. specified conditions, and the diagnostic test, spy for equipment development are patrolled, repaired, more
Change, experiment work.
Transformer aging is Utilities Electric Co.'s facing challenges sex chromosome mosaicism, Utilities Electric Co. generally according to the maintenance being previously set and
The retired age safeguards and more new equipment.This strategy is disliked due to not considering transformer practical operation situation for running environment
Bad transformer is possible to not arrange yet retired and cause system risk in its end-of-life, preferable for running environment
Transformer, which is possible to the transformer when reaching the retired age, still preferable state, and too early retired can make being worth for assets
Less than making full use of.
The content of the invention
It is an object of the invention to provide a kind of pre- anti-aging maintenance policy formulation side of transformer based on semi-Markov chain
Method, can correct the observation error of Transformer State Assessment, and assess situation according to time of day is carried out to the maintenance policy of equipment
Optimization, so that Optimal Maintenance strategy, improves the service efficiency of transformer, while reducing maintenance cost.
To achieve the above object, the present invention is adopted the technical scheme that：
A kind of pre- anti-aging maintenance policy formulating method of transformer based on semi-Markov chain, it comprises the following steps：
(1) Ageing Model of transformer preventative maintenance is set up；
(2) index of transformer ageing state is obtained, index parameter is Z={ 1,2,3 ... L }；
(3) confidence level of transformer ageing state is set up；
(4) according to the confidence level of transformer ageing state, iterate and draw optimal maintenance policy and the maintenance policy
Totle drilling cost.
As a modification of the present invention, the step (1) includes following sub-step：
Transformer equipment ageing state table 1-1) is set up, the state decomposition of equipment is expressed as：X={ D1,D2,D3,…,
Dk},DkState is Dk-1Situation after state deteriorating, XtThe state of equipment during for moment t；
1-2) set up ageing equipment state-transition matrix model, it is assumed that equipment state is shifted in moment t：
T=0, t1,t2,…(tq+1-tq＞ 0),Represent equipment in moment tqState；Assuming that making maintenance in moment t
After decision-making (maintenance, do not overhaul), the matrix model that equipment state is transferred to the probability of a certain ageing step is:
Pij(at)=p (Xt+1=j | Xt=i, a) (9)
In formula (10), it is assumed that random failure speed is fixed value, and when equipment is not in state DkWhen, ageing failure speed
Rate is zero, employs confidence distribution in formula (11) to represent that equipment is in the probability of a certain ageing step；Pij(a) represent at the moment
T is performed after decision-making a, and equipment state is transferred to j probability from i；
Equipment state residence time model 1-3) is set up, due in equipment shape can not be changed at once after decision-making is carried out
State, in order to describe residence time of the equipment between two states, introduces the probability matrix model of state residence time and state decision-making：
M is the average waiting time that equipment is transferred to j from state i；
1-4) according to formula (1) (2), the Ageing Model of equipment state is represented with Q, ageing equipment procedural representation equipment when
Carve tqPerform decision-makingAfterwards, equipment state is in tq+1Moment changes to j from i, then：
Further, the step (3) includes following sub-step：
3-1) consider that equipment state index is obtained from sampling observation, the state index parameter Z={ 1,2,3 ... L } of sampling observation
The ageing state of equipment can not accurately be represented, therefore introduces state index parameter confidence level bikTo represent the ageing state of equipment
Probability distribution：
Wherein, k ∈ Z, i ∈ X, bikWhen the parameter for representing to observe Ageing Index is k, the ageing state of equipment is the general of i
Rate, k is the setting valve of the indexs such as oil chromatography, electrical test, oiling test, fortune inspection record；
3-2) assume that the confidence level of ageing equipment state can be represented with information vector π, πt=[πt(1),πt(2),…,
πt(N)]
Then,πtRepresent the probability distribution of moment t equipment state；Therefore, in timing node tq, policymaker
Vector to information known to the ageing process is dt={ π0,Z1,a1,…,Zt-1,at-1,Zt}；
Finally, the probability distribution of future device state in which is corrected using bayes rule, the probability distribution is also referred to as
Make posterior probability
Further, step (4) detailed process is as follows：
The expectation of totle drilling cost is iterated to calculate out using the confidence level of ageing equipment state, the expectation of the cost can be used to iteration
The minimum value of cost under decision scheme is calculated, is iterated, optimal Strategies of Maintenance is obtained,
(i a) represents to take decision-making a, C in state i that (i a) represents to take decision-making a instant cost, c in state i to R
(i, a, t) represents the stepped cost before next decision-making arrival when moment t, state i took decision-making a.
