CN107607820B - method for predicting latent fault rate in transformer based on life-kill process - Google Patents
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Abstract
The invention discloses an method for predicting latent fault rate in a transformer based on a extinction process, which comprises the following steps of 1, obtaining historical data of residence time of a transformer state and winding hot point temperature data, detecting gas content in transformer oil, 2, calculating transfer rate lambda of 4 transformer historical operation states, 3, calculating an aging acceleration factor of the transformer according to the winding hot point temperature data, 4, calculating a historical average acceleration factor, 5, solving the transfer rate of a time-varying state of the transformer, 6, writing an extinction process equation set of the transformer state transfer according to a transformer state transfer mechanism and the operation historical data, calculating the time-varying fault rate of the transformer in an m operation state, 7, correcting the time-varying fault rate according to the maintenance type of the transformer, 8, calculating the reliability of the transformer according to the fault rate of the step 7, 9, outputting a transformer fault rate and reliability prediction curve, and analyzing the operation state of the transformer in the next steps by combining the actual operation state of the transformer to provide a maintenance suggestion.
Description
Technical Field
The invention relates to the technical field of transformer fault rate prediction, in particular to a method for predicting the latent fault rate in a transformer.
Background
Transformer faults are divided into external faults and internal faults, wherein the internal faults are mainly caused by aging of internal devices of the transformer. Inside the transformer, the hot spot temperature increases, which accelerates the aging of internal components, thereby increasing the failure rate of the transformer. The temperature rise of the hot spot of the transformer can lead to the accelerated generation of gas in the insulating oil of the transformer, so the gas content in the oil can reflect the health condition of the transformer.
The latent fault rate inside the transformer is usually calculated according to the gas content in the transformer oil. Common failure rate solving methods are classified into an analytic method and a simulation method.
The analytical method has the advantages of simplicity, easiness in mastering and no need of knowing the state transition principle of the transformer, and the condition that the transition among the states of the transformer has no aftereffect before the failure rate is solved, but the analytical method is not easy to comprehensively consider the influence of the failure rate of the transformer due to various influence factors, and has corresponding limiting conditions when the relevant theory is used, for example, the Markov process requires that the state retention time of the transformer is required to be subjected to exponential distribution, but the state retention time of the transformer is not and meets the requirement in the real operation of the transformer.
The simulation method is characterized in that scene simulation is carried out on the operation process of the transformer, and the failure rate of the transformer is solved by using simulation data. A typical example is a transformer time-varying shutdown model considering on-line monitoring information, which uses an acceptance-Rejection sampling method to obtain the residence time of each state, generates sampling samples, and quantifies the influence of different influencing factors on the fault rate of the transformer by changing the parameters of a proportional model.
In addition, in the traditional method, the influence of the operation age of the transformer, the maintenance strategy, the operation load and the like on the fault probability of the transformer is mostly not considered when the fault probability of the transformer is calculated, and after the transformer is subjected to oil purification treatment, the fault probability of the transformer is predicted only according to the gas content in oil, so that the obtained result is obviously inaccurate. The influence of the operation history of the transformer on the fault rate of the transformer at the future moment cannot be quantified in some transformer fault rate models.
In addition, when the maintenance strategy of the transformer is made, the failure rate of the transformer needs to be predicted for a long time, but most of the traditional methods cannot meet the requirements.
It is therefore desirable to have methods of predicting the potential failure rate within a transformer that overcome or at least alleviate the problems of the prior art.
Disclosure of Invention
The invention aims to provide methods for predicting the latent fault rate in a transformer, which are used for accurately solving the operation fault rate of the transformer, fully considering acceleration factors influencing the fault of the transformer and solving the problem that the fault rate of the transformer is difficult to solve by using an analytical method.
