CN115267006A - Based on SF 6 Method for diagnosing fault of DC gas insulation equipment for decomposition component analysis - Google Patents
Based on SF 6 Method for diagnosing fault of DC gas insulation equipment for decomposition component analysis Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/62—Detectors specially adapted therefor
- G01N30/72—Mass spectrometers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
- G01R31/1254—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of gas-insulated power appliances or vacuum gaps
Abstract
The invention discloses a method based on SF 6 The fault diagnosis method of the direct current gas insulation equipment for decomposing component analysis comprises the following steps: taking a gas sample, and detecting the content of characteristic gas in the gas sample, wherein the characteristic gas is SOF 2 、SO 2 F 2 、CF 4 、SO 2 、CO 2 Five gases; calculating the proportion of characteristic components according to the content of the characteristic gas, and judging the fault type according to a characteristic component ratio-fault type decision tree, wherein the characteristic component ratio-fault type decision tree comprises a pre-established corresponding relation between the characteristic component ratio and the fault type, and the fault type is metal protrusion defect or free metal particle defectAnd metal contamination defect and air gap defect on the surface of the insulator. The invention judges the working state of the direct current gas insulation equipment by utilizing a chemical analysis and detection method, and has the advantages of high sensitivity, high accuracy and low cost.
Description
Technical Field
The invention belongs to the technical field of electrical equipment fault detection, and relates to SF 6 Fault diagnosis of gas-insulated equipment, in particular based on SF 6 A method for diagnosing a fault in a DC gas-insulated apparatus by analyzing a decomposition component.
Background
The direct current transmission technology has the advantages of large transmission capacity, long transmission distance, point-to-point, low loss and the like, is very suitable for the national conditions of China, and has excellent social benefits. SF 6 The direct current gas insulation equipment is increasingly widely applied to extra-high voltage direct current transmission engineering. The direct current gas insulation equipment represented by the gas direct current wall bushing has great advantages compared with other conventional electric equipment: the structure is simple and compact, the weight is light, the heat dissipation capability is good, the through-flow capability is strong, and the daily maintenance is convenient. Although the dc gas-insulated equipment has the above-mentioned great advantages, various latent insulation defects are still inevitably generated, and if the latent insulation defects are not processed in time, the latent insulation defects may affect other non-fault components, and even further develop into equipment insulation faults, thereby threatening the power supply safety. Therefore, fault diagnosis and online monitoring are performed on the direct current gas insulation equipment, and a maintenance schedule is made in a targeted manner. The method has very important engineering significance for ensuring safe and reliable operation of direct-current gas insulation equipment and even the whole extra-high voltage direct current power grid.
Based on SF 6 SF of decomposition component analysis 6 On-line monitoring and fault diagnosis techniques for gas-insulated equipment have been the focus of research in the field,these studies have focused primarily on SF under arc discharge, spark discharge and ac partial discharge 6 The situation is decomposed. Based on SF 6 Some reports have been reported on the alternating current equipment fault detection method for decomposition component analysis, and the method has good application prospect. However, the gas composition and content under the conditions of alternating current discharge and direct current discharge are different and are not comparable. At present, SF 6 The research on the decomposition characteristics under partial discharge of typical insulation defects in direct-current gas insulation equipment is less, and the research on the decomposition characteristics based on SF has not been developed yet 6 A method for diagnosing the failure of DC equipment by analyzing its components is disclosed.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a SF-based system 6 A method for diagnosing a fault in a DC gas-insulated apparatus for decomposition component analysis.
The technical scheme is as follows:
based on SF 6 The fault diagnosis method of the direct current gas insulation equipment for decomposition component analysis is characterized by comprising the following steps,
s1, sampling a gas sample at a sampling port of direct-current gas insulation equipment;
s2, detecting the content of characteristic gas in the gas sample, wherein the characteristic gas is SOF 2 、SO 2 F 2 、CF 4 、SO 2 、CO 2 Five gases;
and S3, calculating a characteristic component proportion according to the content of the characteristic gas, and judging a fault type according to a characteristic component ratio-fault type decision tree, wherein the characteristic component ratio-fault type decision tree comprises a pre-established corresponding relation between the characteristic component ratio and the fault type, and the fault type is one of a metal protrusion defect, a free metal particle defect, an insulator surface metal pollution defect and an air gap defect.
