CN114814501A - On-line diagnosis method for capacitor breakdown fault of capacitor voltage transformer - Google Patents
On-line diagnosis method for capacitor breakdown fault of capacitor voltage transformer Download PDFInfo
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Abstract
The application relates to an on-line diagnosis method for a capacitor breakdown fault of a capacitor voltage transformer, which comprises the following specific steps: s1, collecting secondary voltage amplitude data of CVTs of the same phase and the same model on the same bus by using a CVT online monitoring system, constructing a data set and attaching corresponding labels; s2, constructing a feature vector set by using the inter-group amplitude ratio parameters, and performing feature extraction on the preprocessed data set; s3, training a KNN model by adopting a KNN machine learning mode, adjusting corresponding hyper-parameters, and outputting an optimal KNN model; and S4, inputting secondary voltage amplitude data in the CVT on-line monitoring system into the optimal KNN model, and diagnosing the capacitor breakdown state of the CVT to be detected in real time. The invention carries out online diagnosis on breakdown faults of the CVT capacitor and feeds back the state of the capacitor in the CVT in real time, thereby ensuring the stability and the safety performance of the operation of a power grid.
Description
Technical Field
The application relates to the field of capacitor breakdown fault online diagnosis, in particular to a capacitor breakdown fault online diagnosis method for a capacitor voltage transformer.
Background
Compared with the traditional electromagnetic transformer, the Capacitor Voltage Transformer (CVT) has the characteristics of strong anti-ferromagnetic resonance capability, low manufacturing cost, small volume, light weight and the like. However, because the CVT has a complex internal structure, the operating state is susceptible to environmental factors, and abnormal states such as error over-tolerance and insulation performance degradation are likely to occur in the long-term operation process, thereby affecting the accuracy of electric energy metering and the safety of the power system. Among them, as one of the main factors affecting the state of the CVT, the state of the insulation performance is mainly determined by the state of the capacitor and the insulating medium in the insulating structure inside the CVT. In contrast to insulating media, where there are many well-established methods for characterizing the state, there is currently no accurate method for characterizing the state of capacitance on-line. And the capacitor state is monitored on line in real time, so that the state of the CVT can be judged in time, and related operation and maintenance personnel can conveniently overhaul and maintain the work. If the capacitor state is not found to be abnormal in time, the state of the transformer is abnormal, so that the operation of a power grid is influenced, and even the personnel safety problem caused by explosion is caused. In order to avoid inaccuracy of a secondary information system information source, reduce loss of electric energy measurement and ensure normal operation of a measurement and control protection device, the on-line diagnosis of the breakdown fault of the capacitor of the CVT is a technical problem.
Disclosure of Invention
The embodiment of the application aims to provide an online diagnosis method for capacitor breakdown faults of a capacitor voltage transformer, which is used for online diagnosis of the capacitor breakdown faults of a CVT (constant voltage transformer), and feeding back the state of a capacitor in the CVT in real time, so that the running stability and safety performance of a power grid are ensured.
In order to achieve the above purpose, the present application provides the following technical solutions:
the embodiment of the application provides an on-line diagnosis method for a capacitor breakdown fault of a capacitor voltage transformer, which comprises the following specific steps:
s1, collecting secondary voltage amplitude data of CVTs of the same phase and the same model on the same bus by using a CVT online monitoring system, constructing a data set and attaching corresponding labels;
s2, constructing a feature vector set by using the inter-group amplitude ratio parameters, and performing feature extraction on the preprocessed data set;
s3, training a KNN model by adopting a KNN machine learning mode, adjusting corresponding hyper-parameters, and outputting an optimal KNN model;
and S4, inputting secondary voltage amplitude data in the CVT on-line monitoring system into the optimal KNN model, and diagnosing the capacitor breakdown state of the CVT to be detected in real time.