The invention provides an method for predicting the latent fault rate in a transformer based on a live-out process, which comprises the following steps:
, acquiring historical data of the residence time of the transformer state and hot spot temperature data of a transformer winding, and detecting the gas content in the transformer oil;
step two: carrying out statistical analysis on the residence time of 4 states in the historical operation of the transformer, and respectively calculating the transfer rate lambda of the 4 states;
step three: calculating the aging acceleration factor of the transformer according to the hot spot temperature data of the transformer winding:
wherein, FAAIs a transformer aging acceleration factor, B is a constant which is related to the load factor and the ambient temperature,
the reference value is the winding hot point temperature reference value, and theta is the transformer winding hot point temperature at the current moment;
step four: and (3) representing the influence of the operation history of the transformer on the future fault rate of the transformer through a historical average acceleration factor, and calculating the historical average acceleration factor:
wherein, FAAlossAverage value of the historical accelerated aging factor of the transformer1And t2Respectively representing the transformer operation time and the current time;
step five: solving the transformer time-varying state transition rate:
λ′mn＝(FAA+FAAloss)λmn(3)，
wherein λ ismnThe transfer rate of the transformer from the m state to the n state;
step six: according to the transformer state transfer mechanism and the operation historical data, writing a life-extinction process equation set of the transformer state transfer, and solving the time-varying fault rate of the transformer in the m operation state
Wherein, PmIs the time-varying failure rate of the transformer in m-state, m ═ 1, 2 or 3, λ'mnFor a transformer is composed ofTime-varying state transition rate for m-state to n-state, n-4 being a fault state, unmThe repair rate for the transition from the n state to the m state;
step seven: correcting the time-varying fault rate of the transformer according to the maintenance type of the transformer, wherein the selection of the maintenance strategy of the transformer and the state transfer rate of the transformer are in a proportional function relationship:
wherein λ isnewFor the state transition rate after the maintenance of the transformer, lambda is the state transition rate before the maintenance of the transformer, a takes a value of 1 when the maintenance type of the transformer is minimum maintenance, a takes a value of 2 when the maintenance type of the transformer is incomplete maintenance, a takes a value of 3 when the maintenance type of the transformer is complete maintenance strategy, b is a correction factor, b takes a value according to the operation state and the maintenance strategy of the transformer, and the maintenance fault rate of the transformer is as follows:
P(t,λnew)new＝kP(t,λ) (6)，
wherein, P (t, λ)new)newThe fault rate of the transformer is changed after maintenance, P (t, lambda) is the fault rate of the transformer before maintenance, k is a proportionality coefficient, and k is taken according to the running state and the maintenance strategy of the transformer;
step eight: and solving the reliability of the transformer according to the fault rate of the step seven:
and step nine, outputting a prediction curve of the fault rate and the reliability of the transformer, and analyzing the running state of the transformer in the step to provide a maintenance suggestion by combining the actual running state of the transformer.
Preferably, the historical operation of the transformer in the second step includes 4 states: good status, warning status, dangerous status, and fault status.
Preferably, the transformer time-varying state transition rate is valued according to historical transformer winding temperature data and current winding temperature data, and when the future transformer winding temperature data is predictable, the transformer fault rate model can predict the fault rate.
Preferably, in the sixth step, a birth-extinction process theory is adopted to model the fault rate of the transformer.
Preferably, in the seventh step, the values of the coefficients a, b, and k in the formulas (5) and (6) are taken according to the change value of the fault rate after the transformer selects different maintenance strategies, and the values of a, b, and k are applied to the correction of the fault rate next time, so as to quantify the influence of the difference of the transformer maintenance strategies on the fault rate of the transformer.
The invention discloses transformer internal latent fault rate prediction methods based on a killing process, which can reasonably and comprehensively consider the influence of the transformer operation history and the current operation environment on the transformer fault rate, and improve the accuracy of fault rate prediction at the future time by adopting the state transition rate of a time-varying transformer, so that the reliability analysis and calculation result is more reasonable.
Drawings
FIG. 1 is a schematic diagram of a state transition of a transformer;
FIG. 2 is a graph illustrating the effect of different maintenance strategies on the failure rate of a transformer;
FIG. 3 is a diagram illustrating the volume content of gas in transformer oil;
FIG. 4 is a graph of a fault rate simulation of a transformer in a good condition;
FIG. 5 is a fault rate simulation graph of a transformer in a warning state;
FIG. 6 is a graph of a simulation of the fault rate during the transformer operating cycle;
fig. 7 is a flowchart of a method for predicting the latent fault rate in the transformer based on the life-time process.
Detailed Description
For purposes of making the embodiments of the present invention more apparent and the advantages thereof, reference is now made to the drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functionality throughout, and wherein the depicted embodiment is part, but not all, of an embodiment of the present invention.
The method for predicting the latent fault rate in the transformer based on the extinction process is described according to the attached figures 1-7 in the specification. The fault rate of the transformer is used for representing the health condition of the transformer, and when the number of times of the transformer faults in unit time is more, the running reliability of the transformer is lower, and the economic value is lower. The transformer failure rate is defined as a probability function of the probability distribution f (T) of the life T:
the failure rate of the present invention is based on the transformer state transition rate, so the failure rate of the present invention transformer is defined as the number of failures per week.
