CN107607820A - A kind of inside transformer Hidden fault rate Forecasting Methodology based on birth and death process - Google Patents
A kind of inside transformer Hidden fault rate Forecasting Methodology based on birth and death process Download PDFInfo
- Publication number
- CN107607820A CN107607820A CN201710934717.7A CN201710934717A CN107607820A CN 107607820 A CN107607820 A CN 107607820A CN 201710934717 A CN201710934717 A CN 201710934717A CN 107607820 A CN107607820 A CN 107607820A
- Authority
- CN
- China
- Prior art keywords
- transformer
- mrow
- msub
- fault rate
- maintenance
- 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.)
- Granted
Links
Landscapes
- Housings And Mounting Of Transformers (AREA)
- Protection Of Transformers (AREA)
- Testing Electric Properties And Detecting Electric Faults (AREA)
- Testing Of Short-Circuits, Discontinuities, Leakage, Or Incorrect Line Connections (AREA)
Abstract
The invention discloses a kind of inside transformer Hidden fault rate Forecasting Methodology based on birth and death process, the described method comprises the following steps：1. obtaining the historical data and hot spot temperature of winding data of transformer state residence time, gas content in transformer oil is detected；2. the transfer rate λ of 4 kinds of states of calculating transformer history run；3. according to hot spot temperature of winding data, calculating transformer aging accelerated factor；4. calculate the average accelerated factor of history；5. solve transformer time-varying state transfer rate；6. writing the birth and death process equation group of transformer state transfer according to transformer state transfer mechanism and operation history data row, calculating transformer is in m running status time-varying fault rates；7. according to the maintenance type amendment time-varying fault rate of transformer；8. transformer reliability is asked according to the fault rate of step 7；9. exporting transformer fault rate and reliability prediction curve, with reference to transformer reality operation conditions, analysis is carried out to transformer next step running status and proposes that maintenance is suggested.
Description
Technical field
It is pre- more particularly to inside transformer Hidden fault rate the present invention relates to transformer fault rate electric powder prediction
Survey method.
Background technology
Transformer fault is divided into external fault and internal fault, and wherein internal fault is mainly old by inside transformer device
Change causes.In the inside of transformer, hot(test)-spot temperature rise, internal components accelerated ageing can be made, so that transformer fault rate liter
It is high.Hot-spot temperature of transformer rise can cause gas in transformer insulation oil to produce quickening, so, oil dissolved gas content can
Reflect the health status of transformer.
Inside transformer Hidden fault rate is often asked for according to gas content in transformer oil.Conventional fault rate is asked
Solution method is divided into analytic method and simulation.
Analytic method is using the transformer fault rate model based on Poisson process and markoff process as representative, the class model root
According to transformer state transfer process, the fault rate of transformer is derived using correlation theory.The advantage of analytic method is letter
List, it is easy to grasp, and requires no knowledge about the state principle of transfer of transformer, between each state of transformer is thought before fault rate solves
Transfer markov property.But analytic method is not easy to consider the influence of each factor of influence transformer fault rate, and uses related reason
By when have a corresponding restrictive condition, such as Markov process requires that the state duration of transformer must obey exponential distribution,
But in transformer reality operation, its state duration not necessarily meets the requirement.
Simulation is by carrying out scenario simulation, the failure using analogue data to transformer to the running of transformer
Rate is solved, and such method is although workable, but must have a large amount of training datas to support, and cost is higher, and not
Easily transformer reliability function is verified.Exemplary is to consider the transformer time-varying outage model of on-line monitoring information,
The model obtains each state duration using the Acception-Rejection method of samplings, generates sample, and pass through
The parameter for changing scale model quantifies influence of the different affecting factors to transformer fault rate.
In addition, conventional method, in calculating transformer probability of malfunction, majority does not account for the transformer station high-voltage side bus time limit, maintenance plan
The influence to transformer fault probability such as summary, operating load, after transformer carries out oil purification processing, contain according only to oil dissolved gas
Measure to predict transformer fault probability, it is evident that obtain result inaccuracy.It can not quantify to become in some transformer fault rate models
Influence of the depressor history run to future time instance transformer fault rate.
In addition, to transformer carry out maintenance policy formulation when, it is necessary to transformer fault rate carry out long-time prediction, but pass
System method can not meet to require mostly.
It is desirable to have a kind of inside transformer Hidden fault rate Forecasting Methodology to overcome or at least mitigate existing skill
The problem of in art.