The specific method for constructing the data set comprises the steps of screening historical data of secondary voltage amplitude values and metering errors of CVTs (continuously variable Transmission) which are of the same type and have the same phase and adopt the same wiring mode on one bus from an online monitoring system, manually classifying sample data at each moment according to the relation between capacitance breakdown and CVT transformation ratio, wherein the category of the capacitance breakdown state isWhereinIndicating that the capacitance has not broken down,indicating a breakdown of a single high-voltage capacitor,indicating that the two high-voltage capacitors are broken down,indicating a single medium voltage capacitance breakdown and,and representing two medium-voltage capacitor breakdowns, taking one CVT as a CVT to be tested for capacitor breakdown fault diagnosis, and selecting a sample with only the CVT to be tested in an abnormal capacitance state or all the CVTs in a normal state from the selected data set as a data set D for subsequent online capacitor breakdown diagnosis.
The construction of the feature vector set by using the inter-group amplitude ratio parameters is to extract the features of the preprocessed data set,
splitting the data set D into a training set and a test set;
carrying out normalization processing on samples in the training set;
and constructing a characteristic vector of the CVT capacitance breakdown online diagnosis according to the inter-group amplitude ratio parameter so as to eliminate the influence of primary voltage on the capacitance breakdown online diagnosis result.
The specific method for splitting the data set D into the training set and the test set is to set the sample proportion of the training set and the test set as 7: if the number of samples in the data set D isThen the number of samples in the training set isThe number of samples in the test set isRandomly non-replaced drawn samples from the data set D to the training sample setIn, co-extractionNext, the remaining samples in data set D constitute the test set。
The normalization process for the samples in the training set specifically includes,
WhereinIs the mean value of the features of the dimension,for the standard deviation of the dimension feature, record the correspondence,Apply it to test setAnd (4) normalization processing.
Parameter of amplitude ratio between groupsRefers to the ratio of the secondary voltage amplitudes of two CVTs of the same model representing the same phase, in different groups located on the same bus,
whereinRespectively representing the secondary voltage amplitudes of two different CVTs according to a metering error ratio difference formula of the CVT
WhereinIs the ratio of the rated variation to the rated variation,is the voltage of the secondary battery, and is,is a primary voltage, and the form conversion is carried out to obtain:
substituting the form-converted ratio equation into the interclass amplitude ratio parameter
Because two CVTs are connected to the same bus, have the same model and represent the same phase, the primary voltage and the rated transformation ratio of the CVT are the same, namely the formula can be simplified into
That is, it is approximately considered that the secondary voltage value of the CVT between the two sets is related to the ratio difference of the two CVTs, thereby eliminating the influence of the primary voltage value, in the same bus, assuming that there are n sets of CVTs of the same model,for the CVTs located in the same phase in the n sets of CVTs, selecting one of the CVTs to perform capacitance breakdown online diagnosis, and marking the CVT as G, the set of the sets of the CVTs located in the same phase on the same bus can be marked as GThe feature vector set of the inter-group amplitude ratio parameters of the individual samples set accordingly is
Taking the characteristic vector set as a characteristic vector set for on-line diagnosis of the capacitor breakdown, and after the characteristic vector set extraction is carried out on the training set after the normalization operation is completed, taking the sample of the training set asThe corresponding category isThe samples of the test set corresponding to the training set using normalizationAndcorresponding normalization is carried out to obtain a training setAnd corresponding categories。
The method for training the KNN model by adopting the KNN machine learning mode, adjusting the corresponding hyper-parameters and outputting the optimal KNN model specifically comprises the following steps of,
s31, selecting the hyperparameter in KNN through knowledge and experience in the field,Is a positive integer;
s32. selecting a sample from the test set, calculating its distance to all training samples, wherein the distance metric used is Min's distance
Wherein,Respectively represent twoThe dimension samples are then processed to obtain a dimensional sample,indicates that is the second of the sampleDimensional features, s is a constant;
s33, selecting the nearest distance in all the distances obtained by the last step of calculationTraining sample corresponding to each distance;
S34, the selected training samples correspond to the classes respectivelyCounting W according to L, namely calculating the number of the various types in L appearing in WSelecting the most numerous classesThe corresponding class is used as the class of the test sample;
s35, repeating the steps from S32 to S34, and classifying all samples in the test set to obtain a corresponding class setThe result of the classificationAnd the actual resultsComparing to obtain the precision of the scheme;
s36. selected according to step S31Value, selectionRepeating the steps S31 to S35 for a plurality of right and left data to output a plurality of different accuracies, and selecting the most accurate data as the optimal parameters of KNN.
The step S4 is to collect the amplitude of the CVT on-line monitoring system at the current time, and input the amplitude with the best hyper-parameter after data preprocessingAnd outputting the on-line diagnosis result of the capacitor breakdown of the CVT to be detected at the current moment in the KNN model.
Compared with the prior art, the invention has the beneficial effects that:
a scheme for on-line diagnosis of the breakdown fault of the capacitor voltage transformer based on the KNN is provided. And constructing a characteristic vector according to the inter-group amplitude ratio parameters, and constructing a capacitor breakdown online diagnosis model of the capacitor voltage transformer by using KNN. And inputting real-time data of the CVT on-line monitoring system into the trained KNN model, and diagnosing the breakdown fault state of the current CVT capacitor in real time. And performing online diagnosis on breakdown faults of the capacitor of the CVT, and feeding back the state of the capacitor in the CVT in real time, thereby ensuring the stability and safety performance of the operation of a power grid.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
In a first aspect of the present invention, an online diagnosis method for a capacitor breakdown fault of a capacitor voltage transformer is provided, as shown in fig. 1,
step 1: acquisition of a data set
And acquiring secondary voltage amplitude information through a CVT online monitoring system. The specific mode is that historical data of secondary voltage amplitude and metering error of CVTs of the same type and the same phase adopting the same wiring mode on one bus are screened from an online monitoring system, sample data at each moment can be manually classified according to the relation between capacitance breakdown and CVT transformation ratio, and the category of the capacitance breakdown state isWhereinIndicating that the capacitance has not broken down,indicating a single high-voltage capacitance breakdown and,indicating that the two high-voltage capacitors are broken down,indicating a single medium voltage capacitance breakdown and,and representing two medium-voltage capacitor breakdowns, taking one CVT as a CVT to be tested for capacitor breakdown fault diagnosis, and selecting a sample with only the CVT to be tested in an abnormal capacitance state or all the CVTs in a normal state from the selected data set as a data set D for subsequent online capacitor breakdown diagnosis.
Step2 data preprocessing
Step2.1 dataset splitting
Gathering CVT historical data collected by step1And splitting the training set and the test set. The specific method is that the sample proportion of the training set and the test set is set as 7: if the number of samples in the data set D isThen the number of samples in the training set isThe number of samples in the test set isRandomly non-replaced drawn samples from the data set D to the training sample setIn, co-extractionNext, the remaining samples in data set D constitute the test set。
Step2.2 normalization treatment
For the training setNormalizing the sample in (1)And (6) chemical treatment. Training set with z-score normalizationNormalized by z-score of the formula
WhereinIs the mean value of the features of the dimension,for the standard deviation of the dimension feature, record the correspondence,Apply it to test setAnd (4) normalization processing.
Construction of Step2.3 feature vectors
And constructing a characteristic vector of the CVT capacitance breakdown online diagnosis according to the inter-group amplitude ratio parameter so as to eliminate the influence of primary voltage on the capacitance breakdown online diagnosis result. Parameter of amplitude ratio between groupsRefers to the ratio of the secondary voltage amplitudes of two CVTs of the same model representing the same phase, in different groups located on the same bus,
whereinRespectively representing the secondary voltage amplitudes of two different CVTs according to a metering error ratio difference formula of the CVT
WhereinIs the ratio of the rated variation to the rated variation,is the voltage of the secondary battery, and is,is a primary voltage, and the form conversion is carried out to obtain:
substituting the form-converted ratio equation into the interclass amplitude ratio parameter
Because two CVTs are connected to the same bus, have the same model and represent the same phase, the primary voltage and the rated transformation ratio of the CVT are the same, namely the formula can be simplified into
That is, it is approximately considered that the secondary voltage value of the CVT between the two sets is related to the ratio difference of the two CVTs, thereby eliminating the influence of the primary voltage value, in the same bus, assuming that there are n sets of CVTs of the same model,for n sets of CVT neutralFor the CVT with the same phase, one CVT is selected for capacitance breakdown online diagnosis, the CVT is marked as G, and the set of the groups of CVTs with the same phase under the same bus can be marked as GThe feature vector set of the inter-group amplitude ratio parameters of the individual samples set accordingly is
Taking the characteristic vector set as a characteristic vector set for on-line diagnosis of the capacitor breakdown, and after the characteristic vector set extraction is carried out on the training set after the normalization operation is completed, taking the sample of the training set asThe corresponding category isThe samples of the test set corresponding to the training set using normalizationAndcorresponding normalization is carried out to obtain a training setAnd corresponding categories。
Step3 classification of CVT for on-line diagnosis of capacitive breakdown using KNN
Step3.1 selection of hyper-parameters in KNN by domain knowledge and experience(Is a positive integer).
Step3.2 select one sample from the test set and calculate its distance to all training samples. Wherein the distance measure used is a Min-style distance
Wherein,Respectively represent twoThe dimension samples are then processed to obtain a dimensional sample,indicates that is the second of the sampleDimensional features, s is a constant;
step3.3 selects the nearest of all distances calculated by step3.2Training sample corresponding to each distance。
The classes corresponding to the training samples selected by Step3.4 are respectivelyCounting W according to L, namely calculating the number of the various types in L appearing in WSelecting the most numerous classesThe corresponding class is taken as the class of the test sample;
step3.5 repeating step3.2-step3.4, classifying all samples in the test set to obtain a corresponding category setThe result of the classificationAnd the actual resultsComparing to obtain the precision of the scheme;
step3.6 selected according to step3.1Value, selectionAnd repeating the steps of Step3.1-Step3.5 on the left and the right to output a plurality of different accuracies, and selecting the optimal parameter with the highest accuracy as KNN.
Step4 classification of CVT for on-line diagnosis of capacitive breakdown using KNN
Collecting the amplitude value in the CVT on-line monitoring system at the current moment, preprocessing the data, and inputting the data with the optimal hyper-parameterAnd outputting the on-line diagnosis result of the capacitor breakdown of the CVT to be detected at the current moment in the KNN model.
Specific application examples of the present application:
firstly, a simulation circuit experiment platform is constructed, 6 groups of three-phase four-wire CVTs of the same type are connected to a 110kV bus, an A phase is selected, the secondary voltage amplitude of each group of A phase CVT is recorded according to seconds, and 15000 samples are obtained in total.
Each sample was labeled as follows:
label 1: the corresponding ratio difference change is about 0 when the capacitance is not broken down
And 2, label: when a single high-voltage capacitor breaks down, the corresponding ratio difference change is about 0.4%;
and (3) labeling: the corresponding ratio difference change of the two high-voltage capacitors is about 0.8% when the two high-voltage capacitors are broken down;
and (4) labeling: the corresponding ratio difference change is about-0.4% when a single medium-voltage capacitor breaks down;
and (5) labeling: the corresponding change in the ratio between the two medium voltage capacitors in breakdown is about-0.8%.
I.e. each sample taken contains six secondary voltage magnitudes of the CVT and corresponding labels. Collected data set analysis is shown in the following table
The collected sample data set comprises 600 normal samples, 2400 samples with No. 1 CVT failure (600 samples in four abnormal states), 2400 samples with No. 2 CVT failure (600 samples in four abnormal states), 2400 samples with No. 3 CVT failure (600 samples in four abnormal states), 2400 samples with No. 4 CVT failure (600 samples in four abnormal states), 2400 samples with No. 5 CVT failure (600 samples in four abnormal states), and 2400 samples with No. 6 CVT failure (600 samples in four abnormal states).
And secondly, performing online monitoring analysis on the failed CVT, namely constructing six data sets,
data setThe number of samples containing normal samples and CVT No. 1 failed is 3000. Data setThe number of samples containing normal samples and CVT No. 2 failed is 3000. Data setThe samples containing normal samples and CVT No. 3 failed, total 3000. Data setThe samples containing normal samples and CVT No. 4 failed, total 3000. Data setThe samples containing normal samples and CVT No. 5 failed, total 3000. Data setThe number of samples containing normal samples and CVT No. 6 failed is 3000. Each data set was calculated as 7: 3 into training set and test set. And carrying out normalization operation on each data set, and then extracting a data feature set based on the voltage ratio difference between the groups.
Based on experience, willThe nearest neighbor number is set to 5, and the settings are different after the experimentThe method accuracy of the value mapping is shown below
As shown in the table, the highest precision squareCases 5, 10, 15 were obtained because of empirical settingsThe accuracy of the KNN model corresponding to the value is highest, so that the KNN model is selectedThe KNN method at =5 is an optimum method for this method.
The embodiment of the application provides a KNN-based capacitive voltage transformer capacitance breakdown fault on-line diagnosis scheme. And constructing a characteristic vector according to the inter-group amplitude ratio parameters, and constructing a capacitor breakdown online diagnosis model of the capacitor voltage transformer by using KNN. And inputting real-time data of the CVT on-line monitoring system into the trained KNN model, and diagnosing the breakdown fault state of the current CVT capacitor in real time.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (8)
1. The on-line diagnosis method for the capacitor breakdown fault of the capacitor voltage transformer is characterized by comprising the following specific steps of:
s1, collecting secondary voltage amplitude data of CVTs of the same phase and the same model on the same bus by using a CVT online monitoring system, constructing a data set and attaching corresponding labels;
s2, constructing a feature vector set by using the inter-group amplitude ratio parameters, and performing feature extraction on the preprocessed data set;
s3, training a KNN model by adopting a KNN machine learning mode, adjusting corresponding hyper-parameters, and outputting an optimal KNN model;
and S4, inputting secondary voltage amplitude data in the CVT on-line monitoring system into the optimal KNN model, and diagnosing the capacitor breakdown state of the CVT to be detected in real time.
2. The method according to claim 1, wherein the specific method for constructing the data set is to screen historical data of secondary voltage amplitude and metering error of CVT (constant-voltage transformer) of the same type and the same phase in the same wiring way on a bus from an online monitoring system, and manually classify sample data at each moment according to the relation between the capacitor breakdown and the CVT transformation ratio, wherein the capacitor breakdown state is classified intoIn whichIndicating that the capacitance has not broken down,indicating a single high-voltage capacitance breakdown and,indicating that the two high-voltage capacitors are broken down,indicating a single breakdown of the medium voltage capacitor,and representing two medium-voltage capacitor breakdowns, taking one CVT as a CVT to be tested for capacitor breakdown fault diagnosis, and selecting a sample with only the CVT to be tested in an abnormal capacitance state or all the CVTs in a normal state from the selected data set as a data set D for subsequent online capacitor breakdown diagnosis.
3. The on-line diagnosis method for the capacitor breakdown fault of the capacitor voltage transformer as claimed in claim 2, wherein the feature vector set is constructed by using the inter-group amplitude ratio parameter, and the feature extraction is specifically performed on the preprocessed data set,
splitting the data set D into a training set and a test set;
carrying out normalization processing on samples in the training set;
and constructing a characteristic vector of the CVT capacitance breakdown online diagnosis according to the inter-group amplitude ratio parameter so as to eliminate the influence of primary voltage on the capacitance breakdown online diagnosis result.
