CN103777123A - Partial discharge fault comprehensive diagnosis method for GIS device - Google Patents
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- CN103777123A CN103777123A CN201410039588.1A CN201410039588A CN103777123A CN 103777123 A CN103777123 A CN 103777123A CN 201410039588 A CN201410039588 A CN 201410039588A CN 103777123 A CN103777123 A CN 103777123A
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Abstract
The invention discloses a partial discharge fault comprehensive diagnosis method for a GIS device. The method includes the following implementation steps that firstly, three sets of diagnosis results output through an ultrahigh frequency detection method, an ultrasonic detection method and an SF6 componential analysis method of the partial discharge fault comprehensive diagnosis method for the GIS device respectively are respectively output; secondly, a probability matrix is built according to the three sets of diagnosis results; thirdly, a comprehensive diagnosis probability vector is acquired by multiplying the probability matrix by an optimal weight vector; fourthly, the maximum value among the probability value of point discharge, the probability value of suspension discharge and the probability value of particle discharge in the comprehensive diagnosis probability vector is found, the discharge type corresponding to the maximum value serves as a partial discharge fault comprehensive diagnosis result and is output. Early fault hidden danger of the GIS device can be diagnosed, the detection ratio and the accuracy of fault diagnosis are improved, the scientific basis and the guidance are provided for condition overhaul and maintenance of the GIS device on site, and the partial discharge fault comprehensive diagnosis method has the advantages that misjudgment risks are small, and diagnosis accuracy and stability are high.
Description
Technical field
The present invention relates to the fault diagnosis technology field of power system transformer substation, be specifically related to a kind of partial discharges fault error comprehensive diagnosis method for GIS equipment.
Background technology
In recent years, along with the development of Construction of Intercity Network, the quantity of GIS transformer station constantly increases, and GIS equipment has plurality of advantages because of it, has become the leading switchgear of electric system.At present, the failure rate of GIS equipment is higher, although new GIS equipment has passed through product export examination and on-the-spot commissioning test, still has equipment just to have an accident soon putting into operation, and what have just has an accident in the time starting shipment.And in operational process, because GIS device interior field intensity is very high, even if there is tiny flaw, also easily spread, cause equipment failure, cause large economic loss.Therefore be necessary to study GIS Diagnosis Technique and develop fault diagnosis system, improving the recall rate of early stage hidden danger and the accuracy of fault diagnosis.
At present, there are ultrasonic Detection Method, ultrahigh frequency detection method, SF for the partial discharges fault error comprehensive diagnosis method of GIS equipment
6three kinds of componential analysis, the partial discharges fault comprehensive diagnos result of GIS equipment comprises that point discharge, suspended discharge, particle discharge three kinds.But, ultrasonic Detection Method, ultrahigh frequency detection method, SF
6there is respectively following defect in componential analysis.
Although 1, the interference of strong electromagnetic environment in ultrasonic Detection Method Bu Shou transformer station, but GIS Processing of Partial Discharge Ultrasonic Signals frequency is low, decay is fast, in ultrasonic method measurement bandwidth (30～80kHz), and the strong noise jamming that Outdoor Trusses For Transformer Substation exists, especially natural wind and corona are very large on the impact of testing result, therefore, supercritical ultrasonics technology sensitivity is low, poor anti jamming capability, very strong shelf depreciation and mechanical vibration can only be detected, be difficult to reach the requirement of GIS Partial Discharge Detection.
2, ultrahigh frequency detection method mainly detects the electromagnetic wave signal of 0.3～3GHz, highly sensitive, and antijamming capability is strong, but the problems such as its quantitatively calibrating and pattern-recognition not yet solve, and is difficult to local discharge condition and discharge capacity size accurately to judge.
3, SF
6componential analysis has the electromagnetic noise of not being subject to and vibration interference, and the while is also applicable to the advantages such as the detection of overheating fault, has a extensive future.But in current existing Research Literature, still there is no in clear and definite GIS equipment relation between shelf depreciation type, the order of severity and SF6 air pressure and decomposition product volume fraction, and due under some fault mode, decomposition gas product content there is no significant change, the diagnosis of simple dependence gas analyte exists larger blind area, may be having the device Diagnostic of electric discharge property defect for normal.
Summary of the invention
The technical problem to be solved in the present invention is to provide one can diagnose GIS equipment initial failure hidden danger, improve fault diagnosis recall rate and accuracy, for repair based on condition of component, the maintenance of on-the-spot GIS equipment provide scientific basis and guidance, the partial discharges fault error comprehensive diagnosis method for GIS equipment that erroneous judgement risk is little, accuracy rate of diagnosis is high, stability is strong.