Compared with prior art, the present invention has advantages below：
The present invention can optimize existing transformer preventive maintenance decision strategy, it is contemplated that equipment out of use institute band during overhaul of the equipments
The economic loss come, sets up Ageing Model of the transformer based on random failure, and assess transformation using semi-Markovian decision
Device state, while setting up the confidence level of observation index, passes through the condition evaluation results of bayesian theory amendment transformer equipment；This
The model set up and evaluation decision method are invented, can effectively be reduced due to the transformation that Condition Detection uncertainty is brought
The error of device state estimation, so as to optimize transformer preventive maintenance decision strategy, improves the service efficiency of transformer, reduces simultaneously
Maintenance cost.
Brief description of the drawings
Fig. 1 is the flow chart of the transformer pre- anti-aging maintenance policy formulating method of the invention based on semi-Markov chain；
Fig. 2 is the schematic diagram of one and half markoff process；
Fig. 3 is the multiple ageing failure model of a consideration random failure factor.
Embodiment
Present disclosure is described in further details with reference to the accompanying drawings and detailed description.It may be appreciated
It is that specific embodiment described herein is used only for explaining the present invention, rather than limitation of the invention.Further need exist for explanation
, for the ease of description, part related to the present invention rather than full content are illustrate only in accompanying drawing.
The present invention is assessed by setting up Ageing Model of the transformer based on random failure, and using semi-Markovian decision
Transformer state, while setting up the confidence level of observation index, passes through the state estimation knot of bayesian theory amendment transformer equipment
Really.
Involved maintenance policy in the present invention, refers to be directed to transformer characteristic, is marked according to associated user's handbook and country
Standard, transformer maintenance strategy is established respectively to be included：Tour project (maintenance of C classes), overhauling project (maintenance of B classes), ageing failure inspection
Repair project (maintenance of A classes).Wherein the content of C classes maintenance as shown in table 1, as shown in table 2, it is interior that overhaul the content of B classes maintenance by A classes
Hold as shown in table 3.The maintenance of A classes should include all B classes overhauling projects in principle, and the maintenance of B classes should include C class overhauling projects.
| 1 | Body temperature is checked | 8 | On-load voltage regulating switch on-line oil filter is checked |
| 2 | Body and shunting switch oil level are checked | 9 | Body and on-load voltage regulating switch pressure relief valve are checked |
| 3 | Leakage of oil is checked | 10 | Connection becomes on-site control |
| 4 | Run audible inspection | 11 | On-load voltage regulating switch |
| 5 | Ground connection checks | 12 | Sleeve pipe is checked |
| 6 | Respirator is checked | 13 | Infrared measurement of temperature |
| 7 | Cooling device is checked | 14 | Oil chromatography on-Line Monitor Device |
The transformer of table 1 makes an inspection tour project
Special make an inspection tour is carried out to transformer in situations：
1) transformer after newly putting into or overhaul, is putting into operation 72 hours in；
2) great and urgent defect (such as dissolved gas analysis results abnormity, has hot-spot phenomenon, cooling system
Abnormal, oil leak etc.) when；
3) during change in weather (such as strong wind, dense fog, heavy snow, hail, cold wave etc.)；
4) after thunderstorm season particularly thunderstorm；
5) high temperature season, during peak load.