Preferably, the characteristic component ratio is
R 1 ＝c(SOF 2 +SO 2 +SO2F 2 )/c(CO 2 +log 10 CF 4 )，
R 2 ＝c(SOF 2 +SO 2 )/c(SO 2 F 2 )；
Calculating R 1 、R 2 And calculating by using the volume concentration value of the characteristic gas.
In the feature component ratio-fault type decision tree, R 1 、R 2 The corresponding relation with the fault type is as follows:
if R is 1 Not less than 2.70 and R 2 Judging that the metal protrusion is defective if the number is more than or equal to 2.64;
if R is 1 Not less than 2.70 and R 2 <2.64, judging as a free metal particle defect;
if R is 1 <2.70 and R 2 Judging that the metal pollution defect on the surface of the insulator is larger than or equal to 2.37;
if R is 1 <2.70 and R 2 <2.37, the defect is judged as an air gap defect.
Preferably, the metal material involved in the metal protrusion defect, the free metal particle defect, and the metal contamination defect on the surface of the insulator is any one or more of aluminum, copper, and stainless steel.
Preferably, in step S2, the detection is performed by using a gas chromatograph-mass spectrometer.
Preferably, in step S1, the sampling timing is:
in the normal operation process of the direct current gas insulation equipment, the equipment is sampled periodically, and the sampling period is 1 time of sampling every 2 months;
immediately sampling direct current gas insulation equipment with possible insulation defects to perform fault diagnosis;
after the fault is cleared, the direct current gas insulation equipment firstly samples in a short period until gas components in a period of time are normal, and then returns to the normal sampling period.
It is another object of the present invention to provide a fault diagnosis system.
The technical scheme is as follows:
the fault diagnosis system is characterized by comprising a data input device, a memory, a processor and a computer program which is stored on the memory and can run on the processor;
the data input device is used for inputting the content data of the characteristic gas and storing the content data in the memory, and when the processor executes the program, the calculation and judgment process of the step S3 in the method is realized according to the input characteristic gas content data, and the judgment result is output.
Compared with the prior art, the invention has the beneficial effects that:
(1) The working state of the direct current gas insulation equipment is judged by utilizing a chemical analysis and detection method, and the method has the advantages of high sensitivity and low cost;
(2) The device body does not need to be modified or a complex detection element is implanted, only the gas needs to be sampled, and the operation is simple and convenient;
(3) The analysis work can be carried out when the equipment runs, and the running state of the direct current gas insulation equipment can be monitored in real time;
(4) The method is not influenced by environmental noise, strong electromagnetic interference and different metal material structures, and the fault judgment accuracy is high.
Drawings
FIG. 1 is a flow chart of a fault diagnosis method of the present invention;
FIG. 2 is a schematic diagram of a feature component ratio-fault type decision tree;
fig. 3 is a characteristic component ratio of characteristic gas components under partial discharge conditions caused by different defects in the gas chamber of the dc insulation apparatus and an apparatus operation time relationship curve, wherein: (a) c (SOF) 2 +SO 2 +SO2F 2 )/c(CO 2 +log 10 CF 4 )，(b)c(SOF 2 +SO 2 )/c(SO 2 F 2 )；
Fig. 4 is a graph of the characteristic component ratio of the characteristic gas component under partial discharge conditions caused by a defect of a metal protrusion made of different metal materials in the gas chamber of the dc-insulated apparatus, versus the operating time of the apparatus, wherein: (a) c (SOF) 2 +SO 2 +SO2F 2 )/c(CO 2 +log 10 CF 4 )，(b)c(SOF 2 +SO 2 )/c(SO 2 F 2 )。
Detailed Description
The present invention will be further described with reference to the following examples and the accompanying drawings.