The content of the invention
It is an object of the invention to provide a kind of inside transformer Hidden fault rate Forecasting Methodology, to transformer station high-voltage side bus event
Barrier rate is accurately solved, and takes into full account the accelerated factor for influenceing transformer fault, is solved transformer fault rate and is used parsing
Method solves the problem of difficult.
Include the invention provides a kind of inside transformer Hidden fault rate Forecasting Methodology based on birth and death process following
Step：
Step 1：The historical data and coiling hot point of transformer temperature data of transformer state residence time is obtained, and it is right
Gas content in transformer oil is detected；
Step 2：Statistical analysis is carried out to the residence time of 4 kinds of states in transformer history run, calculates 4 kinds respectively
The transfer rate λ of state；
Step 3：According to coiling hot point of transformer temperature data, calculating transformer aging accelerated factor：
Wherein, FAAFor transformer aging accelerated factor, B is that constant is relevant with load factor and environment temperature,For winding
Hot(test)-spot temperature a reference value, θ are current time coiling hot point of transformer temperature；
Step 4：Shadow by the average accelerated factor indication transformer history run of history to transformer fault rate in future
Ring, calculate the average accelerated factor of history：
Wherein, FAAlossFor transformer station high-voltage side bus history accelerated ageing factor average value, t1And t2Respectively transformer input fortune
Row moment and current time；
Step 5：Solve transformer time-varying state transfer rate：
λ′mn=(FAA+FAAloss)λmn(3),
Wherein, λmnThe transfer rate of n-state is transferred to by m states for transformer；
Step 6：The life that transformer state transfer is write according to transformer state transfer mechanism and operation history data row is sterilized
Journey equation group, and solve the time-varying fault rate that transformer is in m running statuses
Wherein, PmThe time-varying fault rate of transformer, m=1,2 or 3, λ ' during in m statesmnTurned for transformer by m states
Move on to the time-varying state transfer rate of n-state, n=4 is malfunction, unmTo be transferred to the repair rate of m states by n-state；
Step 7：The time-varying fault rate of transformer is modified according to the maintenance type of transformer, transformer maintenance plan
Selection and transformer state transfer rate slightly is in proportion function relation：
Wherein, λnewState transfer rate after for transformer maintenance, λ are state transfer rate before transformer maintenance, work as change
When the maintenance type of depressor is minimal maintenance a values be 1, when the maintenance type of transformer is imperfect repair a values be 2,
When the maintenance type of transformer is complete maintenance policy, a values are that 3, b is modifying factor, and b is according to the running status of transformer
With maintenance policy value, transformer is through maintenance failure rate：
P(t,λnew)new=kP (t, λ) (6),
Wherein, P (t, λnew)newFor transformer, time-varying fault rate, P (t, λ) are transformer maintenance prior fault after maintenance
Rate, k are proportionality coefficient, and k is according to the running status and maintenance policy value of transformer；
Step 8：The reliability of transformer is sought according to the fault rate of step 7：
Step 9：Transformer fault rate and reliability prediction curve are exported, in conjunction with transformer reality operation conditions, to becoming
Depressor next step running status carries out analysis and proposes that maintenance is suggested.
Preferably, 4 kinds of states of the transformer history run in the step 2 include：Kilter, alarm condition, danger
State and malfunction.
Preferably, the transformer time-varying state transfer rate according to Transformer Winding temperature history and works as front winding
Temperature data value, when following Transformer Winding temperature data is predictable, transformer fault rate model can carry out fault rate
Prediction.
Preferably, transformer fault rate is modeled using birth and death process theory in the step 6.
Preferably, the changing value of fault rate after different maintenance policies is selected in the step 7 according to transformer, to formula
(5) and in formula (6) coefficient a, b and k carry out value, and by a, b and k numerical applications in the amendment of next fault rate, with
This quantifies the different influences to transformer fault rate of transformer maintenance strategy.
The invention discloses a kind of inside transformer Hidden fault rate Forecasting Methodology based on birth and death process, this method energy
Reasonably consider transformer station high-voltage side bus history and influence of the current operating environment to transformer fault rate, and use time-varying transformation
Device state transfer rate, the accuracy to the prediction of future time instance fault rate is improved, makes fail-safe analysis result of calculation more reasonable.And
Transformer state equation of transfer group is solved using birth and death process method for solving, it is possible to increase solving precision.