The overhauling project of table 2
| 1 | The light gas alarming of body relay | 4 | There is contact overheat in sleeve pipe | 7 | Oil level is abnormal |
| 2 | Body relay grave gas is acted | 5 | There is serious oil leak in body construction | 8 | Cacophonia |
| 3 | Bulk density discharges valve events | 6 | Respirator permeability | 9 | Insulaion resistance is abnormal |
The ageing failure overhauling project of table 3
Embodiment
It refer to Fig. 1, a kind of pre- anti-aging maintenance policy formulating method of transformer based on semi-Markov chain, it includes
Following steps：
(1) Ageing Model of transformer preventative maintenance is set up；
It includes following sub-step：
Transformer equipment ageing state table 1-1) is set up, the state decomposition of equipment is expressed as：X={ D1,D2,D3,…,
Dk},DkState is Dk-1Situation after state deteriorating, XtThe state of equipment during for moment t；
1-2) set up ageing equipment state-transition matrix model, it is assumed that equipment state is shifted in moment t：
T=0, t1,t2,…(tq+1-tq＞ 0),Represent equipment in moment tqState；Assuming that making maintenance in moment t
After decision-making (maintenance, do not overhaul), the matrix model that equipment state is transferred to the probability of a certain ageing step is:
Pij(at)=p (Xt+1=j | Xt=i, a) (17)
In formula (18), it is assumed that random failure speed is fixed value, and when equipment is not in state DkWhen, ageing failure speed
Rate is zero, employs confidence distribution in formula (19) to represent that equipment is in the probability of a certain ageing step；Pij(a) represent at the moment
T is performed after decision-making a, and equipment state is transferred to j probability from i；
Equipment state residence time model 1-3) is set up, due in equipment shape can not be changed at once after decision-making is carried out
State, in order to describe residence time of the equipment between two states, introduces the probability matrix model of state residence time and state decision-making：
M is the average waiting time that equipment is transferred to j from state i；
1-4) according to formula (1) (2), the Ageing Model of equipment state is represented with Q, ageing equipment procedural representation equipment when
Carve tqPerform decision-makingAfterwards, equipment state is in tq+1Moment changes to j from i, then：
(2) index of transformer ageing state is obtained, index parameter is Z={ 1,2,3 ... L }.
(3) confidence level of transformer ageing state is set up；
It includes following sub-step：
3-1) consider that equipment state index is obtained from sampling observation, the state index parameter Z={ 1,2,3 ... L } of sampling observation
The ageing state of equipment can not accurately be represented, therefore introduces state index parameter confidence level bikTo represent the ageing state of equipment
Probability distribution：
Wherein, k ∈ Z, i ∈ X, bikWhen the parameter for representing to observe Ageing Index is k, the ageing state of equipment is the general of i
Rate, k is the setting valve of the indexs such as oil chromatography, electrical test, oiling test, fortune inspection record；
3-2) assume that the confidence level of ageing equipment state can be represented with information vector π, πt=[πt(1),πt(2),…,
πt(N)]
Then,πtRepresent the probability distribution of moment t equipment state；Therefore, in timing node tq, policymaker
Vector to information known to the ageing process is dt={ π0,Z1,a1,…,Zt-1,at-1,Zt}；
Finally, the probability distribution of future device state in which is corrected using bayes rule, the probability distribution is also referred to as
Make posterior probability
(4) according to the confidence level of transformer ageing state, iterate and draw optimal maintenance policy and the maintenance policy
Totle drilling cost；
Specifically：The expectation of totle drilling cost is iterated to calculate out using the confidence level of ageing equipment state, the expectation of the cost can
For iterating to calculate out the minimum value of cost under decision scheme, iterate, obtain optimal Strategies of Maintenance,
(i a) represents to take decision-making a, C in state i that (i a) represents to take decision-making a instant cost, c in state i to R
(i, a, t) represents the stepped cost before next decision-making arrival when moment t, state i took decision-making a.