The transformer state transition rate is the amount of change in transition probability per unit time, while the transition probability from the operating state to the fault state is conditional probabilities, and the formula (8) probability function is the amount of change in the conditional probability per unit time, so both are in nature.
The health of the transformer can be determined by periodically obtaining the gas content in the transformer oil. According to IEEE Std C57.104-1991, the gases in transformer oil used to measure the health status of transformers are: h2，C2H4，C2H2，CH4，C2H6And CO. Real-time transformer operation based on volume fraction of dissolved gas in transformer oilThe line states are divided into four operation states of "good state", "warning state", "dangerous state", and "failure state", and when there are gas volume fractions in a worse state at the time of the transformer state judgment, the transformer is considered to be operated in that state.
When the transformer is operated at a certain state, the accumulation of the elapsed time goes to the lower operation state or the health of the transformer is improved by changing the operation condition of the transformer.
When the current flowing through the transformer is overlarge, the temperature of a hot spot of the transformer is increased, so that the gas production rate in the transformer is increased, and when the current flowing through the transformer is reduced by a certain measure, the heat generated by the transformer can be balanced with the heat dissipation, so that the temperature of the hot spot is reduced, and the health state of the transformer is optimized.
As shown in FIG. 1, the transformer state transition diagram is provided with k transformer statistical data, yijDenotes the dwell time, λ, of the j-th transformer in the i stateijFor the transition rate of state i to state j, the repair rate uijThe reciprocal of the repair time from state i to state j, then:
the aging of the transformer, not only related to the operation age, but also related to the manufacturing and installation quality, and the operation maintenance, is variables capable of comprehensively reflecting the real health state of the transformer, the transformer fault rate is related to the aging degree of the transformer, and the transformer aging degree can influence the capacity of the transformer for bearing short circuit and overload.
The transformer aging is long-term processes, the aging degree of the transformer aging is greatly related to the running time, average load and environmental quality of the transformer, the influence of the factors on the aging degree of the transformer can be reflected by the hot spot temperature of the transformer winding, and an accelerated aging factor F is introduced in the prediction of the risk probability of the transformer in the futureEQA。
IEEE C57.91-1995 discusses in detail the relationship of transformer insulation life to winding hot spot temperature:
in the formula LpuIs the per unit value of the insulation life of the transformer; A. b is a constant related to the load factor and the ambient temperature; theta is the winding hot spot temperature.
Accelerated aging factor FAAComprises the following steps:
in the formula
And the reference value is the winding hot spot temperature.
Loss value L of transformer lifelossComprises the following steps:
in the formula t1Putting the transformer into operation; t is t2The transformer is operated until the present moment.
Because the operating environments of the transformer have a large difference in summer and winter, the influence of the two environments on the aging of the transformer needs to be considered when the life loss value of the transformer is obtained.
When transformingAfter the insulating paper of the device is thermally upgraded, the reference value of the hot spot temperature is 110 ℃, and the following is obtained by referring to IEEE: a is 9.8 × 10-18,B＝15000。
Since the actual loss of life value of a transformer is the integral of the historical acceleration factor of the transformer over the operating time, the effect of the transformer operating history on the future failure rate of the transformer can be expressed by the historical average acceleration factor, as shown in equation (12):
the fault rate of the invention is established on the basis of the state transition rate of the transformer, so that the influence of the accelerated aging factor on the fault rate can be converted into the influence on the state transition rate of the transformer, and the time-varying state transition rate of the transformer can be obtained as follows:
λ′mn＝(FAA+FAAloss)λmn(13)
λmnis the transfer rate of the transformer from the m-state to the n-state.
Obtaining the time-varying fault rate of the transformer based on the life-extinction process
The influence of the current health condition and the real environment of the transformer on the fault rate of the transformer is considered, and the time-varying state transition rate lambda 'of the transformer can be obtained by using the formula (3)'i,i+1A transformer time-varying state transfer rate matrix Q can be established:
starting from the state i after the operation state i of the transformer is determined according to the gas content in the transformer oil, the next step can only reach the adjacent state i +1 or i-1, and when the state of the transformer is 1, the next step can only enter the state 2 or stay in the state 1, and when the transformer is in the state i at the moment t, the probability of transferring to the state i +1 in the time (t, t + h) is lambda'i,i+1h + o (h), the probability of transferring to the i-1 state is ui,i-1h + o (h), in (t, t + h)Probability o (h) that more than transitions occur.
