CN110110784B - Transformer fault identification method based on transformer related operation data - Google Patents
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Abstract
The invention discloses a transformer fault identification method based on transformer related operation data, which comprises the following steps of: 1) reading transformer fault information; 2) numbering the data of the transformer fault information according to the data name; 3) preprocessing the data; 4) obtaining a weight coefficient by using a principal component analysis method; 5) training data by using a weighting support vector machine method to obtain a classifier model; 6) weighting the support vector machine; 7) inputting relevant operation data and fault types of the training classifier into the classifier obtained in the step 6), continuously inputting test data for parameter adjustment and improvement, and finally inputting data required to be predicted for fault prediction; according to the transformer fault identification method, more related operation data are added, reasonable training is conducted on the classifier according to the influence weight of the data on fault identification, the parameters and the accuracy of the classifier are adjusted by inputting related training data according to requirements, and the transformer fault identification is more accurate.
Description
Technical Field
The invention belongs to the technical field of power transformer state evaluation, and particularly relates to a transformer fault identification method based on transformer related operation data.
Background
The reliability of the electrical equipment directly affects the safe operation of the power system. However, because the transformer is a closed whole integrating multiple disciplinary technologies such as mechanical, electrical, chemical and thermodynamic technologies, the reasons for influencing the fault are complicated, the fault diagnosis needs multiple data and knowledge, and the subjective underground conclusion on one or more aspects inevitably leads to misjudgment or missed judgment. The current common fault diagnosis methods are a three-ratio judgment method, an over-temperature discharge diagram judgment method, an HAE triangular diagram judgment method, a characteristic gas method and the like. Each single diagnostic method, although highly specific for certain faults, has some disadvantages. The defects are mainly reflected in that the consideration factor is single, the accuracy rate is reduced, and in addition, the set threshold value is not accurate enough due to different operation factors, and the defects are not favorable for identifying the fault of the transformer. Therefore, the transformer fault identification method which can consider various influence factors and adjust parameters according to the operation conditions is required by the operation and maintenance and fault diagnosis of the transformer at the present stage.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the transformer fault identification method based on the related operation data of the transformer is provided to solve the problems in the prior art.
The technical scheme adopted by the invention is as follows: a transformer fault identification method based on transformer related operation data comprises the following steps:
reading transformer fault diagnosis information including transformer oil test data, insulation oil data and fault data from a database server; the specific data content is as shown in table 1:
numbering the data types of the transformer oil test data, the insulation test data and the insulation oil data according to data names, and respectively setting the transformer oil test data, the insulation test data and the insulation oil data to A, B, C, wherein the data types are respectively according to A1~An、B1~Bm、C1~CkNumbering; setting the fault type of the transformer to be F, and pressing the fault type of the transformer to be F1~FtNumbering;
step three, data preprocessing is carried out on the data with the serial number A, B, C, namely missing value filling and denoising processing are carried out on the data, and then normalization processing is carried out on the data according to the type;
fourthly, carrying out weight analysis on different data types of data by using a principal component analysis method to obtain weight coefficients, and solving a characteristic root Evalaue and a characteristic vector Eectror according to the covariance matrix, wherein the value after the characteristic root normalization is the weight omega of the relevant operating data of each transformerjiI.e. the contribution rate of each feature root, is similarly applicable to a variety of data (a1, a2 … An) of different types (A, B, C);
fifthly, training data by using a weighting support vector machine method to obtain a classifier model;
step six, weighting the support vector machine in the step five;
and step seven, inputting the relevant operation data (A, B, C) of the transformer used by the training classifier and the corresponding fault type (F) into the weighting support vector machine obtained in the step six to obtain the classifier, continuously inputting test data to adjust and improve the parameters of the classifier, and finally inputting the data required to be predicted to predict the fault.
The invention has the beneficial effects that: compared with the prior art, the transformer fault identification method provided by the invention has the advantages that more related operation data are added, the classifier is reasonably trained according to the influence weight of the data on fault identification, and the related training data can be input according to requirements to adjust the parameters and accuracy of the classifier, so that the transformer fault is reasonably identified finally.
Drawings
FIG. 1 is a flow chart of a transformer fault identification method based on transformer related operational data;
FIG. 2 is a flow chart of a principal component analysis acquisition weight method;
FIG. 3 is a schematic diagram of a support vector machine;
fig. 4 is a flow chart of a classifier based on a weighted support vector machine.
Detailed Description
The invention is further described with reference to the accompanying drawings and specific embodiments.
Example (b): as shown in fig. 1 to 4, a transformer fault identification method based on transformer-related operation data includes the following steps:
reading transformer fault diagnosis information including transformer oil test data, insulation oil data and fault data from a database server; the specific data content is as shown in table 1:
TABLE 1 Transformer related data
Numbering the data types of the transformer oil test data, the insulation test data and the insulation oil data according to data names, and respectively setting the transformer oil test data, the insulation test data and the insulation oil data to A, B, C, wherein the data types are respectively according to A1~An、B1~Bm、C1~CkNumbering; setting the fault type of the transformer to be F, and pressing the fault type of the transformer to be F1~FtNumbering as shown in table 2;
TABLE 2 Transformer data numbering case
Step three, data preprocessing is carried out on the data with the serial number A, B, C, namely missing value filling and denoising processing are carried out on the data, and then normalization processing is carried out on the data according to the type;
the normalization processing process is explained by A1 data, and n pieces of data are set;
i is the number of data collected for each type of data,is A1Average value of Medium data, A'1iIs A1Carrying out normalization processing on the data;
step four, carrying out weight analysis on the data of different data types by using a principal component analysis method to obtain a weight coefficient, wherein a flow chart of a method for obtaining the weight by the principal component analysis is shown in fig. 2, and the principal component analysis method is defined as follows:
(1) arranging original data according to rows to form a matrix M;
(2) carrying out data standardization on M to enable the mean value of M to become zero;
(3) solving a covariance matrix Cov of M;
(4) obtaining a feature vector T and a feature root M of the feature vector by using Cov;
the contribution rate V of each characteristic root is calculated by the following formulai;Vi=Mi/(M1+M2+.....) the contribution rate is a weighting factor for each data type.