Brief description of the drawings
Fig. 1 is transformer state transfer schematic diagram；
Fig. 2 is influence curve figure of the transformer difference maintenance policy to transformer fault rate；
Fig. 3 is Gas in Oil of Transformer volume content schematic diagram；
Fig. 4 is transformer fault rate simulation curve figure in shape；
Fig. 5 is that transformer is in alarm condition fault rate simulation curve figure；
Fig. 6 transformer station high-voltage side bus cycle internal fault rate simulation curve figures；
Fig. 7 is the inside transformer Hidden fault rate Forecasting Methodology flow chart based on birth and death process.
Embodiment
To make the purpose, technical scheme and advantage that the present invention is implemented clearer, below in conjunction with the embodiment of the present invention
Accompanying drawing, the technical scheme in the embodiment of the present invention is further described in more detail.In the accompanying drawings, identical from beginning to end or class
As label represent same or similar element or the element with same or like function.Described embodiment is the present invention
Part of the embodiment, rather than whole embodiments.The embodiments described below with reference to the accompanying drawings are exemplary, it is intended to uses
It is of the invention in explaining, and be not considered as limiting the invention.Based on the embodiment in the present invention, ordinary skill people
The every other embodiment that member is obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.Under
Embodiments of the invention are described in detail with reference to accompanying drawing for face.
The inside transformer Hidden fault rate Forecasting Methodology based on birth and death process is situated between according to Figure of description 1-7
Continue.The fault rate of transformer is to characterize transformer health status, when transformer fault number is more in the unit interval, transformer
Operational reliability it is lower, economic value is smaller.Transformer fault rate is defined as life-span T probability distribution F (t) probability letter
Number：
Fault rate of the present invention is built upon on transformer state transfer rate basis, so the failure of transformer of the present invention
Rate is defined as the number of each week failure.
Transformer state transfer rate is the variable quantity of the transition probability in the unit time, and is transferred to event from running status
The transition probability of barrier state is a kind of conditional probability, and formula (8) probability function is change of the conditional probability within the unit interval
Amount, so, both are consistent in itself.
The health status of transformer can be determined by the acquisition gas content in transformer oil that the cycle is carried out.According to
IEEE Std C57.104-1991, the gas for being used in transformer oil weighing transformer health status have：H2, C2H4, C2H2,
CH4, C2H6And CO.Volume fraction based on Gases Dissolved in Transformer Oil, transformer real-time running state are divided into " good shape
State ", " alarm condition ", " precarious position ", " malfunction " four kinds of running statuses.When transformer state judges, when there is one kind
When gas volume fraction is in worse state, then it is assumed that transformer station high-voltage side bus is in the state.
When transformer is run under a certain state, the accumulation in elapsed time goes to next running status or become by changing
The service condition of depressor makes the health status of transformer improve.
It is excessive when flowing through transformer current, hot-spot temperature of transformer is raised, causes inside transformer gas production rate to become big,
When passing through certain measure, make to flow through transformer current and diminish, at this moment heat caused by transformer can reach balance with radiating, make
Hot(test)-spot temperature reduces, and transformer health status is become excellent.Transformer state transfer process of the present invention only considers to deteriorate into next
Running status, or repair to a upper running status.
As shown in figure 1, transformer state transfer figure is provided with k platform transformer statistics, yijRepresent that jth platform transformer is in
The residence time of i states, λijState j transfer rate, repair rate u are transferred to for state iijReparation for state i to state j
Time is reciprocal, then：
Inside transformer Hidden fault is the process of a long-run development, is not only had with transformer reality running environment
Close, and it is relevant with the history run of transformer.Transformer in the process of running, body interior insulation (including solid insulation and absolutely
Edge oil) performance gradually reduce, until lose its function after, transformer fault.The decay of transformer insulated performance can be used old
Change degree is weighed.The aging of transformer, it is not only relevant with the operation time limit, also with manufacture and installation quality, and operation maintenance
It is relevant, it is a variable that can reflect transformer reality health status comprehensively.The aging journey of transformer fault rate and transformer
Spend closely bound up.The degree of aging of transformer can influence the ability that transformer bears short circuit and overload.If transformation is utilized merely
Device oil dissolved gas content asks for the physical fault probability of transformer, it is impossible to enough reflects the actual value of transformer fault probability.
The aging of transformer is a long-term process, the run time of its degree of aging and transformer, average load, ring
Border quality has much relations.With the hot(test)-spot temperature of Transformer Winding each factor of the above can be reflected to transformer degree of aging
Influence, and accelerated ageing factor F is introduced in the prediction of following transformer risk probabilityEQA。
The relation of transformer insulated life-span and hot spot temperature of winding has been discussed in detail in IEEE C 57.91-1995：
L in formulapuFor the perunit value in transformer insulated life-span；A, B is constant, relevant with load factor and environment temperature；θ is
Hot spot temperature of winding.
