CN105629109B - Oil-immersed transformer fault diagnosis method based on ART1 neural network - Google Patents
Oil-immersed transformer fault diagnosis method based on ART1 neural network Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
Oil-immersed transformer fault diagnosis method disclosed by the invention based on ART1 neural network, is specifically implemented according to the following steps: step 1 utilizes ART1 neural network specific algorithm, oil-immersed transformer fault diagnosis model of the building based on ART1 neural network；Step 2, the input and output amount that oil-immersed transformer fault diagnosis model of the building based on ART1 neural network obtained through step 1 is determined using four ratio methods；Step 3, after step 2, be arranged the oil-immersed transformer fault diagnosis based on ART1 neural network parameter；Step 4 carries out learning training to fault sample using the oil-immersed transformer fault diagnosis model based on ART1 neural network；Step 5 carries out identifying and diagnosing to physical fault data type using the oil-immersed transformer fault diagnosis model based on ART1 neural network.The present invention is based on the oil-immersed transformer fault diagnosis methods of ART1 neural network, correctly can carry out real-time online fault diagnosis for the different types of Superheated steam drier of oil-immersed transformer.
Description
Technical field
The invention belongs to power transformer resultant fault monitoring method technical fields, and in particular to one kind is based on ART1 nerve
The oil-immersed transformer fault diagnosis method of network.
Background technique
Power transformer is most important electric device equipment in electric system, and causes power system accident most
One of electrical equipment.The security and stability that its operating status directly affects power supply system is horizontal.Power transformer is found in time
Potentiality failure ensures that transformer safety is effectively run, is power department to improve the reliability of transformer stable power-supplying
One problem of crucial importance of concern.Therefore, diagnosing fault of power transformer technology is studied, the operation and maintenance of transformer are improved
Level has important practical significance.
Current diagnosing fault of power transformer technology, although a variety of effective method for diagnosing faults have been developed,
Major part is superposition accumulation to a certain extent, and most of method for diagnosing faults itself still lack perfect theoretical basis and are
The concept system of systemization.In addition, most of method for diagnosing faults are all the characteristic signals that are shown using object to diagnose spy
The failure for levying type, generally requires a large amount of failure training sample, and large-scale effectively fault sample data are in practice
It is difficult to obtain.Power transformer all makes solution gas in transformer oil because of voltage class, insulation system and fault degree difference
Body content has larger randomness, and there are certain redundant datas, and this requires method for diagnosing faults to have very strong fault-tolerance,
The research of this respect is also not nearly enough deep at present.
On the basis for fully understanding various fault conditions and fault diagnosis the relevant technologies existing for oil-immersed power transformer
On, in conjunction with home and abroad related scientific research mechanism power transformer on-line monitoring, intelligent diagnostics and state repair the research in field at
It is particularly important to develop a kind of method mainly for research diagnosing fault of power transformer for fruit and practical experience.
Summary of the invention
The purpose of the present invention is to provide a kind of oil-immersed transformer fault diagnosis method based on ART1 neural network, energy
It is enough correctly to carry out real-time online fault diagnosis for the different types of Superheated steam drier of oil-immersed transformer.
Institute of the invention is using technical solution, based on the oil-immersed transformer fault diagnosis method of ART1 neural network, tool
Body follows the steps below to implement:
Step 1 utilizes ART1 neural network specific algorithm, oil-immersed transformer failure of the building based on ART1 neural network
Diagnostic model；
Step 2 determines oil-immersed transformer of the building based on ART1 neural network obtained through step 1 using four ratio methods
The input and output amount of fault diagnosis model；
Step 3, after step 2, be arranged the oil-immersed transformer fault diagnosis based on ART1 neural network parameter；
Step 4, using the oil-immersed transformer fault diagnosis model based on ART1 neural network to fault sample
Practise training；
Step 5, using the oil-immersed transformer fault diagnosis model based on ART1 neural network to physical fault data class
Type carries out identifying and diagnosing.