Based on SF 6 The fault diagnosis method of the direct current gas insulation equipment for decomposition component analysis comprises the following steps as shown in figure 1:
s1, a gas sample is collected at a sampling port of the direct current gas insulation equipment. During sampling, a hose can be used for connecting a gas sampling port of the direct-current gas insulation equipment with the needle cylinder, and gas is introduced into the needle cylinder to be used as a detection sample.
In the step S1, the sampling timing is:
in the normal operation process of the direct-current gas insulation equipment, the equipment is sampled periodically, and the sampling period is 1 time of sampling every 2 months;
immediately sampling direct-current gas insulation equipment possibly having insulation defects to perform fault diagnosis;
after the fault is cleared, the direct current gas insulation equipment samples in a short period, wherein the short period of sampling is 1-3 days, the sampling period is gradually prolonged to 7 days until the gas components are normal within a period of time such as one month, and then the direct current gas insulation equipment is recovered to the normal sampling period.
S2, detecting the content of characteristic gas in the gas sample, wherein the characteristic gas is SOF 2 、SO 2 F 2 、CF 4 、SO 2 、CO 2 Five gases. In particular, a gas chromatograph-mass spectrometer (GC/MS) can be used to detect the content of characteristic gases in a gas sample. The method has the advantages of high sensitivity and accurate detection.
Under partial discharge conditions, SF 6 Decomposition occurs, the majority of active F atoms and SF are formed 5 、SF 4 The low fluorine sulfide is recombined into SF 6 Only a small part of the gas will contact with the trace H in the gas chamber 2 O、O 2 Metal materials and organic solid insulating materials and the like further undergo a series of irreversible chemical reactions, and the generated products mainly comprise: SO (SO) 2 F 2 、SOF 2 、CF 4 、SO 2 、SOF 4 、HF、CO 2 And SF 4 And the like. But due to SF 4 Transformation ofThe chemical property is very active and unstable, and other chemical substances can be easily generated by further reaction and decomposition; HF has strong acidity, can react with metal, organic solid insulating materials and the like to be consumed, and cannot be quantitatively detected by a GC/MS combined detection method because of the strong corrosivity of HF; SOF 4 Easy hydrolysis, high hydrolysis speed, high influence of water content in equipment and air chamber, and is not suitable for use as SF 6 Decomposing characteristic gas components by direct current partial discharge; h 2 S is relatively stable chemically, but SF 6 H generated by partial discharge decomposition 2 S is very little, and the insulation fault is hardly detected by a GC/MS combined detector, and only when serious insulation faults such as spark discharge, arc discharge and the like occur, H is 2 S will be generated in large quantities. While SOF 2 And SO 2 F 2 Although the hydrolysis is also carried out, the hydrolysis speed is slow, and the chemical property is relatively stable; SO 2 Is stable and consists mainly of SOF 2 And SO 2 F 2 Obtained by hydrolysis and can be used as para-SOF 2 And SO 2 F 2 And (4) supplementing. Therefore, the invention mainly detects SF 6 SO generated by DC partial discharge decomposition 2 F 2 、SOF 2 、CF 4 、SO 2 And CO 2 Five characteristic gases.
And S3, calculating a characteristic component proportion according to the content of the characteristic gas, and judging a fault type according to a characteristic component ratio-fault type decision tree, wherein the characteristic component ratio-fault type decision tree comprises a pre-established corresponding relation between the characteristic component ratio and the fault type, and the fault type is one of a metal protrusion defect, a free metal particle defect, an insulator surface metal pollution defect and an air gap defect.
Specifically, the characteristic component ratio is
R 1 ＝c(SOF 2 +SO 2 +SO2F 2 )/c(CO 2 +log 10 CF 4 )，
R 2 ＝c(SOF 2 +SO 2 )/c(SO 2 F 2 )。
Calculation of R 1 、R 2 And calculating by using the volume concentration value of the characteristic gas.