Accelerated ageing factor FAAFor：
In formulaFor hot spot temperature of winding a reference value.
Transformer life penalty values LlossFor：
T in formula1Put into operation the moment for transformer；t2For transformer station high-voltage side bus moment till now.
Because in summer and winter, transformer station high-voltage side bus environment difference is larger, so when asking for transformer life penalty values,
Influence of two environment to transformer aging is considered respectively.
After the insulating paper of transformer carries out hot upgrading, a reference value of its hot(test)-spot temperature takes 110 DEG C, with reference to IEEE, draws：
A=9.8 × 10-18, B=15000.
Because the actual life penalty values of transformer be transformer history accelerated factor at runtime on integration, institute
With, influence of the transformer station high-voltage side bus history to transformer fault rate in future can be represented with the average accelerated factor of history, it is such as public
Shown in formula (12)：
The fault rate of the present invention is built upon on the basis of transformer state transfer rate, so, accelerated ageing factor pair
The influence of fault rate, the influence to transformer state transfer rate can be converted into, transformer time-varying state can be obtained with this
Transfer rate is：
λ′mn=(FAA+FAAloss)λmn (13)
λmnThe transfer rate of n-state is transferred to by m states for transformer.
Ask for the transformer time-varying fault rate based on birth and death process
Consider transformer current time health status and influence of the actual environment to transformer fault rate, using formula (3),
It can obtain transformer time-varying state transfer rate λ 'i,i+1, transformer time-varying state transition rates Q can be established：
Residing for determine transformer according to gas content in transformer oil after running status i, from i states, in next step
Adjacent states i+1 or i-1 can only be reached, and when transformer status is 1 state, can only enter state 2 in next step, or stop
Stay in 1 state；When transformer at t in i states, then being transferred to i+1 shape probability of states within the time (t, t+h) is
λ′i,i+1H+o (h), it is u to be transferred to i-1 shapes probability of statei,i-1H+o (h), the general of transfer more than once occurs in (t, t+h)
Rate o (h).
Moment t transformer state in which is represented with xt, then { xt, t >=0 } and a random process is formed, with Pij(t) represent
Transformer state transition probability, then when (h → 0), just like lower probability formula：
Derivation transformer is in the probability P of j states belowj(t), i.e.,：
Pj(t)=P (xt=j) (15)
Understood by analysis above, only can just obtain (t+h) moment by following three processes, transformer is in j
State：
(1) transformer at t in j states, and (t, t+h) in the time state of transformer do not shift；
(2) transformer at t in (j-1) or (j+1) state, after be transferred to j states；
(3) at (t, t+h) in the time, once above transfer process occurs, is eventually transferred into j states, such case occurs
Probability be o (h)；
Analyzed more than, following new probability formula can be obtained：
Pj(t+h)=[1- (λ 'j,j+1+uj,j-1)]Pj(t)+λ′j-1,jhPj-1(t)+uj+1,jhPj+1(t)+o(h) (16)
Pj (t) in formula (16) is moved to left, and both sides divided by h, then makes (h → 0) to obtain：
P’j(t)=[- (λ 'j,j+1+uj,j-1)]Pj(t)+λ′j-1,jPj-1(t)+uj+1,jPj+1(t) (17)
So according to formula (17), there is following formula：
P’0(t)=- b0P0(t)+a1P1(t) (18)
Because by transformer oil dissolved gas analysis, it is known that transformer original state i, so, introduce initial strip
Part：
Pi(0)=1, Pj(0)=0, j ≠ i (19)
Simultaneous formula (17), (18) and (19), t, the time domain formula of transformer fault probability can be obtained：
In formula：Pmn(t) probability of n-state is transferred to for m states；
In addition, the transfer rate of transformer adjacent states can be obtained by historical data, but between non-adjacent state
Transfer rate is bad to be obtained, so, provide and be transferred to state n average time by state k and be：
After transformer fault rate is obtained according to above method, it is necessary to the fault rate tried to achieve to the running status of transformer
Analyzed, so as to provide foundation to the formulation of Condition Maintenance Method of Transformer.
As shown in Fig. 2 the maintenance of transformer is divided into imperfect repair, completely maintenance, minimal maintenance, if transformer needs
Repaired, the time-varying fault rate of transformer is modified according to maintenance policy.