The features of the present invention also characterized in that:
Step 1 is specifically implemented according to the following steps:
Step 1.1 carries out initialization process to the connection weight between ART1 neural network model input layer and output layer,
The specific method is as follows:
The input layer of setting ART1 neural network model has N number of neuron, and output layer has M neuron；
Two-value failure input vector Mode AkWith output vector BkIt is as follows respectively:
Wherein, k=1,2 ..., p, p are the number for inputting mode of learning；
Enable tij(0)=1,I=1,2 ..., N, j=1,2 ..., M；
Wherein, Vigilance parameter 0 < ρ≤1, tijFor network model feedback link weight, WijFor network model feedforward connection weight
Value；
Step 1.2, after step 1.1, by two-value failure input vector modeIt is supplied to net
The input layer of network；
Step 1.3, after step 1.2, calculate each neuron of ART1 neural network model output layer weighted input and, tool
Body is implemented according to following algorithm:
Wherein, WijFor network model feedforward connection weight；
For specific composition two-value failure input vector Mode AkValue, specially 0 or 1；
SjFor each neuron of ART1 neural network model output layer weighted input and；
Step 1.4, after step 1.3, select the network model optimal classification of input pattern as a result, the result is according to following
Algorithm is computed acquisition:
In formula, enabling the output of neuron g is 1, SjFor the weighted input of each neuron of ART1 neural network model output layer
With；
Step 1.5 calculates following three formulas, and judges calculated result, judges final calculation result
The Vigilance parameter ρ (0 < ρ≤1) for whether meeting the setting of ART1 neural network model, that is, judge that can ART1 neural network model connect
By this recognition result:
If last formula is set up, 1.7 are entered step；
If last formula is invalid, 1.6 are entered step；
Step 1.6, after step 1.5, cancel recognition result, method particularly includes:
The output valve of output layer neuron g is first reset to 0, and by this neuron exclude identification next time range it
Outside, then return step 1.5；
If all sharp used neurons are all unable to satisfy last formula in step 1.5, select one it is new
Then neuron is transferred to step 1.7 as classification results；
Step 1.7 receives recognition result, and adjusts connection weight according to following algorithm:
Wherein, i=1,2 ..., N；
T is current time；
For the value of specific composition two-value failure input vector Mode A k, specifically: 0 or 1；
tgiFor network model feedback link weight；
wigFor network model feedforward connection weight；
Step 1.8 rejoins all neurons resetted in step 1.6 in identification range, and return step 1.2 is under
One mode carries out identification storage；
The oil-immersed transformer fault diagnosis based on ART1 neural network has just been constructed by 1.1~step of step 1.8
Model.
Step 2 is specific to be implemented in accordance with the following methods:
First, the extraction of fault characteristic gases component is carried out to failure transformer；
Secondly, measurement oil dissolved gas CH4、C2H2、C2H6、C2H4、H2Concentration of component content；
Finally, fault feature vector T is obtained using four ratio methods:
Two concentration of component ratios are then indicated with 1 if more than 1；
If two concentration of component ratios are indicated less than 1 with 0；
1 or so, then it represents that the exposure of the intermediate change process of nature of trouble, i.e. nature of trouble is less obvious；
Ratio is bigger, then the display of nature of trouble is more obvious.
The method for obtaining fault feature vector T using four ratio methods involved in step 2 is specific as follows:
Utilize five kinds of main feature gas CH4、C2H2、C2H6、C2H4、H2Form four reduced values, it may be assumed that takeBinary coding is carried out to its ratio, obtains fault feature vector T.
Step 3 is specifically implemented according to the following steps:
Step 3.1, environment and condition according to oil-immersed power transformer actual motion, not for transformer fault diagnosis
The specific Vigilance parameter ρ (0 < ρ≤1) of ART1 neural network is arranged to realize to the fast of transformer fault type in same detection accuracy
Speed accurately identification judgement；
Step 3.2 carries out data initialization after step 3.1, then to ART1 neural network, waits fault feature vector T
Input, then realize that ART1 neural network model identifies the diagnosis of transformer fault.
Step 4 is specific to be implemented in accordance with the following methods:
Using fault sample data, the study of fault sample is carried out to ART1 neural network, is realized to transformer fault class
The classification of type；Input of the binary coding fault feature vector T obtained in conjunction with four ratio methods as ART1 neural network, selection
The sample data of 20 groups of known fault types comprising nine kinds of transformer fault types carries out study instruction to ART1 neural network
Practice.