The schematic diagram of one and half markoff process is illustrated in figure 2, T represents the maintenance decision moment, in decision-making a0Work
Under, the state of equipment is in moment T1From X0It is transferred to X1；Simultaneously based on observation data B1, new decision-making a1Migrate system mode
To X2。
As shown in figure 3, in transformer failure model, while considering random failure factor and ageing failure factor.Will
Power system transformer failure is regarded as based on random failure and the coefficient model of ageing failure.Transformer state is divided into three
Individual state：Ageing state D, service mode M, failure state F, Z is take maintenance decision under current state, K is ageing state
Exponent number.Under each ageing state, maintenance decision can be carried out to equipment (maintenance of A classes, the maintenance of B classes, C classes are overhauled).At this
In model, it is assumed that D1It is brand-new, D to represent the equipmentkD is compared in expressionk-1Worse aging conditions.Preventative maintenance implement with
Afterwards, equipment state can revert to the ageing state of upper level.If do not repaired to equipment, eventually ageing failure reaches equipment
To state F1。ZkIt is the observing matrix of ageing equipment state.Because the random failure of equipment can not be subtracted by preventative maintenance
It is small.When equipment failure F occurs, it is not necessary to which any detection is known that.In equipment Manager ageing failure F1When, equipment is carried out
Overhaul or replacing, so that equipment state returns to the state D of a new equipment1.In random failure F0After generation, equipment is entered
Row maintenance, equipment returns to the ageing state before random failure occurs.
Wherein, λ0For the failure rate of random failure, λ1For the failure rate of ageing failure, μ0Represent unaged failure shape
Maintenance speed under state, μ1Represent the maintenance speed under ageing state, λmRepresent preventative maintenance failure rate, μmRepresent prevention
Property maintenance speed.
Above-described embodiment only not limits the technical scheme described by this patent to illustrate this patent；Therefore, although
This specification has been carried out detailed description, still, the ordinary skill of this area to this patent with reference to each above-mentioned embodiment
Personnel should be appreciated that still can modify or equivalent substitution to this patent；And all do not depart from this patent spirit and
The technical scheme of scope and its improvement, it all should cover among the right of this patent.
Claims (4)
1. the pre- anti-aging maintenance policy formulating method of a kind of transformer based on semi-Markov chain, it is characterised in that including following
Step：
(1) Ageing Model of transformer preventative maintenance is set up；
(2) index of transformer ageing state is obtained, index parameter is Z={ 1,2,3 ... L }；
(3) confidence level of transformer ageing state is set up；
(4) according to the confidence level of transformer ageing state, iterate and draw the assembly of optimal maintenance policy and the maintenance policy
This.
2. the pre- anti-aging maintenance policy formulating method of the transformer according to claim 1 based on semi-Markov chain, its
It is characterised by：The step (1) includes following sub-step：
Transformer equipment ageing state table 1-1) is set up, the state decomposition of equipment is expressed as：X={ D1,D2,D3,…,Dk},Dk
State is Dk-1Situation after state deteriorating, XtThe state of equipment during for moment t；
1-2) set up ageing equipment state-transition matrix model, it is assumed that equipment state is shifted in moment t：
T=0, t1,t2,…(tq+1-tq＞ 0),Represent equipment in moment tqState；Assuming that making maintenance decision in moment t
After (maintenance, do not overhaul), the matrix model that equipment state is transferred to the probability of a certain ageing step is:
Pij(at)=p (Xt+1=j | Xt=i, a) (1)
In formula (2), it is assumed that random failure speed is fixed value, and when equipment is not in state DkWhen, ageing failure speed is
Zero, confidence distribution is employed in formula (3) to represent that equipment is in the probability of a certain ageing step；Pij(a) represent to perform in moment t
After decision-making a, equipment state is transferred to j probability from i；
Equipment state residence time model 1-3) is set up, due in equipment state can not be changed at once after decision-making is carried out, is
Residence time of the description equipment between two states, introduce the probability matrix model of state residence time and state decision-making：
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1-4) according to formula (1) (2), the Ageing Model of equipment state represents that ageing equipment procedural representation equipment is in moment t with Qq
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3. the pre- anti-aging maintenance policy formulating method of the transformer according to claim 2 based on semi-Markov chain, its