As shown in figure 3, the transformer fault rate Model in Time Domain based on birth and death process is analyzed, Gas in Oil of Transformer contains
Amount.(gas gross counts 2500 days, and each gas content counts 1750 days)
By data statistics, each state transfer rate (unit of transformer is tried to achieve：Secondary/week) be：λ12=0.0083, λ23
=0.0085, λ34=0.0193, corresponding repair rate is：u21=0.2863, u32=0.5120, u43=0.8543, meanwhile, root
λ is tried to achieve according to formula (18)14=8.686 × 10-7, u41=0.01252, λ24=3.04 × 10-5, u42=0.032.
(1) oil purification processing was carried out by 2100 days from transformer historical data, transformer station high-voltage side bus, to transformer event
During barrier rate is asked for, if only considering gas content in transformer oil, shadow of the aging to fault rate without considering transformer
Ring, obtained fault rate will not meet actual conditions.
(2) before transformer station high-voltage side bus 150 days it is in shape, and gas gross increase is very fast, represents to accelerate for convenience
The factor, make F=FAA+FAAlossIt is according to formula (20), kilter fault rate time domain formula then：
P1=exp [- 7 × 10-5×F(1-e-0.01252t)][7×10-5×F(1-e-0.01252t)]
As shown in figure 4, when accelerated factor is respectively 2,3,4, fault rate simulation curve in shape, difference accelerates
The influence of factor pair transformer fault rate is very big, therefore it is necessary to consider transformer reality health status with environmental aspect to transformation
The influence of device fault rate.
(3) when running to 1000 days, transformer enters alarm condition.Into after alarm condition, oil dissolved gas content ratio
Kilter increase is slightly fast, and when entering precarious position, gas content in transformer oil speedup is substantially accelerated, so, work as transformation
When device enters precarious position, associated maintenance is carried out in time.Transformer is into alarm condition fault rate time domain formula：
P2=exp [- 9.5 × 10-4×F(1-e-0.032t)]×[9.5×10-4×F(1-e-0.032t)],
It is illustrated in figure 5 the simulation curve of its different accelerated factor.
(4) precarious position is entered when transformer station high-voltage side bus was by 1400 days.Transformer station high-voltage side bus was by 2100 days, though failure is not reached
State, but consider that the aging of transformer makes the reduction of its performance, and fault rate is larger, about 0.06, so needing to enter transformer
Row oil purification is handled.
As shown in fig. 6, transformer entirety time-varying probability of malfunction curve, can from the analysis of above transformer fault rate
Go out, before precarious position, the internal fault of transformer also complies with the universal law of things development (generation, development and maturation),
Its probability of malfunction meets the Logistic models that Belgian mathematician in 1938 proposes, i.e. curve is first slowly increased, then quickly
Increase, it is rear to tend towards stability again.But in the case of transformer station high-voltage side bus is nonserviceabled, probability of malfunction increase is most fast.
It is last it is to be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations.To the greatest extent
The present invention is described in detail with reference to the foregoing embodiments for pipe, it will be understood by those within the art that：It is still
Technical scheme described in foregoing embodiments can be modified, or which part technical characteristic is equally replaced
Change；And these modifications or replacement, the essence of appropriate technical solution is departed from the essence of various embodiments of the present invention technical scheme
God and scope.