Step 5 is specific to be implemented in accordance with the following methods:
The fault feature vector T that the fault data of the oil-immersed transformer under actual working environment is obtained through step 2, will be real
Oil-immersed transformer fault diagnosis model of the fault feature vector T input in border based on ART1 neural network carries out accident analysis, defeated
Be out of order type.
The beneficial effects of the present invention are:
(1) the present invention is based on the oil-immersed transformer fault diagnosis methods of ART1 neural network, can be realized transformer event
The real-time online of barrier data learns and accurately identifies transformer fault type.
(2) the present invention is based on the oil-immersed transformer fault diagnosis methods of ART1 neural network, are not necessarily to a large amount of transformer
Fault data sample is trained, and carries out reduction without the fault data sample to redundancy.
(3) the present invention is based in the oil-immersed transformer fault diagnosis method of ART1 neural network, network is to transformer
Fault data being capable of self-organizing, adaptive learning, moreover it is possible to can carry out classifying from normalizing to unknown transformer fault data type.
(4) compared with ART2 and ART3 neural network, the present invention is based on the oil-immersed transformer failures of ART1 neural network to examine
The ART1 neural network algorithm used in disconnected method is simply easily realized, causes failure to be examined it is not necessary that excessive parameter value is manually set
Disconnected objectivity reduces.
(5) the present invention is based on the oil-immersed transformer fault diagnosis methods of ART1 neural network, by adjusting Vigilance parameter ρ
The value of (0 < ρ≤1) can carry out the diagnosis of technique of different accuracy to the oil-immersed transformer failure under the conditions of particular job, real
The maximization of existing trouble diagnosibility.
(6) the present invention is based on the oil-immersed transformer fault diagnosis method of ART1 neural network, principles are as follows: in conjunction with tradition
Four ratio methods that are used in the analysis of DGA technology, by oil-immersed transformer work in different Superheated steam drier types carry out two
Scale coding constitutes fault feature vector, and the fault feature vector recycled is trained and learns to ART1 neural network
It practises；Shown by theory analysis and emulation experiment data result: the present invention is based on the events of the oil-immersed transformer of ART1 neural network
Hindering diagnostic method correctly can carry out real-time online fault diagnosis for the different types of Superheated steam drier of oil-immersed transformer,
It has broad application prospects.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts of the oil-immersed transformer fault diagnosis method of ART1 neural network.
Specific embodiment
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
The present invention is based on the oil-immersed transformer fault diagnosis methods of ART1 neural network, as shown in Figure 1, specifically according to
Lower step is implemented:
Step 1 utilizes ART1 neural network specific algorithm, oil-immersed transformer failure of the building based on ART1 neural network
Diagnostic model is specifically implemented according to the following steps:
Step 1.1 carries out initialization process to the connection weight between ART1 neural network model input layer and output layer,
The specific method is as follows:
The input layer of setting ART1 neural network model has N number of neuron, and output layer has M neuron；
Two-value failure input vector Mode AkWith output vector BkIt is as follows respectively:
Wherein, k=1,2 ..., p, p are the number for inputting mode of learning；
Enable tij(0)=1,I=1,2 ..., N, j=1,2 ..., M；
Wherein, Vigilance parameter 0 < ρ≤1, tijFor network model feedback link weight, WijFor network model feedforward connection weight
Value；
Step 1.2, after step 1.1, by two-value failure input vector modeIt is supplied to net
The input layer of network；
Step 1.3, after step 1.2, calculate each neuron of ART1 neural network model output layer weighted input and, tool
Body is implemented according to following algorithm:
Wherein, WijFor network model feedforward connection weight；
For specific composition two-value failure input vector Mode AkValue (0 or 1)；
SjFor each neuron of ART1 neural network model output layer weighted input and.