For solving the problems of the technologies described above, the technical solution used in the present invention is:
For a partial discharges fault error comprehensive diagnosis method for GIS equipment, implementation step is as follows:
1) input respectively by ultrahigh frequency detection method, ultrasonic Detection Method, SF
6three groups of diagnostic results of the partial discharges fault diagnostic method output of three kinds of GIS equipment of componential analysis, each group diagnostic result comprises the probable value of point discharge, suspended discharge, 3 kinds of electric discharge types of particle electric discharge;
2) build probability matrix as the formula (1) according to described three groups of diagnostic results;
In formula (1), M represents to build the probability matrix obtaining, M
1represent one group of diagnostic result of ultrahigh frequency detection method output, M
1expression formula be M
1=[m
11, m
12, m
13]
t, m
11the probable value of point discharge in the diagnostic result of expression ultrahigh frequency detection method output, m
12the probable value of suspended discharge in the diagnostic result of expression ultrahigh frequency detection method output, m
13the probable value of particle electric discharge in the diagnostic result of expression ultrahigh frequency detection method output; M
2represent one group of diagnostic result of ultrasonic Detection Method output, M
2expression formula be M
2=[m
21, m
22, m
23]
t, m
21the probable value of point discharge in the diagnostic result of expression ultrasonic Detection Method output, m
22the probable value of suspended discharge in the diagnostic result of expression ultrasonic Detection Method output, m
23the probable value of particle electric discharge in the diagnostic result of expression ultrasonic Detection Method output; M
3represent SF
6one group of diagnostic result of componential analysis output, M
3expression formula be M
3=[m
31, m
32, m
33]
t, m
31represent SF
6the probable value of point discharge in the diagnostic result of componential analysis output, m
32represent SF
6the probable value of suspended discharge in the diagnostic result of componential analysis output, m
33represent SF
6the probable value of particle electric discharge in the diagnostic result of componential analysis output;
3) described probability matrix is multiplied by optimal weights vector and obtains comprehensive diagnos probability vector as the formula (2);
U=[M
1,M
2,M
3]W
opt=[U
1,U
2,U
3]
T （2）
In formula (2), U represents the comprehensive diagnos probability vector finally calculating, U
1represent the probable value of point discharge in described comprehensive diagnos probability vector, U
2represent the probable value of suspended discharge in described comprehensive diagnos probability vector, U
3represent the probable value of particle electric discharge in described comprehensive diagnos probability vector; W
optbe illustrated in the optimal weights vector of under criterion of least squares, error sum of squares being tried to achieve, its expression formula as the formula (3);
In formula (3), E represents information matrix of forecast errors, and the expression formula of E is E=[(e
it)
m × n] [(e
it)
m × n]
t, wherein e
itrepresent the predicated error of i forecast model at moment t, m represents the number of forecast model, and n represents the moment value of prediction, e
itexpression formula be e
it=f (t)-f
i(t), wherein f (t) represents the property value of a forecasting object at moment t, f
i(t) represent the property value of i forecast model at moment t, wherein the sequence number i span of forecast model is i=1,2 ..., m, the span of moment t is t=1,2 ..., n, E
-1represent the inverse matrix of E; R
tthe transposed matrix of representing matrix R, R
texpression formula be R
t=(1,1 ..., 1)
1 × m, matrix R is the matrix that is made up of single-row structure m individual 1;
4) find out the maximal value in the probable value three of probable value, particle electric discharge of probable value, the suspended discharge of point discharge in described comprehensive diagnos probability vector, export electric discharge type corresponding described maximal value as partial discharges fault comprehensive diagnos result.
Further, in each group diagnostic result of described step 1), the probable value of only having a kind of fault type in described point discharge, suspended discharge, 3 kinds of electric discharge type values of particle electric discharge is that the probable value of " 1 ", all the other two kinds of fault types is " 0 "; Described step 2) probability matrix in, [m
11, m
12, m
13] value of only having 1 element in three is " 0 " for the value of " 1 ", all the other two elements; [m
21, m
22, m
23] value of only having 1 element in three is " 0 " for the value of " 1 ", all the other two elements; [m
31, m
32, m
33] value of only having 1 element in three is " 0 " for the value of " 1 ", all the other two elements.