Claims (5)
1. a kind of inside transformer Hidden fault rate Forecasting Methodology based on birth and death process, it is characterised in that including following step
Suddenly：
Step 1：The historical data and coiling hot point of transformer temperature data of transformer state residence time is obtained, and to transformation
Device oil dissolved gas content is detected；
Step 2：Statistical analysis is carried out to the residence time of 4 kinds of states in transformer history run, calculates 4 kinds of states respectively
Transfer rate λ；
Step 3：According to coiling hot point of transformer temperature data, calculating transformer aging accelerated factor：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>A</mi>
<mi>A</mi>
</mrow>
</msub>
<mo>=</mo>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<mfrac>
<mi>B</mi>
<mrow>
<mover>
<mi>&theta;</mi>
<mo>&OverBar;</mo>
</mover>
<mo>+</mo>
<mn>273</mn>
</mrow>
</mfrac>
<mo>-</mo>
<mfrac>
<mi>B</mi>
<mrow>
<mi>&theta;</mi>
<mo>+</mo>
<mn>273</mn>
</mrow>
</mfrac>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
Wherein, FAAFor transformer aging accelerated factor, B is that constant is relevant with load factor and environment temperature,For coiling hotspot temperature
A reference value is spent, θ is current time coiling hot point of transformer temperature；
Step 4：Pass through average influence of the accelerated factor indication transformer history run to transformer fault rate in future of history, meter
Calculate the average accelerated factor of history：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>A</mi>
<mi>A</mi>
<mi>l</mi>
<mi>o</mi>
<mi>s</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mo>&Integral;</mo>
<msub>
<mi>t</mi>
<mn>1</mn>
</msub>
<msub>
<mi>t</mi>
<mn>2</mn>
</msub>
</msubsup>
<msub>
<mi>F</mi>
<mrow>
<mi>A</mi>
<mi>A</mi>
</mrow>
</msub>
<mi>d</mi>
<mi>t</mi>
</mrow>
<mrow>
<msub>
<mi>t</mi>
<mn>2</mn>
</msub>
<mo>-</mo>
<msub>
<mi>t</mi>
<mn>1</mn>
</msub>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
Wherein, FAAlossFor transformer station high-voltage side bus history accelerated ageing factor average value, t1And t2When respectively transformer puts into operation
Quarter and current time；
Step 5：Solve transformer time-varying state transfer rate：
λ′mn=(FAA+FAAloss)λmn(3),
Wherein, λmnThe transfer rate of n-state is transferred to by m states for transformer；
Step 6：The birth and death process side of transformer state transfer is write according to transformer state transfer mechanism and operation history data row
Journey group, and solve the time-varying fault rate that transformer is in m running statuses
<mrow>
<msub>
<mi>P</mi>
<mi>m</mi>
</msub>
<mo>=</mo>
<mi>exp</mi>
<mo>&lsqb;</mo>
<mo>-</mo>
<mfrac>
<mrow>
<msub>
<msup>
<mi>&lambda;</mi>
<mo>&prime;</mo>
</msup>
<mrow>
<mi>m</mi>
<mi>n</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>u</mi>
<mrow>
<mi>n</mi>
<mi>m</mi>
</mrow>
</msub>
</mfrac>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<msub>
<mi>u</mi>
<mrow>
<mi>n</mi>
<mi>m</mi>
</mrow>
</msub>
<mi>t</mi>
</mrow>
</msup>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>&lsqb;</mo>
<mfrac>
<mrow>
<msub>
<msup>
<mi>&lambda;</mi>
<mo>&prime;</mo>
</msup>
<mrow>
<mi>m</mi>
<mi>n</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>u</mi>
<mrow>
<mi>n</mi>
<mi>m</mi>
</mrow>
</msub>
</mfrac>
<mo>*</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<msub>
<mi>u</mi>
<mrow>
<mi>n</mi>
<mi>m</mi>
</mrow>
</msub>
<mi>t</mi>
</mrow>
</msup>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
Wherein, PmThe time-varying fault rate of transformer, m=1,2 or 3, λ ' during in m statesmnIt is transferred to for transformer by m states
The time-varying state transfer rate of n-state, n=4 are malfunction, unmTo be transferred to the repair rate of m states by n-state；
Step 7：The time-varying fault rate of transformer is modified according to the maintenance type of transformer, transformer maintenance strategy
Selection and transformer state transfer rate are in proportion function relation：
<mrow>
<msub>
<mi>&lambda;</mi>
<mrow>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mi>&lambda;</mi>
<mi>a</mi>
</mfrac>
<mo>+</mo>
<mi>b</mi>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
Wherein, λnewState transfer rate after for transformer maintenance, λ are state transfer rate before transformer maintenance, work as transformer
Maintenance type a values are 1 when being minimal maintenance, when the maintenance type of transformer is imperfect repair, a values are 2, work as change
A values are that 3, b is modifying factor when the maintenance type of depressor is complete maintenance policy, and b is according to the running status and dimension of transformer
Tactful value is repaiied, transformer is through maintenance failure rate：
P (t, λnew)new=kP (t, λ) (6),
Wherein, P (t, λnew)newFor transformer, time-varying fault rate, P (t, λ) are transformer maintenance prior fault rate after maintenance, k
For proportionality coefficient, k is according to the running status and maintenance policy value of transformer；
Step 8：The reliability of transformer is sought according to the fault rate of step 7：
<mrow>
<mi>R</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>exp</mi>
<mo>&lsqb;</mo>
<mo>-</mo>
<msubsup>
<mo>&Integral;</mo>
<mn>0</mn>
<mi>t</mi>
</msubsup>
<mi>P</mi>
<mrow>
<mo>(</mo>
<mi>u</mi>
<mo>)</mo>
</mrow>
<mi>d</mi>
<mi>u</mi>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Step 9：Transformer fault rate and reliability prediction curve are exported, in conjunction with transformer reality operation conditions, to transformer
Next step running status carries out analysis and proposes that maintenance is suggested.