When xt represents the transformer state at time t, xtT ≧ 0} constituting random processes, Pij(t) represents the transformer state transition probability, then when (h → 0), there is the following probability formula:
the probability P of the transformer being in the j state is derived belowj(t), namely:
Pj(t)＝P(xt＝j) (15)
from the above analysis, it can be seen that the (t + h) time can be obtained only through the following three processes, and the transformer is in the j state:
(1) the transformer is in a j state at the time t, and the state of the transformer is not transferred within the time (t, t + h);
(2) the transformer is in a (j-1) or (j +1) state at the time t, and then is transferred to a j state;
(3) in the time (t, t + h), times of transition processes occur, and finally the transition is to the j state, and the probability of the occurrence of the condition is o (h);
from the above analysis, the following probability formula can be derived:
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)
moving Pj (t) in equation (16) to the left, and dividing both sides by h, and let (h → 0) 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)
therefore, according to equation (17), there is the following equation:
P’0(t)＝-b0P0(t)+a1P1(t) (18)
because the initial state i of the transformer can be known through gas analysis in the transformer oil, the initial conditions are introduced:
Pi(0)＝1,Pj(0)＝0,j≠i (19)
the time domain formula of the fault probability of the transformer at the time t can be obtained by combining the formulas (17), (18) and (19):
in the formula: pmn(t) is the probability of transition of the m state to the n state;
in addition, the transition rate of the adjacent states of the transformer can be obtained through historical data, but the transition rate between the non-adjacent states is not good, so the average time for transition from the state k to the state n is given as:
after the fault rate of the transformer is obtained according to the method, the obtained fault rate is needed to be used for analyzing the running state of the transformer, so that a basis is provided for formulating transformer state maintenance.
As shown in fig. 2, the maintenance of the transformer is divided into incomplete maintenance, complete maintenance, and minimum maintenance, and if the transformer needs to be maintained, the time-varying fault rate of the transformer is corrected according to a maintenance strategy.
As shown in fig. 3, the transformer oil gas content is analyzed based on the time domain model of the transformer failure rate of the extinction process. (gas total amount statistics 2500 days, each gas content statistics 1750 days)
Through data statistics, the transfer rate (unit: times/week) of each state of the transformer is obtained as follows: lambda [ alpha ]12＝0.0083，λ23＝0.0085，λ340.0193, the corresponding repair rate was: u. of21＝0.2863，u32＝0.5120，u43At the same time, λ is obtained from equation (18) at 0.854314＝8.686×10-7，u41＝0.01252，λ24＝3.04×10-5，u42＝0.032。
(1) According to the historical data of the transformer, the transformer is operated for 2100 days to carry out oil purification treatment, and in the process of solving the fault rate of the transformer, if only the gas content in the transformer oil is considered, but the influence of the aging of the transformer on the fault rate is not considered, the obtained fault rate is not in line with the actual situation.
(2) The transformer is in a good state 150 days before operation, the total gas amount is increased quickly, and for convenience of expressing an acceleration factor, F is equal to FAA+FAAlossThen the good-condition failure rate time-domain equation is, according to equation (20):
P1＝exp[-7×10-5×F(1-e-0.01252t)][7×10-5×F(1-e-0.01252t)]
as shown in fig. 4, in the fault rate simulation curves in the good state when the acceleration factors are 2, 3, and 4, respectively, the influence of different acceleration factors on the fault rate of the transformer is large, and therefore, the influence of the actual health condition and the environmental condition of the transformer on the fault rate of the transformer must be considered.
(3) At 1000 days of operation, the transformer enters a warning state. After entering the warning state, the gas content in the oil increases slightly faster than in the good state, and when entering the dangerous state, the gas content in the transformer oil increases speed obviously, so when the transformer enters the dangerous state, the related maintenance is required to be carried out in time. The time domain formula of the fault rate of the transformer entering the warning state is as follows:
P2＝exp[-9.5×10-4×F(1-e-0.032t)]×[9.5×10-4×F(1-e-0.032t)]，
fig. 5 shows simulation curves of different acceleration factors.
(4) The transformer enters a dangerous state when running for 1400 days. When the transformer is operated for 2100 days, the transformer does not reach a fault state, but the performance of the transformer is reduced due to aging of the transformer, and the fault rate is about 0.06, so that the transformer needs to be subjected to oil purification treatment.
As shown in fig. 6, the overall time-varying fault probability curve of the transformer, as can be seen from the above analysis of the fault rate of the transformer, before the dangerous state, the internal fault of the transformer also conforms to -like rules of development (occurrence, development and maturity) of things, and the fault probability conforms to the Logistic model proposed by belgium mathematicians in 1938, that is, the curve increases slowly, then increases rapidly, and then becomes stable.