The present invention has following advantage for the partial discharges fault error comprehensive diagnosis method of GIS equipment: the present invention is by utilizing ultrahigh frequency detection method, ultrasonic Detection Method, SF
6three groups of diagnostic results of the partial discharges fault diagnostic method output of three kinds of GIS equipment of componential analysis, each group diagnostic result comprises the probable value of point discharge, suspended discharge, 3 kinds of electric discharge types of particle electric discharge, by setting up the probability matrix shown in formula (1) as multi-method combined diagnosis model, by ultrahigh frequency detection, ultrasound examination, SF
6these 3 kinds of diagnostic method methods of constituent analysis carry out comprehensively, can overcoming ultrahigh frequency detection method, ultrasonic Detection Method, SF as diagnostic method independently
6the shortcomings and limitations of any one single diagnostic method in componential analysis three, and the present invention obtains comprehensive diagnos probability vector as the formula (2) by probability matrix being multiplied by optimal weights vector, and find out the probable value of point discharge in comprehensive diagnos probability vector, the probable value of suspended discharge, maximal value in the probable value three of particle electric discharge, export electric discharge type corresponding maximal value as partial discharges fault comprehensive diagnos result, utilize the calculating of optimal weights vector, effectively solve GIS equipment discharge fault and diagnosed the assembly reunification of multiple independent assessment result.In sum, the present invention can diagnose GIS equipment initial failure hidden danger, improve fault diagnosis recall rate and accuracy, for repair based on condition of component, the maintenance of on-the-spot GIS equipment provide scientific basis and guidance, have advantages of that erroneous judgement risk is little, accuracy rate of diagnosis is high, stability is strong.
Accompanying drawing explanation
Fig. 1 is the method flow schematic diagram of the embodiment of the present invention.
Embodiment
As shown in Figure 1, the present embodiment is as follows for the implementation step of the partial discharges fault error comprehensive diagnosis method of GIS equipment:
1) input respectively by ultrahigh frequency detection method, ultrasonic Detection Method, SF
6three groups of diagnostic results of the partial discharges fault diagnostic method output of three kinds of GIS equipment of componential analysis, each group diagnostic result comprises the probable value of point discharge, suspended discharge, 3 kinds of electric discharge types of particle electric discharge.
In the present embodiment, in each group diagnostic result of step 1), the probable value of only having a kind of fault type in point discharge, suspended discharge, 3 kinds of electric discharge type values of particle electric discharge is that the probable value of " 1 ", all the other two kinds of fault types is " 0 ".For example, if diagnostic result is point discharge, the value of this group diagnostic result is " 100 "; If diagnostic result is suspended discharge, the value of this group diagnostic result is " 010 "; If diagnostic result is particle electric discharge, the value of this group diagnostic result is " 001 ".
2) build probability matrix as the formula (1) according to three groups of diagnostic results;
In formula (1), M represents to build the probability matrix obtaining, M
1represent one group of diagnostic result of ultrahigh frequency detection method output, M
1expression formula be M
1=[m
11, m
12, m
13]
t, m
11the probable value of point discharge in the diagnostic result of expression ultrahigh frequency detection method output, m
12the probable value of suspended discharge in the diagnostic result of expression ultrahigh frequency detection method output, m
13the probable value of particle electric discharge in the diagnostic result of expression ultrahigh frequency detection method output; M
2represent one group of diagnostic result of ultrasonic Detection Method output, M
2expression formula be M
2=[m
21, m
22, m
23]
t, m
21the probable value of point discharge in the diagnostic result of expression ultrasonic Detection Method output, m
22the probable value of suspended discharge in the diagnostic result of expression ultrasonic Detection Method output, m
23the probable value of particle electric discharge in the diagnostic result of expression ultrasonic Detection Method output; M
3represent SF
6one group of diagnostic result of componential analysis output, M
3expression formula be M
3=[m
31, m
32, m
33]
t, m
31represent SF
6the probable value of point discharge in the diagnostic result of componential analysis output, m
32represent SF
6the probable value of suspended discharge in the diagnostic result of componential analysis output, m
33represent SF
6the probable value of particle electric discharge in the diagnostic result of componential analysis output.
In the present embodiment, point discharge, suspended discharge, 3 kinds of electric discharge types of particle electric discharge homography subscript code 1,2,3 respectively, wherein, [m
11, m
12, m
13] value of only having 1 element in three is " 0 " for the value of " 1 ", all the other two elements; [m
21, m
22, m
23] value of only having 1 element in three is " 0 " for the value of " 1 ", all the other two elements; [m
31, m
32, m
33] value of only having 1 element in three is " 0 " for the value of " 1 ", all the other two elements.