2. the inside transformer Hidden fault rate Forecasting Methodology based on birth and death process, its feature exist as claimed in claim 1
In：4 kinds of states of transformer history run in the step 2 include：Kilter, alarm condition, precarious position and failure shape
State.
3. the inside transformer Hidden fault rate Forecasting Methodology based on birth and death process, its feature exist as claimed in claim 1
In：The transformer time-varying state transfer rate takes according to Transformer Winding temperature history and current winding temperature data
Value, when following Transformer Winding temperature data is predictable, transformer fault rate model can carry out fault rate prediction.
4. the inside transformer Hidden fault rate Forecasting Methodology based on birth and death process, its feature exist as claimed in claim 1
In：Transformer fault rate is modeled using birth and death process theory in the step 6.
5. the inside transformer Hidden fault rate Forecasting Methodology based on birth and death process, its feature exist as claimed in claim 2
In：The changing value of fault rate after different maintenance policies is selected in the step 7 according to transformer, to formula (5) and formula (6)
Middle coefficient a, b and k carry out value, and by a, b and k numerical applications in the amendment of next fault rate, quantify transformer with this
The different influences to transformer fault rate of maintenance policy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710934717.7A CN107607820B (en) | 2017-10-10 | 2017-10-10 | method for predicting latent fault rate in transformer based on life-kill process |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710934717.7A CN107607820B (en) | 2017-10-10 | 2017-10-10 | method for predicting latent fault rate in transformer based on life-kill process |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107607820A true CN107607820A (en) | 2018-01-19 |
| CN107607820B CN107607820B (en) | 2020-01-31 |
Family
ID=61068400
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710934717.7A Expired - Fee Related CN107607820B (en) | 2017-10-10 | 2017-10-10 | method for predicting latent fault rate in transformer based on life-kill process |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107607820B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111311171A (en) * | 2019-12-06 | 2020-06-19 | 江西洪都航空工业集团有限责任公司 | Maintainability distribution method based on maintenance activities and correction weighting coefficients |
| CN112345678A (en) * | 2020-11-10 | 2021-02-09 | 重庆大学 | Transformer fault rate prediction model obtaining method and system and readable storage medium |
| CN112528510A (en) * | 2020-12-17 | 2021-03-19 | 中国航空工业集团公司成都飞机设计研究所 | Method for predicting repairable aviation material spare parts based on life-extinction process model |
| CN112633611A (en) * | 2021-01-07 | 2021-04-09 | 中海石油(中国)有限公司 | Submarine cable state maintenance strategy optimization method and system based on big data analysis |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005331415A (en) * | 2004-05-20 | 2005-12-02 | Toenec Corp | Insulation diagnostic system |
| JP4096340B2 (en) * | 2005-03-11 | 2008-06-04 | 株式会社トーエネック | Electrical facility diagnostic system |
| CN104091238A (en) * | 2014-07-11 | 2014-10-08 | 国家电网公司 | Method for analyzing and evaluating electricity utilization safety risk evolution of user in severe weather |
| CN104897784A (en) * | 2015-03-15 | 2015-09-09 | 国家电网公司 | Transformer fault diagnosis method based on coupled hidden Markov model |
| CN105717382A (en) * | 2014-12-05 | 2016-06-29 | 青岛泰威机床有限公司 | Service life pre-estimate system for transformer |
| CN105891684A (en) * | 2016-01-13 | 2016-08-24 | 武汉大学 | Gas insulated switchgear insulation state evaluation index system construction method |
| CN107194476A (en) * | 2017-05-26 | 2017-09-22 | 中国南方电网有限责任公司超高压输电公司天生桥局 | The pre- anti-aging maintenance policy formulating method of transformer based on semi-Markov chain |
-
2017
- 2017-10-10 CN CN201710934717.7A patent/CN107607820B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005331415A (en) * | 2004-05-20 | 2005-12-02 | Toenec Corp | Insulation diagnostic system |
| JP4096340B2 (en) * | 2005-03-11 | 2008-06-04 | 株式会社トーエネック | Electrical facility diagnostic system |
| CN104091238A (en) * | 2014-07-11 | 2014-10-08 | 国家电网公司 | Method for analyzing and evaluating electricity utilization safety risk evolution of user in severe weather |
| CN105717382A (en) * | 2014-12-05 | 2016-06-29 | 青岛泰威机床有限公司 | Service life pre-estimate system for transformer |