It is characterised by：The step (3) includes following sub-step：
3-1) consider that equipment state index is obtained from sampling observation, the state index parameter Z={ 1,2,3 ... L } of sampling observation can not
The ageing state of equipment is accurately represented, therefore introduces state index parameter confidence level bikCome represent equipment ageing state it is general
Rate is distributed：
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Wherein, k ∈ Z, i ∈ X, bikWhen the parameter for representing to observe Ageing Index is k, the ageing state of equipment is i probability, k
For the setting valve of the indexs such as oil chromatography, electrical test, oiling test, fortune inspection record；
3-2) assume that the confidence level of ageing equipment state can be represented with information vector π, πt=[πt(1),πt(2),…,πt
(N)]
Then,πtRepresent the probability distribution of moment t equipment state；Therefore, in timing node tq, policymaker is old to this
The vector of information known to change process is dt={ π0,Z1,a1,…,Zt-1,at-1,Zt}；
Finally, the probability distribution of future device state in which is corrected using bayes rule, after the probability distribution is also referred to as
Test probability
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</mrow>
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4. the pre- anti-aging maintenance policy formulating method of the transformer according to claim 3 based on semi-Markov chain, its
It is characterised by：Step (4) detailed process is as follows：
The expectation of totle drilling cost is iterated to calculate out using the confidence level of ageing equipment state, the expectation of the cost can be used to iterate to calculate
Go out the minimum value of cost under decision scheme, iterate, obtain optimal Strategies of Maintenance,
<mrow>
<mi>R</mi>
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<mo>+</mo>
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R (i, a) represent to take in state i decision-making a, C (i, a) represents to take decision-making a instant cost in state i, c (i,
A, t) represent the stepped cost before next decision-making arrival when moment t, state i took decision-making a.
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| CN107679631A (en) * | 2017-09-30 | 2018-02-09 | 东莞新奥燃气有限公司 | The formulating method and operation management platform of a kind of maintenance plan |
| CN107992678A (en) * | 2017-11-30 | 2018-05-04 | 南方电网科学研究院有限责任公司 | A kind of transformer health status evaluation method and device based on Life cycle |
| CN108122049A (en) * | 2017-12-21 | 2018-06-05 | 郓城金河热电有限责任公司 | Photovoltaic array operation and maintenance method based on the analysis of photovoltaic module failure rate |
| CN108445761A (en) * | 2018-03-15 | 2018-08-24 | 南京林业大学 | Combine modeling method with maintenance strategy based on GERT network statistics process control |
| CN108445384A (en) * | 2018-05-10 | 2018-08-24 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | A kind of change of current becomes the on-line monitoring and appraisal procedure of load ratio bridging switch operating status |
| CN109583601A (en) * | 2018-11-30 | 2019-04-05 | 天津大学 | A kind of controller switching equipment repair based on condition of component policy optimization method |
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| CN107607820A (en) * | 2017-10-10 | 2018-01-19 | 华北电力大学 | A kind of inside transformer Hidden fault rate Forecasting Methodology based on birth and death process |
| CN107607820B (en) * | 2017-10-10 | 2020-01-31 | 华北电力大学 | method for predicting latent fault rate in transformer based on life-kill process |
| CN107992678A (en) * | 2017-11-30 | 2018-05-04 | 南方电网科学研究院有限责任公司 | A kind of transformer health status evaluation method and device based on Life cycle |
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| CN108445384A (en) * | 2018-05-10 | 2018-08-24 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | A kind of change of current becomes the on-line monitoring and appraisal procedure of load ratio bridging switch operating status |
| CN108445384B (en) * | 2018-05-10 | 2020-11-24 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | On-line monitoring and evaluating method for operating state of converter transformer on-load tap-changer |
| CN109583601A (en) * | 2018-11-30 | 2019-04-05 | 天津大学 | A kind of controller switching equipment repair based on condition of component policy optimization method |
| CN109583601B (en) * | 2018-11-30 | 2023-05-23 | 天津大学 | Distribution equipment state maintenance strategy optimization method |
| CN110018371A (en) * | 2019-03-28 | 2019-07-16 | 国网山东省电力公司青岛供电公司 | Converter station master status examination and repair system and its implementation |
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| CN112632677A (en) * | 2020-12-25 | 2021-04-09 | 浙江大学 | Bridge full-life maintenance strategy optimization method based on half Markov decision process |
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