4. The on-line diagnosis method for the capacitor breakdown fault of the capacitor voltage transformer as claimed in claim 3, wherein the data set D is divided into the training set and the test set by setting the sample ratio of the training set to 7: if the number of samples in the data set D isThen the number of samples in the training set isThe number of samples in the test set isRandomly non-replaced drawn samples from the data set D to the training sample setIn, co-extractionNext, the remaining samples in data set D constitute the test set。
5. The on-line diagnosis method for the capacitor breakdown fault of the capacitor voltage transformer as claimed in claim 4, wherein the normalization process for the samples in the training set is specifically,
6. The on-line diagnosis method for the capacitor breakdown fault of the capacitor voltage transformer as claimed in claim 5, wherein the inter-group amplitude ratio parameterRefers to the ratio of the secondary voltage amplitudes of two CVTs of the same model representing the same phase, in different groups located on the same bus,
whereinRespectively representing the secondary voltage amplitudes of two different CVTs according to a metering error ratio difference formula of the CVT
WhereinIs the ratio of the rated variation to the rated variation,is the voltage of the secondary battery, and is,is a primary voltage, and the form conversion is carried out to obtain:
substituting the ratio difference formula after the form conversion into the intergroup amplitude ratio parameter
Because two CVTs are connected to the same bus, have the same model and represent the same phase, the primary voltage and the rated transformation ratio of the CVT are the same, namely the formula can be simplified into
Namely, the secondary voltage value of the CVT between the two groups is approximately considered to be related to the ratio difference of the two CVTsThereby eliminating the influence of the primary voltage value, in the same bus, assuming that there are n sets of CVTs of the same model,for the CVTs located in the same phase in the n sets of CVTs, selecting one of the CVTs to perform capacitance breakdown online diagnosis, and marking the CVT as G, the set of the sets of the CVTs located in the same phase on the same bus can be marked as GThe feature vector set of the inter-group amplitude ratio parameters of the individual samples set accordingly is
Taking the characteristic vector set as a characteristic vector set for on-line diagnosis of capacitor breakdown, and after extracting the characteristic vector set from the training set after normalization operation, taking the sample of the training set asThe corresponding category isThe samples of the test set corresponding to the training set using normalizationAndcorresponding normalization is carried out to obtain a training setAnd corresponding categories。
7. The on-line diagnosis method for the capacitor breakdown fault of the capacitor voltage transformer as claimed in claim 5, wherein the KNN model is trained by KNN machine learning, the corresponding hyper-parameters are adjusted, and the optimal KNN model is outputted,
s31, selecting the hyperparameter in KNN through knowledge and experience in the field,Is a positive integer;
s32. selecting a sample from the test set, calculating its distance to all training samples, wherein the distance metric used is Min's distance
Wherein,Respectively represent twoThe dimension samples are then processed to obtain a dimensional sample,indicates that is the second of the sampleDimensional features, s is a constant;
s33, selecting the obtained result obtained by the previous step of calculationWith the nearest in distanceTraining sample corresponding to each distance;
S34, the selected training samples correspond to the classes respectivelyCounting W according to L, namely calculating the number of the various types in L appearing in WSelecting the most numerous classesThe corresponding class is used as the class of the test sample;
s35, repeating the steps from S32 to S34, and classifying all samples in the test set to obtain a corresponding class setThe result of the classificationAnd the actual resultsComparing to obtain the precision of the scheme;
8. The on-line diagnosis method for capacitor breakdown fault of capacitor voltage transformer of claim 7, wherein the step S4 is to collect the amplitude value of the CVT on-line monitoring system at the current moment, and after data preprocessing, input the amplitude value with the best hyper-parameterAnd outputting the on-line diagnosis result of the capacitor breakdown of the CVT to be detected at the current moment in the KNN model.
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CN117169669A (en) * | 2023-11-02 | 2023-12-05 | 国网江西省电力有限公司供电服务管理中心 | Breakdown capacitance monitoring method and device, electronic equipment and readable storage medium |
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