Finally, it should be pointed out that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (5)
1, a method for predicting the latent fault rate in the transformer based on the life-kill process, which is characterized by comprising the following steps:
, acquiring historical data of the residence time of the transformer state and hot spot temperature data of a transformer winding, and detecting the gas content in the transformer oil;
step two: carrying out statistical analysis on the residence time of 4 states in the historical operation of the transformer, and respectively calculating the transfer rate lambda of the 4 states;
step three: calculating the aging acceleration factor of the transformer according to the hot spot temperature data of the transformer winding:
wherein, FAAIs a transformer aging acceleration factor, B is a constant which is related to the load factor and the ambient temperature,
is a winding hot point temperature reference value, and theta is the current transformerWinding hot spot temperature;
step four: and (3) representing the influence of the operation history of the transformer on the future fault rate of the transformer through a historical average acceleration factor, and calculating the historical average acceleration factor:
wherein, FAAlossAverage value of the historical accelerated aging factor of the transformer1And t2Respectively representing the transformer operation time and the current time;
step five: solving the transformer time-varying state transition rate:
λ′mn＝(FAA+FAAloss)λmn(3)，
wherein λ ismnThe transfer rate of the transformer from the m state to the n state;
step six: according to the transformer state transfer mechanism and the operation historical data, writing a life-extinction process equation set of the transformer state transfer, and solving the time-varying fault rate of the transformer in the m operation state
Wherein, PmIs the time-varying failure rate of the transformer in m-state, m ═ 1, 2 or 3, λ'mnFor the time-varying state transition rate of the transformer from the m-state to the n-state, n-4 being the fault state, unmThe repair rate for the transition from the n state to the m state;
step seven: correcting the time-varying fault rate of the transformer according to the maintenance type of the transformer, wherein the selection of the maintenance strategy of the transformer and the state transfer rate of the transformer are in a proportional function relationship:
wherein λ isnewFor the state transition rate after the transformer has been serviced,lambda is the state transfer rate before the maintenance of the transformer, a takes a value of 1 when the maintenance type of the transformer is the minimum maintenance, a takes a value of 2 when the maintenance type of the transformer is the incomplete maintenance, a takes a value of 3 when the maintenance type of the transformer is the complete maintenance strategy, b is a correction factor, b takes a value according to the operation state and the maintenance strategy of the transformer, and the maintenance failure rate of the transformer is as follows:
P(t,λnew)new＝kP(t,λ) (6)，
wherein, P (t, λ)new)newThe fault rate of the transformer is changed after maintenance, P (t, lambda) is the fault rate of the transformer before maintenance, k is a proportionality coefficient, and k is taken according to the running state and the maintenance strategy of the transformer;
step eight: and solving the reliability of the transformer according to the fault rate of the step seven:
p (u) is the failure rate of the transformer calculated when the transformer runs to the moment u, and u represents the time t;
and step nine, outputting a prediction curve of the fault rate and the reliability of the transformer, and analyzing the running state of the transformer in the step to provide a maintenance suggestion by combining the actual running state of the transformer.
2. The method for predicting the latent fault rate inside the transformer based on the extinction process according to claim 1, wherein: the 4 states of the transformer historical operation in the second step comprise: good status, warning status, dangerous status, and fault status.
3. The method for predicting the latent fault rate inside the transformer based on the extinction process according to claim 1, wherein: the transformer time-varying state transfer rate is valued according to historical transformer winding temperature data and current winding temperature data, and when the future transformer winding temperature data is predictable, the transformer fault rate model can predict the fault rate.
4. The method for predicting the latent fault rate inside the transformer based on the extinction process according to claim 1, wherein: and in the sixth step, a life-extinction process theory is adopted to model the fault rate of the transformer.
5. The method for predicting the latent fault rate inside the transformer based on the extinction process according to claim 2, wherein: and seventhly, evaluating coefficients a, b and k in the formulas (5) and (6) according to the change value of the fault rate after different maintenance strategies are selected for the transformer, and applying the values of a, b and k in the next fault rate correction so as to quantify the influence of the different maintenance strategies for the transformer on the fault rate of the transformer.
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| 《EWDGA and Markov process based failure rate estimation of transformer internal latent fault》;YongLiang Liang etc.;《IEEE PES Innovative Smart Grid Technologies》;20120917;第1-5页 * |
| 《基于油色谱监测数据的变压器动态可靠性分析》;赵婉芳等;《电力系统自动化》;20141125;第38卷(第22期);第38-49页 * |
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| CN107607820A (en) | 2018-01-19 |
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