When insulation defects exist in the organic solid insulating material, the thermal/electrical effect of partial discharge causes the epoxy organic polymer compound in the organic solid insulating material to degrade and decompose and generate carbon-containing particles, namely CF 4 And CO 2 The carbon-containing characteristic decomposition component can judge whether the insulation defect relates to the organic solid insulating material and the degradation degree of the organic solid insulating material, because of CF 4 Is low, so C (CO) is extracted 2 +log 10 CF 4 ) Is a characteristic quantity. SOF 2 +SO 2 +SO 2 F 2 The S atoms in the three characteristic decomposition components are only possible from SF 6 . Therefore, the sum of the generation amounts of the three sulfur-containing characteristic components can be used for characterizing the discharge strength and the SF 6 C (SOF) is extracted 2 +SO 2 +SO 2 F 2 ) Is a characteristic amount.
In the feature component ratio-fault type decision tree, R, as shown in FIG. 2 1 、R 2 The corresponding relation with the fault type is as follows:
if R is 1 Not less than 2.70 and R 2 Judging that the metal protrusion is defective if the metal protrusion is more than or equal to 2.64;
if R is 1 Not less than 2.70 and R 2 <2.64, judging as a free metal particle defect;
if R is 1 <2.70 and R 2 Judging that the metal pollution defect on the surface of the insulator is larger than or equal to 2.37;
if R is 1 <2.70 and R 2 <2.37, the defect is judged as an air gap defect.
The defects of the metal protrusion, the free metal particles and the metal dirt on the surface of the insulator are all defects related to metal materials, and the related metal materials are any one or more of aluminum, copper and stainless steel. The method does not distinguish.
The characteristic component ratio-fault type decision tree is established in a mode of combining a partial discharge experiment and algorithm deduction.
The partial discharge experiment can refer to a partial discharge experiment device of alternating current equipment in the prior art, one of four defect models, namely a metal protrusion defect, a free metal particle defect, an insulator surface metal contamination defect, an air gap defect and the like, is independently arranged in a simulation device, direct current voltage is applied to serve as the zero moment of the experiment, then the direct current voltage is continuously applied to the defect models for 96h, 20ml of gas samples in a gas chamber are taken every 12h for GC/MS detection, the content of various characteristic gases is detected, the characteristic component proportion is calculated, and the experiment is ended after the four defect experiments are completed. The experiment is repeated for a plurality of times to obtain a characteristic component proportion value data set.
As shown in FIG. 3, c (SOF) is a defect of metal protrusions, a defect of free metal particles, a defect of metal contamination on the surface of the insulator, and a defect of air gaps 2 +SO 2 )/c(SO 2 F 2 ) Namely R 2 The fluctuation ranges of the two-dimensional light source are respectively 2.81-2.87, 2.04-3.05, 1.90-2.55 and 1.91-2.01; c (SOF) under defects of metal protrusions, free metal particles, metal contamination and air gaps on the surface of the insulator 2 +SO 2 +SO 2 F 2 )/c(CO 2 +log 10 CF 4 ) Namely R 1 The fluctuation ranges of the two-dimensional light source are respectively 10.99-245.89, 8.81-58.39, 1.07-2.62 and 0.67-2.45.
In the research, a decision tree based on the content ratio of the characteristic components under the direct-current insulation fault is constructed by adopting a C4.5 algorithm, so that the identification and diagnosis of different insulation faults are realized. The process of constructing by adopting the C4.5 algorithm comprises the following steps:
assume that C is a data set of N data, the data samples of this set C may divided into M different types D = { D = } 1 ,D 2 ,…,D M }. With these data samples at each of the type appearing probability of P = { P = { (P) 1 ,P 2 ,…,P M }. Let Ni be type D i (i =1,2, \8230;, M) is the corresponding P i Can be estimated using Ni/N. At this time, for set C, its scent concentration information entropy is as follows:
assume that each data sample of set C has multiple feature attributes, F being one of the features, F having v different values. Thus, the set C can be grouped into v subsets { C according to the feature attribute F 1 ,C 2 ,…,C v And F, if the nodes are grouped according to the characteristic attribute F, the subsets are child leaf nodes grown from the parent node set C. If N is present j Is a subset C j (wherein j =1,2, \8230;, v) contains the number of samples, and N i,j As a subset C j Of type D i The number of samples. Then a conditional information entropy representing the feature attribute F can be defined:
wherein the subset C j Wherein the sample belongs to type D i The probability of (a) of (b) being,
grouping the information gain value InfGain (C | F) obtained by the set C by using the characteristic attribute F:
InfGian(C|F)＝H(C)-H(C|F)；
the information gain Ratio Inf _ Ratio (C | F) obtained by grouping the set C with the feature attribute F is equal to:
InfRatio＝InfGain/InsicInf，
where InsiciInf represents the information gain value of a grouped sub-leaf node data sample, which is equal in magnitude to:
and obtaining the information gain rate under each characteristic attribute of the currently prepared grouping sample set through the above formula, and then selecting the characteristic attribute with the maximum information gain rate as the current grouping attribute to realize the classification and grouping of the sample set. Finally, the subset obtained after each grouping is continuously divided by adopting the same method as the method until the condition is terminated or all the groupings are completed.
In addition, in order to effectively adjust the accuracy and complexity of the constructed decision tree, the decision tree needs to be pruned. Where pruning is carried out using the post-pruning mode. The pruning criterion is as follows: pruning is performed if the predicted sample error rate of the sub-leaf nodes after branching is greater than that before branching. The expected sample error rate e for a child leaf node is as follows:
where z is the confidence limit, taken to be 0.25. The number of current cotyledon node sample is a, the number of misclassified samples is k, and the misclassification rate b = k/a.
By using the method, the confidence limit is 0.25, the lowest sample number of the node is 2, and the SF under four insulation defects of direct current partial discharge is adopted 6 Content ratio R of characteristic component 1 ＝c(SOF 2 +SO 2 +SO2F 2 )/c(CO 2 +log 10 CF 4 ) And R 2 ＝c(SOF 2 +SO 2 )/c(SO 2 F 2 ) As data attributes, decision trees under different fault conditions are generated as in fig. 1.
In addition, a dc gas insulated apparatus represented by a gas dc wall bushing has a complicated structure, and includes a large number of metal members such as metal bus bars in addition to an insulator made of an organic insulating material, which constitutes a main structure of the apparatus. These metal components are not made of a single metal, including aluminum, copper, stainless steel, silver, and other metal materials, as required for function. SF 6 Decomposition reactions by partial discharge are extremely complex, SF 6 The F atoms and the low-fluorine sulfides generated by decomposition can react with various metals to generate metal fluorides or metal sulfides, the physical properties and the chemical properties of different metals are greatly different, and the electrode material and the decomposed products are in direct relation.
In order to research the effectiveness of the decision tree of the invention on the defects of different metal materials, the partial discharge experiment is also made of red copper, aluminum and 18/8 stainless steel which are commonly used in gas insulation equipmentForming metal protrusion defect and performing partial discharge experiment. As shown in FIG. 4, under the DC partial discharge, the content ratio c (SOF) of the characteristic components of the three metal materials 2 +SO 2 )/c(SO 2 F 2 ) Namely R 2 The fluctuation range is 2.84-4.50, and the characteristic component ratio R of different metal materials is under the condition of partial discharge caused by similar defects 2 The difference between them is not large. Characteristic component content ratio c (SOF) of three metal materials 2 +SO 2 +SO 2 F 2 )/c(CO 2 +log 10 CF 4 ) Namely R 1 In the range of 2.37 to 290.12, the characteristic decomposition component content ratio is greatest under aluminum materials, and is next to 18/8 stainless steel and copper under dc partial discharge conditions. And respectively carrying out insulation defect classification and identification on the obtained data of different metal insulation defects by adopting a decision tree under the partial discharge condition corresponding to the graph 1, wherein the accuracy is not interfered by the metal type. This also illustrates the rationality and robustness of the decision tree of the present invention.
Experiments and practical use show that the fault diagnosis method has the fault identification accuracy rate of about 85 percent, and has higher reliability and application prospect.
For practical use, the fault diagnosis system comprises a data input device, a memory, a processor and a computer program which is stored in the memory and can run on the processor. The data input device is used for inputting the content data of the characteristic gas and storing the content data in the memory, and when the processor executes the program, the calculation and judgment process of the step S3 in the method is realized according to the input characteristic gas content data, and the judgment result is output. Further for convenience of use, the data input device may be a data output module of the GC/MS detector, which directly transmits data to the memory.
Finally, it should be noted that the above-mentioned description is only a preferred embodiment of the present invention, and those skilled in the art can make various similar representations without departing from the spirit and scope of the present invention.
Claims (6)
1. Based on SF 6 A method for diagnosing a fault in a direct current gas insulated apparatus for decomposition component analysis, characterized by comprising the steps of:
s1, sampling a gas sample at a sampling port of direct-current gas insulation equipment;
s2, detecting the content of characteristic gas in the gas sample, wherein the characteristic gas is SOF 2 、SO 2 F 2 、CF 4 、SO 2 、CO 2 Five gases;
and S3, calculating a characteristic component proportion according to the content of the characteristic gas, and judging a fault type according to a characteristic component ratio-fault type decision tree, wherein the characteristic component ratio-fault type decision tree comprises a pre-established corresponding relation between the characteristic component ratio and the fault type, and the fault type is one of a metal protrusion defect, a free metal particle defect, an insulator surface metal pollution defect and an air gap defect.
2. SF-based as in claim 1 6 A fault diagnosis method for direct current gas insulation equipment for decomposition component analysis is characterized in that: the characteristic component proportion is
R 1 ＝c(SOF 2 +SO 2 +SO2F 2 )/c(CO 2 +log 10 CF 4 )，
R 2 ＝c(SOF 2 +SO 2 )/c(SO 2 F 2 )；
In the feature component ratio-fault type decision tree, R 1 、R 2 The corresponding relation with the fault type is as follows:
if R is 1 Not less than 2.70 and R 2 Judging that the metal protrusion is defective if the number is more than or equal to 2.64;
if R is 1 Not less than 2.70 and R 2 <2.64, judging the defect as a free metal particle defect;
if R is 1 <2.70 and R 2 Judging that the metal pollution defect on the surface of the insulator is more than or equal to 2.37;
if R is 1 <2.70 and R 2 <2.37, the defect is judged as an air gap defect.
3. SF-based as in claim 2 6 The fault diagnosis method of the direct current gas insulation equipment for decomposition component analysis is characterized by comprising the following steps of: the metal material related to the defects of the metal protrusions, the free metal particles and the metal dirt on the surface of the insulator is any one or more of aluminum, copper and stainless steel.
4. SF-based as in any of claims 1 to 3 6 A fault diagnosis method for direct current gas insulation equipment for decomposition component analysis is characterized in that: and in the step S2, detecting by using a gas chromatograph-mass spectrometer.
5. SF-based according to any of claims 1 to 3 6 A fault diagnosis method for direct current gas insulation equipment for decomposition component analysis is characterized in that: in the step S1, the sampling timing is:
in the normal operation process of the direct current gas insulation equipment, the equipment is sampled periodically, and the sampling period is 1 time of sampling every 2 months;
immediately sampling direct current gas insulation equipment with possible insulation defects to perform fault diagnosis;
after the fault is cleared, the direct current gas insulation equipment firstly samples in a short period until gas components in a period of time are normal, and then recovers to a normal sampling period.
6. A fault diagnosis system characterized by: comprises a data input device, a memory, a processor and a computer program stored on the memory and operable on the processor;
wherein, the data input device is used for inputting the content data of the characteristic gas and storing the content data on the memory, and when the processor executes the program, the calculation and judgment process of step S3 in the method of claim 1 or 2 is realized according to the input characteristic gas content data, and the judgment result is output.
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| CN102323346A (en) * | 2011-07-26 | 2012-01-18 | 广西电网公司电力科学研究院 | Three-specific value method for diagnosing fault of SF6 electrical equipment |
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| CN110927267A (en) * | 2019-11-22 | 2020-03-27 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Gas insulated switchgear fault diagnosis method based on decomposition component analysis method |
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