Step 1.4, after step 1.3, select the network model optimal classification of input pattern as a result, the result is according to following
Algorithm is computed acquisition:
In formula, enabling the output of neuron g is 1, SjFor the weighted input of each neuron of ART1 neural network model output layer
With；
Step 1.5 calculates following three formulas, and judges calculated result, judges final calculation result
The Vigilance parameter ρ (0 < ρ≤1) for whether meeting the setting of ART1 neural network model, that is, judge that can ART1 neural network model connect
By this recognition result:
If last formula is set up, 1.7 are entered step；
If last formula is invalid, 1.6 are entered step；
Step 1.6, after step 1.5, cancel recognition result, method particularly includes:
The output valve of output layer neuron g is first reset to 0, and by this neuron exclude identification next time range it
Outside, then return step 1.5；
If all sharp used neurons are all unable to satisfy last formula in step 1.5, select one it is new
Then neuron is transferred to step 1.7 as classification results；
Step 1.7 receives recognition result, and adjusts connection weight according to following algorithm:
Wherein, i=1,2 ..., N；T is current time；
For specific composition two-value failure input vector Mode AkValue (0 or 1)；
tgiFor network model feedback link weight；
wigFor network model feedforward connection weight；
Step 1.8 rejoins all neurons resetted in step 1.6 in identification range, and return step 1.2 is under
One mode carries out identification storage；
The oil-immersed transformer fault diagnosis based on ART1 neural network has just been constructed by 1.1~step of step 1.8
Model.
Step 2 determines oil-immersed transformer of the building based on ART1 neural network obtained through step 1 using four ratio methods
The input and output amount of fault diagnosis model is specifically implemented in accordance with the following methods:
The extraction for carrying out fault characteristic gases component to failure transformer first, measures oil dissolved gas CH later4、
C2H2、C2H6、C2H4、CO、CO2And H2Concentration of component content；
In the inventive solutions, not seven kinds of main feature gases in the above transformer oil directly as network
Input, but use four ratio methods and obtain fault feature vector, five kinds of main feature gas CH therein is only utilized4、
C2H2、C2H6、C2H4、H2；
Four ratio methods are to make full use of five kinds of main feature gas CH4、C2H2、C2H6、C2H4、H2Form four reduced values, it may be assumed that
It takesBinary coding is carried out to its ratio, obtains fault feature vector T:
Two concentration of component ratios are then indicated with 1 if more than 1；
If two concentration of component ratios are indicated less than 1 with 0；
1 or so, then it represents that the exposure of the intermediate change process of nature of trouble, i.e. nature of trouble is less obvious；
Ratio is bigger, then the display of nature of trouble is more obvious；
If there are two types of the failures of property to exist simultaneously, such as: 1011, then it may be interpreted as continuous electric spark and overheat.
Fault feature vector T is obtained using four ratio methods, every kind of fault feature vector T corresponds to a kind of fault signature type；
Then fault feature vector T is the input vector of ART1 neural network, and the output of network is corresponding to fault feature vector T
Fault signature type.
Step 3, after step 2, be arranged the oil-immersed transformer fault diagnosis based on ART1 neural network parameter, specifically
It follows the steps below to implement:
Step 3.1, environment and condition according to oil-immersed power transformer actual motion, not for transformer fault diagnosis
The specific Vigilance parameter ρ (0 < ρ≤1) of ART1 neural network is arranged to realize to the fast of transformer fault type in same detection accuracy
Speed accurately identification judgement；
Step 3.2 carries out data initialization after step 3.1, then to ART1 neural network, waits fault feature vector T
Input, then realize that ART1 neural network model identifies the diagnosis of transformer fault.
Step 4, using the oil-immersed transformer fault diagnosis model based on ART1 neural network to fault sample
Training is practised, is specifically implemented in accordance with the following methods:
Using fault sample data, the study of fault sample is carried out to ART1 neural network, to realize to transformer event
Hinder the classification of type；Input of the binary coding fault feature vector T obtained in conjunction with four ratio methods as ART1 neural network,
Select the sample data of 20 groups of known fault types comprising nine kinds of transformer fault types to ART1 neural network
Practise training；
In order to precisely objectively embody the trouble diagnosibility of ART1 neural network, input data sample learning 2 is taken turns to come
Training network, then examines ART1 neural network to the accuracy rate of transformer fault diagnosis by test data sample.
Step 5, using the oil-immersed transformer fault diagnosis model based on ART1 neural network to physical fault data class
Type carries out identifying and diagnosing, specifically implements in accordance with the following methods:
The fault feature vector T that the fault data of the oil-immersed transformer under actual working environment is obtained through step 2, will be real
Oil-immersed transformer fault diagnosis model of the fault feature vector T input in border based on ART1 neural network carries out accident analysis, defeated
Be out of order type.
The transformer of certain substation 180MVA 10 groups of fault datas since running have been put into production into as ART1 nerve net
The input of network, and diagnose and also compare ART1 neural network and routine three-ratio method, ART1 nerve net is compared with this
Diagnosis capability of the network to the transformer fault type.Show the oil-immersed transformer event based on ART1 neural network by comparing
Barrier diagnosis to transformer fault type have more accurate diagnostic analysis ability, accuracy rate 90%, and routine three-ratio method
Accuracy rate only has 70%.
The present invention is based on be applied to ART1 nerve net in the oil-immersed transformer fault diagnosis method of ART1 neural network
Network, ART1 neural network can while study while work, to transformer fault data can self-organizing, self study, to unknown change
Depressor fault type can carry out classifying and remembering from normalizing.When testing it, ART1 neural network can be to having remembered
The sample automatic identification and quick response of study go out transformer fault type.
The present invention is based on the oil-immersed transformer fault diagnosis methods of ART1 neural network to have the advantage that
(1) the present invention is based on refreshing based on ART1 involved in the oil-immersed transformer fault diagnosis method of ART1 neural network
Fault Diagnosis Model for Power Transformer through network is not necessarily to a large amount of fault data sample, does not require to carry out reduction to fault data, keep away
Redundant data and processing big data problem bring inconvenience are exempted from.
(2) it the present invention is based in the oil-immersed transformer fault diagnosis method of ART1 neural network, is obtained using four ratio methods
To transformer fault feature binary input vectors, dimension-reduction treatment is carried out to fault data feature input vector, reduces ART1
The complexity of neural network, so that ART1 Neural Network Diagnosis model calculation speed is faster, diagnostic accuracy is also higher.
(3) the present invention is based on constructed in the oil-immersed transformer fault diagnosis method of ART1 neural network based on ART1 mind
Oil-immersed transformer fault diagnosis diagnostic model through network can be to the event according to the transformer fault instance data being collected into
Barrier diagnostic model is trained and emulates.
The present invention is based on the oil-immersed transformer fault diagnosis methods of ART1 neural network, are tied by theory analysis and emulation
Fruit shows the transformer fault diagnosis energy with higher of the oil-immersed transformer fault diagnosis method based on ART1 neural network
Power；More importantly the strategy can self-organizing, self study, oil-immersed transformer unknown failure type can be carried out from normalizing
Reason memory, is not necessarily to a large amount of learning training fault data sample, the diagnosis of real-time online transformer fault may be implemented；It is in oil
Also superior to conventional method and other traditional neural network methods in immersion transformer fault diagnosis performance.
Claims (3)
1. the oil-immersed transformer fault diagnosis method based on ART1 neural network, which is characterized in that specifically according to the following steps
Implement:
Step 1 utilizes ART1 neural network specific algorithm, oil-immersed transformer fault diagnosis of the building based on ART1 neural network
Model；
Step 2 determines oil-immersed transformer failure of the building based on ART1 neural network obtained through step 1 using four ratio methods
The input and output amount of diagnostic model；
The step 2 is specific to be implemented in accordance with the following methods:
First, the extraction of fault characteristic gases component is carried out to failure transformer；
Secondly, measurement oil dissolved gas CH4、C2H2、C2H6、C2H4、H2Concentration of component content；
Finally, fault feature vector T is obtained using four ratio methods:
Two concentration of component ratios are then indicated with 1 if more than 1；
If two concentration of component ratios are indicated less than 1 with 0；
1 or so, then it represents that the exposure of the intermediate change process of nature of trouble, i.e. nature of trouble is less obvious；
Ratio is bigger, then the display of nature of trouble is more obvious；
The method for obtaining fault feature vector T using four ratio methods involved in the step 2 is specific as follows:
Utilize five kinds of main feature gas CH4、C2H2、C2H6、C2H4、H2Form four reduced values, it may be assumed that takeBinary coding is carried out to its ratio, obtains fault feature vector T；
Step 3, after step 2, according to the environment and condition of oil-immersed transformer actual motion, not for transformer fault diagnosis
The specific Vigilance parameter ρ of ART1 neural network, 0 ρ≤1 <, to realize to the fast of transformer fault type is arranged in same detection accuracy
Speed accurately identification judgement；Data initialization is carried out to ART1 neural network again, waits the input of fault feature vector T；
Step 4, using fault sample data, the study of fault sample is carried out to ART1 neural network, is realized to transformer fault
The classification of type；Input of the binary coding fault feature vector T obtained in conjunction with four ratio methods as ART1 neural network, choosing
The sample data for having selected 20 comprising nine kinds of transformer fault types groups of known fault types learns ART1 neural network
Training；
Step 5, using the oil-immersed transformer fault diagnosis model based on ART1 neural network to physical fault data type into
Row identifying and diagnosing.
2. the oil-immersed transformer fault diagnosis method according to claim 1 based on ART1 neural network, feature exist
In the step 1 is specifically implemented according to the following steps:
Step 1.1 carries out initialization process, specific method to the connection weight between ART1 neural network input layer and output layer
It is as follows:
The input layer of setting ART1 neural network has N number of neuron, and output layer has M neuron；
Two-value failure input vector Mode AkWith output vector BkIt is as follows respectively:
Wherein, k=1,2 ..., p, p are the number for inputting mode of learning；
Enable tij(0)=1,
Wherein, tijFor network model feedback link weight, WijFor network model feedforward connection weight；
Step 1.2, after step 1.1, by two-value failure input vector modeIt is supplied to network
Input layer；
Step 1.3, after step 1.2, calculate each neuron of ART1 neural network output layer weighted input and, specifically according to
Lower algorithm is implemented:
Wherein, WijFor network model feedforward connection weight；
For specific composition two-value failure input vector Mode AkValue, specifically: 0 or 1；
SjFor each neuron of ART1 neural network output layer weighted input and；
Step 1.4, after step 1.3, select the network model optimal classification of input pattern as a result, the result is according to following algorithm
It is computed acquisition:
In formula, enabling the output of neuron g is 1, SjFor each neuron of ART1 neural network output layer weighted input and；
Step 1.5 calculates following three formulas, and judges calculated result whether judge final calculation result
Meet the Vigilance parameter ρ of ART1 neural network setting, 0 ρ≤1 < judges that can ART1 neural network receive this identification knot
Fruit:
For specific composition two-value failure input vector Mode AkValue, specifically: 0 or 1；
tgiFor network model feedback link weight；
If last formula is set up, 1.7 are entered step；
If last formula is invalid, 1.6 are entered step；
Step 1.6, after step 1.5, cancel recognition result, method particularly includes:
The output valve of the neuron g of output layer is first reset to 0, and by this neuron exclude identification next time range it
Outside, then return step 1.5；
If all sharp used neurons are all unable to satisfy last formula in step 1.5, a new nerve is selected
Member is used as classification results, is then transferred to step 1.7；
Step 1.7 receives recognition result, and adjusts connection weight according to following algorithm:
Wherein, i=1,2 ..., N；
T is current time；
For specific composition two-value failure input vector Mode AkValue, specifically: 0 or 1；
tgiFor network model feedback link weight；
wigFor network model feedforward connection weight；
Step 1.8 rejoins all neurons resetted in step 1.6 in identification range, and return step 1.2 is to next
Mode carries out identification storage；
The oil-immersed transformer fault diagnosis model based on ART1 neural network has just been constructed by 1.1~step of step 1.8.
3. the oil-immersed transformer fault diagnosis method according to claim 1 based on ART1 neural network, feature exist
In the step 5 is specific to be implemented in accordance with the following methods:
The fault feature vector T of fault data is obtained through step 2, fault feature vector T is inputted based on ART1 neural network
Oil-immersed transformer fault diagnosis model carries out accident analysis, exports fault type.
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| CN106338673A (en) * | 2016-09-29 | 2017-01-18 | 国网山东省电力公司电力科学研究院 | Transformer winding fault characteristic parameter extraction method |
| CN106840406B (en) * | 2016-11-29 | 2019-08-16 | 浙江中新电力工程建设有限公司自动化分公司 | Isolation switch method for diagnosing faults based on matrix neural network |
| CN107133378B (en) * | 2017-03-28 | 2019-07-23 | 浙江大学 | Oil-immersed transformer fault early warning method based on the estimation of Higher Dimensional Linear Models height |
| CN110110784B (en) * | 2019-04-30 | 2020-03-24 | 贵州电网有限责任公司 | Transformer fault identification method based on transformer related operation data |
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