3) probability matrix is multiplied by optimal weights vector and obtains comprehensive diagnos probability vector as the formula (2).
U=[M
1,M
2,M
3]W
opt=[U
1,U
2,U
3]
T （2）
In formula (2), U represents the comprehensive diagnos probability vector finally calculating, U
1represent the probable value of point discharge in comprehensive diagnos probability vector, U
2represent the probable value of suspended discharge in comprehensive diagnos probability vector, U
3represent the probable value of particle electric discharge in comprehensive diagnos probability vector; W
optbe illustrated in the optimal weights vector of under criterion of least squares, error sum of squares being tried to achieve, its expression formula as the formula (3).
In formula (3), E represents information matrix of forecast errors, and the expression formula of E is E=[(e
it)
m × n] [(e
it)
m × n]
t, wherein e
itrepresent the predicated error of i forecast model at moment t, m represents the number of forecast model, and n represents the moment value of prediction, e
itexpression formula be e
it=f (t)-f
i(t), wherein f (t) represents the property value of a forecasting object at moment t, f
i(t) represent the property value of i forecast model at moment t, wherein the sequence number i span of forecast model is i=1,2 ..., m, the span of moment t is t=1,2 ..., n, E
-1represent the inverse matrix of E; R
tthe transposed matrix of representing matrix R, R
texpression formula be R
t=(1,1 ..., 1)
1 × m, matrix R is the matrix that is made up of single-row structure m individual 1, the expression formula of matrix R is as the formula (4).
4) find out the probable value of point discharge in comprehensive diagnos probability vector, the probable value of suspended discharge, the probable value three (i.e. [U of particle electric discharge
1, U
2, U
3]) in maximal value, export electric discharge type corresponding maximal value as partial discharges fault comprehensive diagnos result.For example,, if the probable value maximum of point discharge is exported point discharge as partial discharges fault comprehensive diagnos result; If the probable value maximum of suspended discharge, exports suspended discharge as partial discharges fault comprehensive diagnos result; As the probable value maximum of fruit granule electric discharge, particle electric discharge is exported as partial discharges fault comprehensive diagnos result.
The foregoing is only the preferred embodiment of the present invention, protection scope of the present invention is not limited in above-mentioned embodiment, and every technical scheme that belongs to the principle of the invention all belongs to protection scope of the present invention.For a person skilled in the art, some improvements and modifications of carrying out under the prerequisite that does not depart from principle of the present invention, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (2)
1. for a partial discharges fault error comprehensive diagnosis method for GIS equipment, it is characterized in that implementation step is as follows:
1) input respectively by ultrahigh frequency detection method, ultrasonic Detection Method, SF
6three groups of diagnostic results of the partial discharges fault diagnostic method output of three kinds of GIS equipment of componential analysis, each group diagnostic result comprises the probable value of point discharge, suspended discharge, 3 kinds of electric discharge types of particle electric discharge;
2) build probability matrix as the formula (1) according to described three groups of diagnostic results;
In formula (1), M represents to build the probability matrix obtaining, M
1represent one group of diagnostic result of ultrahigh frequency detection method output, M
1expression formula be M
1=[m
11, m
12, m
13]
t, m
11the probable value of point discharge in the diagnostic result of expression ultrahigh frequency detection method output, m
12the probable value of suspended discharge in the diagnostic result of expression ultrahigh frequency detection method output, m
13the probable value of particle electric discharge in the diagnostic result of expression ultrahigh frequency detection method output; M
2represent one group of diagnostic result of ultrasonic Detection Method output, M
2expression formula be M
2=[m
21, m
22, m
23]
t, m
21the probable value of point discharge in the diagnostic result of expression ultrasonic Detection Method output, m
22the probable value of suspended discharge in the diagnostic result of expression ultrasonic Detection Method output, m
23the probable value of particle electric discharge in the diagnostic result of expression ultrasonic Detection Method output; M
3represent SF
6one group of diagnostic result of componential analysis output, M
3expression formula be M
3=[m
31, m
32, m
33]
t, m
31represent SF
6the probable value of point discharge in the diagnostic result of componential analysis output, m
32represent SF
6the probable value of suspended discharge in the diagnostic result of componential analysis output, m
33represent SF
6the probable value of particle electric discharge in the diagnostic result of componential analysis output;
3) described probability matrix is multiplied by optimal weights vector and obtains comprehensive diagnos probability vector as the formula (2);
U=[M
1,M
2,M
3]W
opt=[U
1,U
2,U
3]
T （2）
In formula (2), U represents the comprehensive diagnos probability vector finally calculating, U
1represent the probable value of point discharge in described comprehensive diagnos probability vector, U
2represent the probable value of suspended discharge in described comprehensive diagnos probability vector, U
3represent the probable value of particle electric discharge in described comprehensive diagnos probability vector; W
optbe illustrated in the optimal weights vector of under criterion of least squares, error sum of squares being tried to achieve, its expression formula as the formula (3);
In formula (3), E represents information matrix of forecast errors, and the expression formula of E is E=[(e
it)
m × n] [(e
it)
m × n]
t, wherein e
itrepresent the predicated error of i forecast model at moment t, m represents the number of forecast model, and n represents the moment value of prediction, e
itexpression formula be e
it=f (t)-f
i(t), wherein f (t) represents the property value of a forecasting object at moment t, f
i(t) represent the property value of i forecast model at moment t, wherein the sequence number i span of forecast model is i=1,2 ..., m, the span of moment t is t=1,2 ..., n, E
-1represent the inverse matrix of E; R
tthe transposed matrix of representing matrix R, R
texpression formula be R
t=(1,1 ..., 1)
1 × m, matrix R is the matrix that is made up of single-row structure m individual 1;
4) find out the maximal value in the probable value three of probable value, particle electric discharge of probable value, the suspended discharge of point discharge in described comprehensive diagnos probability vector, export electric discharge type corresponding described maximal value as partial discharges fault comprehensive diagnos result.
2. the partial discharges fault error comprehensive diagnosis method for GIS equipment according to claim 1, it is characterized in that: in each group diagnostic result of described step 1), the probable value of only having a kind of fault type in described point discharge, suspended discharge, 3 kinds of electric discharge type values of particle electric discharge is that the probable value of " 1 ", all the other two kinds of fault types is " 0 "; Described step 2) probability matrix in, [m
11, m
12, m
13] value of only having 1 element in three is " 0 " for the value of " 1 ", all the other two elements; [m
21, m
22, m
23] value of only having 1 element in three is " 0 " for the value of " 1 ", all the other two elements; [m
31, m
32, m
33] value of only having 1 element in three is " 0 " for the value of " 1 ", all the other two elements.
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| CN105676085A (en) * | 2016-01-31 | 2016-06-15 | 国家电网公司 | Extra-high voltage GIS partial discharge detection method based on multi-sensor information fusion |
| CN106324459A (en) * | 2016-09-30 | 2017-01-11 | 国家电网公司 | GIS local discharging detecting system and method based on sound-electricity integration sensor |
| CN107271867A (en) * | 2017-06-27 | 2017-10-20 | 国网河南省电力公司检修公司 | GIS partial discharge fault type recognition method based on D S evidence theories |
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| CN106324459A (en) * | 2016-09-30 | 2017-01-11 | 国家电网公司 | GIS local discharging detecting system and method based on sound-electricity integration sensor |
| CN107271867A (en) * | 2017-06-27 | 2017-10-20 | 国网河南省电力公司检修公司 | GIS partial discharge fault type recognition method based on D S evidence theories |
| CN108693448B (en) * | 2018-03-28 | 2020-11-13 | 西安博源电气有限公司 | Partial discharge mode recognition system applied to power equipment |
| CN108693448A (en) * | 2018-03-28 | 2018-10-23 | 西安博源电气有限公司 | One kind being applied to power equipment PD Pattern Recognition system |
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| CN111505457A (en) * | 2020-05-07 | 2020-08-07 | 国家电网有限公司 | GIS partial discharge comprehensive online monitoring device |
| CN111505457B (en) * | 2020-05-07 | 2022-05-31 | 国家电网有限公司 | GIS partial discharge comprehensive online monitoring device |
| CN113533879A (en) * | 2021-06-11 | 2021-10-22 | 南方电网科学研究院有限责任公司 | GIS equipment detectable rate calculation method based on fault simulation test |
| CN113640629A (en) * | 2021-07-26 | 2021-11-12 | 国网电力科学研究院武汉南瑞有限责任公司 | GIS partial discharge state evaluation method, recording medium and system |
| CN114184911A (en) * | 2021-11-23 | 2022-03-15 | 国网北京市电力公司 | Method and device for detecting defect type of equipment and electronic equipment |
| CN114184911B (en) * | 2021-11-23 | 2023-10-24 | 国网北京市电力公司 | Method and device for detecting defect type of equipment and electronic equipment |
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