| CN104897784A (en) * | 2015-03-15 | 2015-09-09 | 国家电网公司 | Transformer fault diagnosis method based on coupled hidden Markov model |
| CN105891684A (en) * | 2016-01-13 | 2016-08-24 | 武汉大学 | Gas insulated switchgear insulation state evaluation index system construction method |
| CN107194476A (en) * | 2017-05-26 | 2017-09-22 | 中国南方电网有限责任公司超高压输电公司天生桥局 | The pre- anti-aging maintenance policy formulating method of transformer based on semi-Markov chain |
Non-Patent Citations (2)
| Title |
|---|
| YONGLIANG LIANG ETC.: "《EWDGA and Markov process based failure rate estimation of transformer internal latent fault》", 《IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES》 * |
| 赵婉芳等: "《基于油色谱监测数据的变压器动态可靠性分析》", 《电力系统自动化》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111311171A (en) * | 2019-12-06 | 2020-06-19 | 江西洪都航空工业集团有限责任公司 | Maintainability distribution method based on maintenance activities and correction weighting coefficients |
| CN112345678A (en) * | 2020-11-10 | 2021-02-09 | 重庆大学 | Transformer fault rate prediction model obtaining method and system and readable storage medium |
| CN112345678B (en) * | 2020-11-10 | 2022-03-01 | 重庆大学 | Transformer fault rate prediction model obtaining method and system and readable storage medium |
| CN112528510A (en) * | 2020-12-17 | 2021-03-19 | 中国航空工业集团公司成都飞机设计研究所 | Method for predicting repairable aviation material spare parts based on life-extinction process model |
| CN112633611A (en) * | 2021-01-07 | 2021-04-09 | 中海石油(中国)有限公司 | Submarine cable state maintenance strategy optimization method and system based on big data analysis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107607820B (en) | 2020-01-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104484723B (en) | A kind of power transformer Forecast of Economic Life method based on lifetime data | |
| Ke et al. | Cloud computing platform for real-time measurement and verification of energy performance | |
| CN107607820A (en) | A kind of inside transformer Hidden fault rate Forecasting Methodology based on birth and death process | |
| CN105631578A (en) | Risk assessment-orientated modeling method of power transmission and transformation equipment failure probability model | |
| CN103699668B (en) | Electric distribution network electrical equipment assembled state appraisal procedure based on data section uniformity | |
| CN104680254B (en) | A kind of power network planning scheme method for optimizing based on integrated cost model | |
| CN102930344A (en) | Method for forecasting ultra-short term bus load based on load trend changes | |
| Che et al. | Vulnerability assessment of urban power grid based on combination evaluation | |
| CN103048573A (en) | Method and device for electric power system operation risk assessment | |
| CN108629491A (en) | A kind of change of current change repair quality comprehensive estimation method | |
| CN110110933A (en) | A kind of maintenance circle optimization method of intelligent substation protection system | |
| CN108985546A (en) | A kind of power transformer time-varying stoppage in transit methods of risk assessment considering weather conditions | |
| Fan et al. | Human factors’ complexity measurement of human‐based station of assembly line | |
| Huang et al. | Modeling the aging-dependent reliability of transformers considering the individualized aging threshold and lifetime | |
| CN105427005A (en) | Operation risk assessment method of wind power station | |
| Pan et al. | Simulation on the effectiveness of carbon emission trading policy: A system dynamics approach | |
| CN114626769B (en) | Operation and maintenance method and system for capacitor voltage transformer | |
| CN110880055A (en) | Building intelligent ammeter system | |
| CN109559019A (en) | A kind of Electrical Power System Dynamic safety evaluation method based on risk index | |
| Jin et al. | Reliability analysis of the uncertain fractional‐order dynamic system with state constraint | |
| Li et al. | Forecasting China’s CO 2 emissions for energy consumption based on cointegration approach | |
| CN109522615B (en) | Method and device for evaluating zero-sequence inrush current tolerance of relay protection of power system | |
| CN114330773A (en) | Evaluation method and system for time-varying fault probability of power cable | |
| CN105242129A (en) | Fault probability determination method for transformer winding | |
| CN105741184A (en) | Transformer state evaluation method and apparatus |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200131 Termination date: 20211010 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |