CN106096214B - A kind of switchgear Fault Fuzzy Diagnostic method based on indirect thermometric mode - Google Patents
A kind of switchgear Fault Fuzzy Diagnostic method based on indirect thermometric mode Download PDFInfo
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Abstract
The invention discloses a kind of switchgear Fault Fuzzy Diagnostic methods based on indirect thermometric mode.The present invention is further optimized on the basis of existing switchgear indirect thermometric method for diagnosing faults, a variety of different indirect thermometric method for diagnosing faults of integrated use, to make multinomial different fault diagnosis index.According to the source of ambiguity and state, using fuzzy diagnosis theory and fuzzy algorithmic approach, the fuzzy membership function of various diagnosis indexes is worked out, and then construct subordinated-degree matrix relevant to malfunction grade, fuzzy matrix operation by correlation obtains the diagnosis of the malfunction grade of relatively high confidence level.The present invention keeps the conclusion of diagnosis more acurrate, and it is uncertain to avoid bring conclusion due to a kind of ambiguity of any single diagnosis index.
Description
Technical field
The present invention relates to power switch cabinet fault diagnosis technology field, more particularly to a kind of based on indirect thermometric mode
Switchgear Fault Fuzzy Diagnostic method.
Background technique
Switchgear be responsible in the power system close and disconnect power circuit, protection system safety dual function, with
Rapid development of the electric system towards high voltage, large-sized unit, large capacity, power grid is growing and Unattended substation pipe
The safe operation of the promotion and popularization of reason mode and comprehensive automation, switchgear is more and more important.Blade contacts, electric power in switchgear
When the junction contacts of cable incoming-outgoing wire are bad, contact resistance increases, and fever phenomenon can be generated when load current flows through, and overheats meeting
The mechanical strength of metal material is caused to decline, insulating materials aging simultaneously may cause breakdown and form accident.Measurement and monitoring switch
State of temperature in cabinet is one of the effective means for diagnosing switch cabinet equipment malfunction.
For the most of switch cabinet equipment to put into operation at present, there is no fitting temperature monitoring function, is sent out extremely in equipment fault
After hot temperature rise, administrative staff can not be known.The temperature monitoring and diagnostic method of existing switchgear are mostly using electric terminal
The direct temperature measurement and temperature threshold diagnostic method at position.Such as: various contact temperature-measuring sensors, infrared temperature probe, light
Fine grating class temperature transducer etc..And electric terminal position to be installed by any temperature transducer in cabinet, all by many
Restrictive condition, such as: switchgear to have a power failure the limitation for installing temperature measuring equipment, the limitation of space structure in cabinet, electric clearance limit
System, sensor tolerance high pressure and the limitation of high-intensity magnetic field, the limitation of sensor heat-resisting ability, probe power and communication line cloth
The limitation of line, influence of wireless signal transmission shielding etc. so that the installation of switchgear temperature transducer and popularize it is difficult, at
This is very high.
The present inventor once disclosed the indirect thermometric fault diagnosis of switchgear four kinds of methods (application No. is
CN201610059632.4), proposing indirect thermometric is feasible fault diagnosis mode.But diagnostic method independent for four kinds
In, because environment, cabinet body structure, payload, load period variation etc. are because all there is certain diagnosis mould in each method
Paste property, any one individual diagnostic method can not all make accurate diagnosis to switchgear malfunction.
Summary of the invention
Object of the present invention is to overcome a series of problems existing in the prior art, provide a kind of based on indirect thermometric mode
Switchgear Fault Fuzzy Diagnostic method.The present invention is in the indirect thermometric method for diagnosing faults of existing switchgear
(CN201610059632.4) on the basis of, further optimized, a variety of different indirect thermometric fault diagnosises of integrated use
Method, to make multinomial different fault diagnosis index.According to the source of ambiguity and state, using fuzzy diagnosis theory
And fuzzy algorithmic approach, the fuzzy membership function of various diagnosis indexes is worked out, and then construct relevant to malfunction grade
Subordinated-degree matrix, the fuzzy matrix operation by correlation obtain the diagnosis of the malfunction grade of relatively high confidence level.
Form the switchgear Fault Fuzzy Diagnostic method based on indirect thermometric mode of complete set.This method ratio is used alone any
A diagnostic method conclusion is more acurrate, credible, reliable.A set of self-study mechanism is additionally provided in this method, is suitable for using
The expert diagnostic system of this method.
In order to achieve the above object, the technical solution adopted in the present invention is as follows:
A kind of switchgear Fault Fuzzy Diagnostic method based on indirect thermometric mode comprising following steps:
Step A: construction fuzzy diagnosis matrix；With vector Y={ y1,y2,…,ynIndicate switchgear pyrogenicity malfunction
Set, wherein yjIndicate pyrogenicity malfunction, j=l, 2 ..., n, j is from 1 to n, yjFault level increase, malfunction is tight
Weight.Wherein, 0≤rij≤ 1,1≤i≤m, 1≤j≤n, xiIndicate diThe distribution power of item diagnosis index
Weight values, all total weighted values of diagnosis index are 1, whereinN indicates that fault level n is big
In the integer for being equal to 1, the grade quantity being arranged as the case may be, fault level is incremented by n size step by step from small to large；Use vector
D={ d1,d2,…,dmIndicate the set for causing every diagnosis index of pyrogenicity malfunction, wherein di(i=1,2 ..., m)
Indicate that diagnosis index, m indicate the diagnosis index type for being used for Fault Fuzzy Diagnostic；And using vector D as diagonal entry construction pair
Angle-style matrix E=diag (d1,d2,…,dm) it is used for calculating process, when diagnosing using respective items diagnosis index, enable di=1,
Otherwise di=0；With vector X={ x1,x2,…,xmIndicate the weight sets that every diagnosis index is occupied in total diagnosis.
Step B: according to the source of ambiguity and the preset physical model of combinations of states, to the malfunction y in vector Yj
(j=l, 2 ..., n) calculates every diagnosis index, and according to given diagnostic criteria, makes the judge of fault level；It calculates
And determine every diagnosis index diThe confidence level r of (i=1,2 ..., m) to malfunctionij；The malfunction of n grade corresponds to m
The evaluate collection of a diagnosis index just constitutes fuzzy diagnosis matrix R, as follows:
Wherein, 0≤rij≤ 1,1≤i≤m, 1≤j≤n, R indicate the fuzzy relation of vector D to vector Y；Wherein, the mould
The source of paste property and state ambiguity be when referring to calculating or obtaining the diagnosis index, based on physical state and physical parameter etc. no
Be accurately (but rule of thumb or statistical data, be have existing for certain distribution probability, that is, it is certain a possibility that be correct
, that is, there is ambiguity.
Step C: the vector Y={ y after fuzzy operation Y=XER, after obtaining Fuzzy Processing1,y2,…,yn, it chooses
Select the maximum y of numerical valuejDiagnosis as final output.
Preferably, the vector Y={ y in step C, after Fuzzy Processing1,y2,…,ynIn, if there are two or two with
On identical maximum value, then select the maximum y of j valuejDiagnosis as final output.
Preferably, in step B, rijDetermination method are as follows: according to the source of the ambiguity of every diagnosis index and state, structure
Produce corresponding subordinating degree function.
It is further preferred that first item diagnosis index d1To confidence level r1jCalculating process it is as follows:
If generating heat unbalanced degree coefficient are as follows:Unbalanced degree distribution probability are as follows:Wherein θmaxFor the maximum temperature of device in actual switch cabinet, θ1(t) it is
Lump heat source temperature, p in calculated switchgear0For the central value for unbalanced degree coefficient distribution of generating heat, σ is the state system of distribution
Number, and θmax> θ1(t)；
Then subordinating degree function are as follows: And it combines
Following standard,
Obtain r1j=q1*μ1j, 0≤q1≤1。
It is further preferred that Section 2 diagnosis index d2To confidence level r2jCalculating process it is as follows:
If the confidence level of ambiguity condition 1 is q21；The confidence level of ambiguity condition 2 is q22, Section 2 diagnosis index d2's
Total confidence level is q2, then:
Then r2j=q2*μ2j, j=1,2,3,4；
Ambiguity condition 1: for two switch cabinet equipments, because of payload size difference, and the temperature difference of indirection point is brought；
Ambiguity condition 2: it for two switch cabinet equipments, because local environment is different or internal structure is variant, and brings
The temperature difference of indirection point；
If the no-load voltage ratio coefficient of two switchgear indirection point temperature rises are as follows:Wherein, Δ θ2(t)HFor
High temperature rise indirection point；Δθ2(t)LFor low-temperature-rise indirection point；
The no-load voltage ratio coefficient δ diagnostic threshold standard of two switchgear indirection point temperature rises is as follows:
| δ | State description | Diagnosis |
| 0≤δ < 35% | Equipment is in health status | Normally |
| 35≤δ < 80% | The faulty hidden danger of equipment | Hidden danger |
| 80≤δ < 95% | Equipment has catastrophe failure possibility | Early warning |
| δ >=95% | Equipment has emergency possibility | Alarm |
Membership function expression formula is obtained in conjunction with the no-load voltage ratio coefficient δ diagnostic threshold standard of two switchgear indirection point temperature rises are as follows:
Wherein, E1 to E6 is from normally to the value of the no-load voltage ratio coefficient of alarm.
It is further preferred that Section 3 diagnosis index d3To confidence level r3jCalculating process it is as follows:
If diagnosis degree of membership of the switchgear Current Temperatures compared with the temperature at its preceding ten minute moment is μ3j, confidence level is original
Value is set as q3, 0≤q3≤1；
The credible fuzzy membership of synthesis are as follows: r3j=q3*μ3j, j=1,2,3,4；
If the history no-load voltage ratio coefficient of switchgear indirection point temperature value are as follows:Wherein, Δ θ2(t)H
At the time of to be currently diagnosed；Δθ2(t)LIt is history referring to the moment, and needs to meet: Δ θ2(t)H> Δ θ2(t)L；
Membership function expression formula are as follows:
Wherein, E1 to E6 is diagnosis from normally to the value of the history no-load voltage ratio coefficient of alarm.
It is further preferred that Section 3 diagnosis index d4To confidence level r4jCalculating process it is as follows:
If diagnosis degree of membership of the switchgear Current Temperatures compared with the temperature at its previous moment hour is μ4j, confidence level original
Initial value is set as q4, 0≤q4≤1；
The credible fuzzy membership of synthesis are as follows: r4j=q4*μ4j, j=1,2,3,4；
If the history no-load voltage ratio coefficient of switchgear indirection point temperature value are as follows:Wherein, Δ θ2(t)H
At the time of to be currently diagnosed；Δθ2(t)LIt is history referring to the moment, and needs to meet: Δ θ2(t)H> Δ θ2(t)L；
Membership function expression formula are as follows:
Wherein, E1 to E6 is diagnosis from normally to the value of the history no-load voltage ratio coefficient of alarm.
It is further preferred that Section 3 diagnosis index d5To confidence level r5jCalculating process it is as follows:
If switchgear Current Temperatures with its yesterday synchronization temperature compared with diagnosis degree of membership be μ5j, confidence level is original
Value is set as q5, 0≤q5≤1；
The credible fuzzy membership of synthesis are as follows: r5j=q5*μ5j, j=1,2,3,4；
If the history no-load voltage ratio coefficient of switchgear indirection point temperature value are as follows:Wherein, Δ θ2(t)H
At the time of to be currently diagnosed；Δθ2(t)LIt is history referring to the moment, and needs to meet: Δ θ2(t)H> Δ θ2(t)L；
Membership function expression formula are as follows:
Wherein, E1 to E6 is diagnosis from normally to the value of the history no-load voltage ratio coefficient of alarm.
It is further preferred that Section 3 diagnosis index d6To confidence level r6jCalculating process it is as follows:
If diagnosis degree of membership of the switchgear Current Temperatures compared with the temperature at its previous moment hour is μ6j, confidence level original
Initial value is set as q6, 0≤q6≤1；
The credible fuzzy membership of synthesis are as follows: r6j=q6*μ6j, j=1,2,3,4；
If the history no-load voltage ratio coefficient of switchgear indirection point temperature value are as follows:Wherein, Δ θ2(t)H
At the time of to be currently diagnosed；Δθ2(t)LIt is history referring to the moment, and needs to meet: Δ θ2(t)H> Δ θ2(t)L；
Membership function expression formula are as follows:
Wherein, E1 to E6 is diagnosis from normally to the value of the history no-load voltage ratio coefficient of alarm.
Preferably, there are also following steps after step C:
When the judgement conclusion of final output and actual malfunction are not inconsistent, weight sets is repaired using neural network
Just, makeover process is as follows:
It enables(y in formulaj)rFor desired output, yjFor reality output, bjIndicate output error,X is sought using following formulai:
For specific j, by rijIt sorts from large to small, selects the maximum r of numerical valueij, corresponding i value is i ', xi’(t+
1)=xi’(t)*abj, then other in addition to i
Wherein, xi(t) weighted value of moment t, x are indicatedi(t+1) it is obtained after indicating primary to the modified weight of moment t new
Weighted value, a is scale factor, meet 0≤a≤1.
Compared with prior art, the beneficial effects of the present invention are:
1, proposed by the present invention on the basis of existing switchgear indirect thermometric method for diagnosing faults, it carries out further excellent
Change, a variety of different indirect thermometric fault diagnosis indexs of integrated use, and connected applications fuzzy diagnosis theory and fuzzy algorithmic approach, opens
Have issued the calculation method and realization process of fuzzy diagnosis.Keep the conclusion of diagnosis more acurrate, avoids because of any single one kind
The ambiguity of diagnosis index and bring conclusion is uncertain.
2, switchgear thermometric Fuzzy Fault Diagnosis proposed by the present invention, a variety of relatively diagnosis indexes of integrated application, is indulged
Comparison between (history) and lateral (homotype cabinet body), extracts and is utilized the pyrogenicity type failure performance characteristic of a variety of recessiveness, increases
The detection property of the indirect thermometric fault diagnosis of switchgear is added.It can be found in the potential faults phase and the diagnosis quantified is provided
Information makes the method and system of the indirect thermometric of switchgear be suitable for the application of large area and popularize.
Detailed description of the invention
Fig. 1 is Fuzzy temperature (or temperature rise) threshold diagnostic method degree of membership distribution schematic diagram；
Fig. 2 is fuzzy temperature rise variation coefficient diagnosis degree of membership distribution schematic diagram；
Specific embodiment
In the following, being described further in conjunction with attached drawing and specific embodiment to the present invention:
The switchgear method for diagnosing faults based on online thermometric mode indirectly of the present embodiment, includes following three steps
A kind of (step A-C) and self-study mechanism (step D).
Step A: construction fuzzy diagnosis matrix；If the set Y={ y of the pyrogenicity malfunction of switchgear1,y2,…,yn, it enables
N=4, the i.e. quantity of expression fault level share 4 grades, and y1 indicates that equipment is in health status, and equipment operates normally；Y2 expression is set
Potential faults are had, but can maintain to operate normally；Y3 indicates that equipment has catastrophe failure possibility, issues overheat early warning；Y4 is indicated
Equipment has emergency possibility, issues temperature alarm.If causing every diagnosis index of pyrogenicity malfunction is symptom set, sign
Million collection vector D={ d1,d2,…,dmIndicate, wherein m indicates the diagnosis index type for being used for Fault Fuzzy Diagnostic, works as use
When this diagnosis index diagnoses, d is enabledi=1, otherwise di=0 (expression does not use)；And using vector D as diagonal entry construction pair
Angle-style matrix E=diag (d1,d2,…,dm) it is used for calculating process；What if every diagnosis index was occupied in total diagnosis
Weight sets, with vector X={ x1,x2,…,xmIndicate, wherein xiIndicate diThe distribution weighted value of item diagnosis index.All diagnosis
The total weighted value of index is 1, wherein
Step B: every diagnosis index is calculated and determined to malfunction confidence level rijMethod, currently preferred diagnosis
Index is divided into three categories totally 6 subitems.And respectively for each index give detailed diagnostic method, diagnosis calculation,
Diagnostic criteria, fuzzy membership calculation method, fault level algorithm.Below: first kind diagnosis index: with calculated biography
Diagnosis of the calorifics lump heat source temperature compared with level threshold value；Threshold diagnostic standard；The credible fuzzy membership calculating side of synthesis
Method；Fuzzy temperature (or temperature rise) threshold diagnostic method；Second class diagnosis index: two homotype cabinet bodies, the indirection point temperature of same position
Spend lateral comparison, the diagnosis of temperature rise no-load voltage ratio coefficient；The diagnosis calculation method and standard of indirect temperature measuring point across comparison；Fuzzy temperature rise becomes
Than coefficient diagnosis；Third class diagnosis index: the Current Temperatures of indirection point are compared with history different times temperature, temperature rise no-load voltage ratio system
Number diagnosis；The contents such as the method for fault level subordinated-degree matrix construction are described in detail respectively.
First kind diagnosis index: with diagnosis of the calculated thermal conduction study lump heat source temperature compared with level threshold value.
The ambiguity of diagnosis is: the maximum temperature of lump heat source temperature and device in practical cabinet in calculated switchgear
Difference be fringe.
The distribution probability for the unbalanced degree that generates heat is expressed first with subordinating degree function, is electrically connected according to switchgear
Characteristic, the probability distribution that fever lack of uniformity occurs are normal state profile, and fever is described by construction normal distyribution function not
Balanced ambiguity.
If generating heat unbalanced degree coefficient are as follows:Unbalanced degree distribution probability:Wherein θmaxFor the maximum temperature of device in practical cabinet, θ1It (t) is calculating
Lump heat source temperature, p in switchgear out0For the central value for unbalanced degree coefficient distribution of generating heat, σ is the coefficient of regime of distribution,
And θmax> θ1(t)。
The then degree of membership distribution probability (function) of maximum temperature are as follows:
The parameter experience original value of the degree of membership distribution function of optimization are as follows: p0=1.1, σ=12.In this way, can be according to upper
Face maximum temperature reliability distribution function can be calculated further according to maximum temperature diagnostic criteria, in different faults grade,
The specific value of failure degree of membership.
Threshold diagnostic standard:
About primary in reference " GBT11022-2011 common specifications for high-voltage switchgear and controlgear standards "
The standard of device temperature and temperature rise limit provides the one of optimization by taking certain equipment manufacturer's KYN28-1250 type high-tension switch cabinet as an example
The diagnostic threshold standard of secondary device temperature and temperature rise limit are as follows:
The credible fuzzy membership calculation method of synthesis:
It, be using the lump heat source temperature in certain heat transfer physical model calculating cabinet, because calculating in this diagnosis index
There are deviations for model and actual capabilities, so reliability coefficient is added in this conclusion, for judging the credible of computation model
Property.Confidence level setting method: the confidence level of the diagnosis index is set as q1, 0≤q1≤ 1,0 indicates negative, and 0.5 indicates uncertain, 1
Indicate affirmative；Confidence level original value is set as: q1=0.9.The credible fuzzy membership of synthesis are as follows: r1j=q1*μ1j。
Fuzzy temperature (or temperature rise) threshold diagnostic method:
Fuzzy temperature (or temperature rise) threshold diagnostic method is the improvement to threshold method, embodies lump heat source temperature and reality most
The uncertainty of difference between high-temperature is expressed using the mode of fuzzy mathematics, calculates different faults level status
A possibility that appearance, that is, degree of membership.
As shown in connection with fig. 1, the failure subordinating degree function expression formula of Fuzzy temperature (or temperature rise) threshold diagnostic method:
If, only need to be by the θ of above-mentioned expression formula using temperature rise as diagnostic criteria1(t) it is transformed to Δ θ1(t), it and replaces
Corresponding standard diagnostics threshold value.
Second class diagnosis index: two homotype switchgears, the indirection point temperature lateral comparison of same position, temperature rise no-load voltage ratio system
Number diagnosis:
Ambiguity condition 1: for two switchgears, because of payload size difference, and the temperature difference of indirection point is brought；
Ambiguity condition 2: for two switchgears, because local environment is (including position, ventilation, by other adjacent cabinet bodies
Influence, influenced by air-conditioning, climatic factor etc.) it is different；Or internal structure it is variant (including primary equipment topology layout, it is secondary to set
Influence, the influence of cabinet internal heater actual power of standby position and structure etc.) etc., and bring the temperature difference of indirection point.
The confidence level of above-mentioned ambiguity condition is subjected to mathematical expression respectively.Confidence level permission adjustable extent: 0~1,0
Indicate negative, 0.5 indicates uncertain, and 1 indicates affirmative.The confidence level of ambiguity condition 1 is q21If original value q21=0.5；Mould
The confidence level of paste property condition 2 is q22If original value q21=0.8.Total confidence level of second class diagnosis index is q2, then:
The credible fuzzy membership of synthesis: r is calculated by the across comparison method of the second class diagnosis index2j=q2*μ2j.Its
In, μ2jFor the degree of membership of the diagnosis index, the calculation method of degree of membership sees below described " fuzzy temperature rise no-load voltage ratio coefficient diagnosis
Method ".
The diagnosis calculation method and standard of indirect temperature measuring point across comparison:
The no-load voltage ratio coefficient of two cabinet body indirection point temperature rises are as follows:Wherein, Δ θ2(t)HFor high temperature
Rise indirection point；Δθ2(t)LFor low-temperature-rise indirection point.In actual diagnostic application, it is preferred that indoor for the same station to set
It is standby, it chooses indirection point temperature rise highest two different cabinet bodies and carries out diagnosis calculating.It is (former based on a kind of common fault rate
Reason: in the i.e. same station, in the same period, same failure occurs, the probability that usually only an equipment fault occurs is remote
The probability occurred greater than multiple devices simultaneous faults.)
Referring to " the power industry standard charging equipment infrared diagnosis technology application of the DL/T664-1999 People's Republic of China (PRC) is led
About " the relative temperature difference criterion of current caused hot type equipment " in then ", it is with certain equipment manufacturer's KYN28-1250 type high-tension switch cabinet
Example, provides the no-load voltage ratio coefficient δ diagnostic threshold standard of two switchgear indirection point temperature rises of optimization:
| δ | State description | Diagnosis |
| 0≤δ < 35% | Equipment is in health status | Normally |
| 35≤δ < 80% | The faulty hidden danger of equipment | Hidden danger |
| 80≤δ < 95% | Equipment has catastrophe failure possibility | Fault pre-alarming |
| δ >=95% | Equipment has emergency possibility | Fault alarm |
Fuzzy temperature rise no-load voltage ratio coefficient diagnosis:
Fuzzy temperature rise no-load voltage ratio coefficient diagnosis is exactly the improvement of threshold method in fact, is done at blurring to original diagnosis boundary threshold
Reason.As shown in Fig. 2, high-voltage switch cabinet body is possible to normal when δ is between E1 and E2, it is also possible to have hidden danger, at this moment incite somebody to action
To no-load voltage ratio coefficient substitute into subordinating degree function and calculated, obtain high-voltage switch cabinet body and belong to normal probability and to belong to the general of hidden danger
Rate.The subordinating degree function expression formula of the no-load voltage ratio coefficient δ of indirection point temperature rise are as follows:
Third class diagnosis index: the Current Temperatures of indirection point compared with history different times temperature, examine by temperature rise no-load voltage ratio coefficient
It is disconnected:
The ambiguity of diagnosis process is: for same equipment, in the different moments of history, payload size is approximately uniform
Possibility is larger, it is also possible to the ambiguity for bringing temperature rise different because of payload size difference.In order to increase the confidence level of diagnosis,
According to the periodic regularity of the periodic regularity of historical load variation and seasonal variations, period of history contrast points are selected.For example, right
Mr. Yu's electric system transformer and distribution power station, usually has:
1) variation of the flat peak valley in the flat peak in peak regularity in this way is presented in daily power load；
2) annual power load also has the rule for the circulation change that makes a clear distinction between the four seasons；
3) aging curve of electrical equipment is rendered as the parabola state of horizontal type；
4) electrical equipment pyrogenicity type failure generating process, temperature changing regularity present it is first gentle after ramp up heating to
On parabolic shape.
Judged respectively according to 4 different historical times, can be constructed corresponding to 4 kinds of different historical diagnostic conclusions
Diagnose subordinating degree function:
1) the 3.1st kind of diagnosis index, Current Temperatures are compared with ten minute moment before equipment: diagnosis degree of membership μ3j。
2) the 3.2nd kind of diagnosis index, Current Temperatures are compared with equipment previous moment hour: diagnosis degree of membership μ4j。
3) the 3.3rd kind of diagnosis index, Current Temperatures with yesterday in the same time compared with: diagnosis degree of membership μ5j。
4) the 3.4th kind of diagnosis index, First Year (such as nothing, then selecting last year) is in the same time after Current Temperatures and putting equipment in service
Compare: diagnosis degree of membership μ6j。
Above-mentioned each diagnosis degree of membership μijThe calculation method and function expression of (i=2,3,4,5,6), by " the fuzzy temperature
Rise no-load voltage ratio coefficient diagnosis " it is calculated.
The confidence level of different period of history diagnosis indexes, and the credible fuzzy membership of synthesis:
In the temperature rise no-load voltage ratio coefficient diagnosis of 4 different historical times, because there is respectively different ambiguity conditions, so,
The parameter of reliability coefficient is separately added into every conclusion, for judging that the condition that each period compares is credible.It is credible
Degree setting method: the confidence level of each diagnosis index is set as qj(j=3,4,5,6) allows adjustable extent 0~1,0 to indicate negative,
0.5 indicates uncertain, and 1 indicates affirmative；
The then credible fuzzy membership of each diagnosis index synthesis are as follows:
1) the 3.1st kind of diagnosis index, Current Temperatures are compared with ten minute moment before equipment:
The ambiguity of diagnosis process is: for same equipment, in a short time, although loaded fluctuation, temperature
Damping and amortization is presented in fluctuation compared with load, and variation is little.Comparativity is larger, is most likely catastrophe failure if there is obvious temperature rise,
Confidence level is higher.
Confidence level original value is set as: q3=0.8.The credible fuzzy membership of synthesis are as follows: r3j=q3*μ3j。
2) the 3.2nd kind of diagnosis index, Current Temperatures are compared with equipment previous moment hour:
The ambiguity of diagnosis process is: for same equipment, in a hour period, and temperature normal fluctuation, if there is
Obvious temperature rise, it is also possible to failure.
Confidence level original value is set as: q4=0.6.The credible fuzzy membership of synthesis are as follows: r4j=q4*μ4j。
3) the 3.3rd kind of diagnosis index, Current Temperatures with yesterday in the same time compared with:
The ambiguity of diagnosis process is: for same equipment, in two days synchronizations, payload size was approximately uniform
Possibility is larger, it is also possible to the ambiguity for bringing temperature rise different because of payload size difference.It in practical application, can be selected: " modern
It maximum temperature is compared with maximum temperature yesterday."
Confidence level original value is set as: q5=0.7.The credible fuzzy membership of synthesis are as follows: r5j=q5*μ5j。
4) the 3.4th kind of diagnosis index, First Year (such as nothing, then selecting last year) is in the same time after Current Temperatures and putting equipment in service
Compare:
The ambiguity of diagnosis process is: for same equipment, on the same day in 2 years, being in identical season, load
A possibility that size is close is larger.In practical application, can be selected: " First Year is same after the maximum temperature and putting equipment in service on the same day
The comparison of its maximum temperature."
Confidence level original value is set as: q6=0.6.The credible fuzzy membership of synthesis are as follows: r6j=q6*μ6j。
The diagnostic method and standard of indirect temperature measuring point temperature rise historical comparison:
This method is identical as " the diagnosis calculation method and standard of indirect temperature measuring point across comparison ".Specifically, indirection point temperature
The history no-load voltage ratio coefficient of angle value:Wherein, Δ θ2(t)HAt the time of to be currently diagnosed, Δ θ2(t)L
It is history referring to the moment, and needs to meet: Δ θ2(t)H> Δ θ2(t)L.(that is, the degree of membership meter of third class diagnosis index
It calculates, unanimously with " fuzzy temperature rise no-load voltage ratio coefficient diagnosis ", still, the title of each parameter is different for calculation method.)
The method of fault level subordinated-degree matrix construction:
By r calculated in above-mentioned stepsij, it is configured to the fuzzy diagnosis matrix R of matrix form, as follows:
X={ x1,x2,…,xmIndicate the weight sets that every diagnosis index is occupied in total diagnosis, wherein xiTable
Show diThe distribution weighted value of item diagnosis index.Because the total weighted value of all diagnosis indexes is 1, then have:It is every
Weighted value xi(i=1,2,3,4,5,6), can the importance according to the actual situation to every diagnosis index make modification.xiPermission can
The expression of adjustable range 0~1,0 is had no right (or can not use this method), and 1 indicates maximal weight (most authority).In the present embodiment, root
According to high-tension switch cabinet operating experience, original set value is provided are as follows: X={ 0.3,0.3,0.1,0.1,0.1,0.1 }.
Step 3: obtaining the fuzzy vector Y={ y of fault level after fuzzy operation Y=XER1,y2,y3,
y4, pick out the maximum y of numerical valuejIf (in data set, there are two or more than two identical maximum values, then select wherein j value
Maximum one), as final output diagnosis.The fault level for thinking target to be diagnosed is that degree of membership is maximum, most
Output diagnosis is degree of membership maximum fault level j and corresponding degree of membership y afterwardsj。
Step D, self-study mechanism:
rijReliability determine the superiority and inferiority and success or failure of diagnosis, the method determined need to refer to according to the diagnosis of each subitem
The state of ambiguity and source in mark, construct calculating subordinating degree function accordingly, (, degree of membership different according to ambiguity state
Function is possible to different).The wherein initial setting of the regularity of distribution (i.e. distribution parameter) of each subordinating degree function rule of thumb and is gone through
History fault statistics conclusion carries out Comprehensive Assessment.In later practical application, pass through self-study mechanism gradually modification and perfection.
Specific modification method are as follows: fault diagnosis expert system carries out self study to the historical sample by expert's confirmation,
The ambiguity state in revised each subitem diagnosis index is obtained, and then subordinating degree function is modified in algorithm, i.e.,
Actual correction be each subordinating degree function distribution parameter, so that each subitem diagnosis index of each subitem is more accurately reflected equipment
Malfunction, to complete self-study mechanism.The method used due to diagnostic system is based on fuzzy diagnosis, self study
It will be carried out by corresponding fuzzy neural network.The main method of neuroid self study is: provide comprising input and
The iterative learning sample data of output vector, network learning procedure are exactly constantly to be adjusted to subordinating degree function distribution parameter,
And then optimize confidence level distribution parameter and weighted value constantly, make network convergence, error amount reaches the smallest process.
It comprises the concrete steps that: by Fault Fuzzy Diagnostic index weights collection X={ x1,x2,…xm, as the input of neural network,
Neural network obtains reality output Y={ y by synthesis operation1,y2,…,yn, operational formula are as follows:
Wherein, 0≤rij≤ 1,1≤i≤m, 1≤j≤n, xiIt is in network
Weight between input pattern and output mode.
The basic thought of the adjustment process of the weighted value and confidence value of each subitem diagnosis index utilizes neural network
The reference that deviation between desired output and reality output is adjusted as connection weight, and this deviation is finally reduced, it is specific to adjust
Section process are as follows:
It enables(y in formulaj)rIt is desired output (because in simple thermometric fault diagnosis, diagnosis has single
One property, so only choosing the maximum (y of numerical value every timej)rCarry out self study operation), yjFor reality output, bjIndicate output
Error seeks x using following formulai:
For specific j, by rijIt sorts from large to small, selects the maximum r of numerical valueij, corresponding i value is i ', xi’(t+
1)=xi’(t)*abj, then other in addition to i
Wherein, xi(t) weighted value of moment t, x are indicatedi(t+1) it is obtained after indicating primary to the modified weight of moment t new
Weighted value, a is scale factor, meet 0≤a≤1, adopt with the aforedescribed process finally total energy convergence, thus complete examine each
Disconnected index weights xiAdjustment, achieve the effect that self study.
In addition, the present embodiment can be fabricated to the software program that can be run, the operation logic of program in conjunction with above-mentioned steps A to D
As shown in Figure 3.The temperature data of input is acquired by temperature sensor, the acquisition principle of temperature data see application No. is
201610059632.4 Chinese invention patent, details are not described herein.
It will be apparent to those skilled in the art that can make various other according to the above description of the technical scheme and ideas
Corresponding change and deformation, and all these changes and deformation all should belong to the protection scope of the claims in the present invention
Within.
Claims (10)
1. a kind of switchgear Fault Fuzzy Diagnostic method based on indirect thermometric mode, which comprises the following steps:
Step A: construction fuzzy diagnosis matrix；With vector Y={ y1,y2,…,ynIndicate switchgear pyrogenicity malfunction collection
It closes, wherein yj(j=l, 2 ..., n) indicates that pyrogenicity malfunction increases according to the grade that n is arranged from 1 to n fault level, failure
State is serious,Wherein, 0≤rij≤ 1,1≤i≤m, 1≤j≤n, xiIndicate diItem diagnosis index
Weighted value is distributed, all total weighted values of diagnosis index are 1, wherein 0≤xi≤ 1,N indicates fault level, n
For the integer more than or equal to 1；With vector D={ d1,d2,…,dmIndicate the collection for causing every diagnosis index of pyrogenicity malfunction
It closes, wherein di(i=1,2 ..., m) indicates that diagnosis index, m indicate the diagnosis index type for being used for Fault Fuzzy Diagnostic；And with
Vector D is that diagonal entry constructs diagonal form matrix E=diag (d1,d2,…,dm) it is used for calculating process, it is examined when using respective items
When severed finger mark diagnoses, d is enabledi=1, otherwise di=0；With vector X={ x1,x2,…,xmIndicate that every diagnosis index is diagnosed always
The weight sets occupied in conclusion, wherein xiIndicate diThe distribution weighted value of item diagnosis index, all total weighted values of diagnosis index
It is 1；
Step B: according to the source of ambiguity and the preset physical model of combinations of states, to the malfunction y in vector Yj, calculate
Every diagnosis index out, and according to given diagnostic criteria, make the judge of fault level；Every diagnosis index is calculated and determined
diTo the confidence level r of malfunctionij；The evaluate collection that the malfunction of n grade corresponds to m diagnosis index just constitutes fuzzy examine
Disconnected matrix R, as follows:
Wherein, 0≤rijThe fuzzy relation of≤1, R expression vector D to vector Y；
Step C: the vector Y={ y after fuzzy operation Y=XER, after obtaining Fuzzy Processing1,y2,…,yn, it is last defeated
Diagnosis is degree of membership maximum fault level j and corresponding degree of membership y outj。
2. switchgear Fault Fuzzy Diagnostic method as described in claim 1, which is characterized in that in step C, after Fuzzy Processing
Vector Y={ y1,y2,…,ynIn, if there is two or more identical maximum values, then select j value maximum one
yjDiagnosis as final output.
3. switchgear Fault Fuzzy Diagnostic method as described in claim 1, which is characterized in that in step B, rijDetermination method
Are as follows: according to the source of the ambiguity of every diagnosis index and state, construct corresponding subordinating degree function.
4. switchgear Fault Fuzzy Diagnostic method as claimed in claim 3, which is characterized in that
First item diagnosis index d1To confidence level r1jCalculating process it is as follows:
If generating heat unbalanced degree coefficient are as follows:Unbalanced degree distribution probability are as follows:
(p≥1)；Wherein θmaxFor the maximum temperature of device in actual switch cabinet, θ1It (t) is lump heat source temperature in calculated switchgear
Degree, p0For the central value for unbalanced degree coefficient distribution of generating heat, σ be the coefficient of regime being distributed, and θmax> θ1(t)；
Then subordinating degree function are as follows:θmax≥p0θ1(t), and combine following standard,
Obtain r1j=q1*μ1j, 0≤q1≤1。
5. switchgear Fault Fuzzy Diagnostic method as claimed in claim 3, which is characterized in that
Section 2 diagnosis index d2To confidence level r2jCalculating process it is as follows:
If the confidence level of ambiguity condition 1 is q21；The confidence level of ambiguity condition 2 is q22, Section 2 diagnosis index d2It is total can
Reliability is q2, then:0≤q2≤ 1,0≤q21≤ 1,0≤q22≤1；
Then r2j=q2*μ2j, j=1,2,3,4；
Ambiguity condition 1: for two switch cabinet equipments, because of payload size difference, and the temperature difference of indirection point is brought；
Ambiguity condition 2: it for two switch cabinet equipments, because local environment is different or internal structure is variant, and brings indirectly
The temperature difference of point；
If the no-load voltage ratio coefficient of two switchgear indirection point temperature rises are as follows:Wherein, Δ θ2(t)HFor high temperature
Rise indirection point；Δθ2(t)LFor low-temperature-rise indirection point；The no-load voltage ratio coefficient δ diagnostic threshold standard of two switchgear indirection point temperature rises is such as
Under:
Membership function expression formula is obtained in conjunction with the no-load voltage ratio coefficient δ diagnostic threshold standard of two switchgear indirection point temperature rises are as follows:
Wherein, E1 to E6 is from normally to the value of the no-load voltage ratio coefficient of alarm.
6. switchgear Fault Fuzzy Diagnostic method as claimed in claim 3, which is characterized in that
Section 3 diagnosis index d3To confidence level r3jCalculating process it is as follows:
If diagnosis degree of membership of the switchgear Current Temperatures compared with the temperature at its preceding ten minute moment is μ3j, confidence level original value sets
For q3, 0≤q3≤1；
The credible fuzzy membership of synthesis are as follows: r3j=q3*μ3j, j=1,2,3,4；
If the history no-load voltage ratio coefficient of switchgear indirection point temperature value are as follows:Wherein, Δ θ2(t)HTo work as
Before at the time of be diagnosed；Δθ2(t)LIt is history referring to the moment, and needs to meet: Δ θ2(t)H> Δ θ2(t)L；
Membership function expression formula are as follows:
Wherein, E1 to E6 is diagnosis from normally to the value of the history no-load voltage ratio coefficient of alarm.
7. switchgear Fault Fuzzy Diagnostic method as claimed in claim 3, which is characterized in that
Section 3 diagnosis index d4To confidence level r4jCalculating process it is as follows:
If diagnosis degree of membership of the switchgear Current Temperatures compared with the temperature at its previous moment hour is μ4j, confidence level original value
It is set as q4, 0≤q4≤1；
The credible fuzzy membership of synthesis are as follows: r4j=q4*μ4j, j=1,2,3,4；
If the history no-load voltage ratio coefficient of switchgear indirection point temperature value are as follows:Wherein, Δ θ2(t)HTo work as
Before at the time of be diagnosed；Δθ2(t)LIt is history referring to the moment, and needs to meet: Δ θ2(t)H> Δ θ2(t)L；
Membership function expression formula are as follows:
Wherein, E1 to E6 is diagnosis from normally to the value of the history no-load voltage ratio coefficient of alarm.
8. switchgear Fault Fuzzy Diagnostic method as claimed in claim 3, which is characterized in that
Section 3 diagnosis index d5To confidence level r5jCalculating process it is as follows:
If switchgear Current Temperatures with its yesterday synchronization temperature compared with diagnosis degree of membership be μ5j, confidence level original value sets
For q5, 0≤q5≤1；
The credible fuzzy membership of synthesis are as follows: r5j=q5*μ5j, j=1,2,3,4；
If the history no-load voltage ratio coefficient of switchgear indirection point temperature value are as follows:Wherein, Δ θ2(t)HTo work as
Before at the time of be diagnosed；Δθ2(t)LIt is history referring to the moment, and needs to meet: Δ θ2(t)H> Δ θ2(t)L；
Membership function expression formula are as follows:
Wherein, E1 to E6 is diagnosis from normally to the value of the history no-load voltage ratio coefficient of alarm.
9. switchgear Fault Fuzzy Diagnostic method as claimed in claim 3, which is characterized in that
Section 3 diagnosis index d6To confidence level r6jCalculating process it is as follows:
If diagnosis degree of membership of the switchgear Current Temperatures compared with the temperature at its previous moment hour is μ6j, confidence level original value
It is set as q6, 0≤q6≤1；
The credible fuzzy membership of synthesis are as follows: r6j=q6*μ6j, j=1,2,3,4；
If the history no-load voltage ratio coefficient of switchgear indirection point temperature value are as follows:Wherein, Δ θ2(t)HTo work as
Before at the time of be diagnosed；Δθ2(t)LIt is history referring to the moment, and needs to meet: Δ θ2(t)H> Δ θ2(t)L；
Membership function expression formula are as follows:
Wherein, E1 to E6 is diagnosis from normally to the value of the history no-load voltage ratio coefficient of alarm.
10. switchgear Fault Fuzzy Diagnostic method as described in claim 1, which is characterized in that
There are also following steps after step C:
When the judgement conclusion of final output and actual malfunction are not inconsistent, weight sets is modified using neural network,
Makeover process is as follows:
It enables(y in formulaj)rFor desired output, yjFor reality output, bjIndicate output error,X is sought using following formulai: for specific j, by rijIt sorts from large to small, selects numerical value
Maximum rij, corresponding i value is i ', Xi’(t+1)=xi’(t)*abj, then other in addition to i
Wherein, xi(t) weighted value of moment t, x are indicatedi(t+1) the new power obtained after indicating primary to the modified weight of moment t
Weight values, a are scale factor, meet 0≤a≤1.
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Effective date of registration: 20210120 Address after: 1 / F, No.8, Keji 9 Road, high tech Zone, Zhuhai, Guangdong 519000 Patentee after: ZHUHAI YADO MONITORING TECHNOLOGY Co.,Ltd. Address before: No.8, Keji 9 Road, high tech Zone, Zhuhai, Guangdong 519000 Patentee before: Yang Zhiqiang |