Where M is the original data matrix for all A, B, C sample data combinations:
M={A1,A2,…,A5,B1,B2,…,B8,C1,C2,…,C6}
as can be seen from the above, the first two steps in the step (4) are completed in the step (3), and then the covariance matrix Cov of the M matrix formed by the data is obtained as follows (assuming a is provided)1、A2、A3Three sets of data):
the diagonal of the matrix is A1、A2、A3Rather than the diagonal being covariance. Covariance is a measure of the degree of change in which two variables change simultaneously. A covariance greater than 0 means that if one of the two quantities increases, the other increases; less than 0 indicates one increase and one decrease. If the two quantities are statistically independent, then the covariance between the two is 0; but the covariance is 0 and does not indicate that the two quantities are independent. The larger the absolute value of the covariance is, the larger the influence of the two on each other is, and the smaller the influence is otherwise;
solving a characteristic root M and a characteristic vector T according to the covariance matrix, wherein the value after the characteristic root is normalized is the weight omega of the relevant operation data of each transformerjiI.e. contribution rate of each feature root, the same appliesA plurality of data (A1, A2 … An) at different types (A, B, C);
step five, training data by using a weighted support vector machine method to obtain a classifier model, wherein the support vector machine is defined as follows:
a schematic diagram of a support vector machine is shown in fig. 3, the support vector machine (support vector machines) is a binary model, and aims to find a hyperplane to segment a sample, and the segmentation principle is interval maximization, and finally is converted into a convex quadratic programming problem to solve. When the training samples are linearly separable, learning a linearly separable support vector machine through hard interval maximization; when the training samples are linearly irretrievable, a nonlinear support vector machine is learned through the maximization of kernel skills and soft intervals, and a classifier model is as follows:
in the formula: k is a radical ofiIs hyperplane normal vector, C is penalty factor, n is sample number, ξiIs a relaxation factor and represents the allowable error rate under the linear irreducible condition; y isiIs the sample output, and yi∈{-1,1};xiThe sample input quantity is; b is a threshold value;
and (3) introducing a Lagrange multiplier algorithm to solve the problem to obtain an optimized objective function:
α in the above formulai、αjIs Lagrange multiplier, xi、xjAs sample input amount, yi、yjFor sample output, the following equation is a constraint.
SVM by introducing non-linear mappingRn→ H, map the samples to a new data setCan transform the optimized objective function into
in the formula xi、xjFor the sample input, γ is the radial basis kernel function vector whose value determines the classification accuracy of the support vector machine.
Step six, weighting the support vector machine in the step five, wherein the definition of the weighted support vector machine and the k weighting steps are as follows:
the characteristic weighting is given to each characteristic in the data set according to the criterion, the characteristic weighting is called as characteristic weighting, the performance of the algorithm can be improved by applying the characteristic weighting, and a formula for expanding the standard Euclidean distance by utilizing a characteristic weight vector omega is as follows:
wherein d isω(xi,xj) Representing two samples xiAnd xjWeighted euclidean distance of, xikRepresents a sample xiOf the kth feature, ω ═ ω (ω ═ ω)1,ω2…ωn) Is a weight vector, ωk0(k ═ 1, … n) is the importance weight corresponding to each feature;
the support vector machine constructed based on the feature weighting kernel function is called a feature weighting support vector machine, and the feature weighting kernel function is defined as follows:
let K be the kernel function defined at X,p is an n-th order linear transformation matrix for a given input space, where n is the dimension of the input space, a feature weighting kernel function KpIs defined as
The linear transformation matrix P, also called the feature weighting matrix, is of the form:
omega in the above formulajiThe weight of each type of data obtained in the step (3) and the step (4) is obtained;
the weighting form of the kernel function, i.e. the radial basis function weighting form, is selected as follows:
namely, it is
Kp(xi,xj)=exp(-γ((xi-xj)TPPT(xi-xj))2);
And step seven, inputting the relevant operation data (A, B, C) of the transformer used by the training classifier and the corresponding fault type (F) into the weighting support vector machine obtained in the step six to obtain the classifier, continuously inputting test data to adjust and improve the parameters of the classifier, finally inputting the data required to be predicted to predict the fault of the classifier, and obtaining a flow chart of the classifier based on the weighting support vector machine as shown in fig. 4.
The transformer fault identification method can add more related operation data, reasonably train the classifier according to the influence weight of the data on fault identification, and input related training data according to requirements to adjust the parameters and the accuracy of the classifier, thereby finally playing a role in reasonably identifying the transformer fault.
The invention can also be combined with other functions required by users, such as the acquisition and storage functions of related data, the fault early warning function and the like.
The conditions that influence factors related to transformer faults are too many, the types of the transformer faults are not distinguished obviously and the like are considered, the weight analysis can be carried out on the related operation data of the transformer, and a support vector machine in a weighting form is adopted for classification. The method has the advantages that the weight adjustment is carried out according to actual data, the fault types are intelligently classified by a support vector machine, and the like, namely, the classification factors are not only single threshold judgment, but also related data are mapped to a higher-dimensional space for reasonable classification. The method can provide guidance for early warning and maintenance of the transformer, can reasonably allocate the operation of the transformer, moderately reduces the load rate of the early warning transformer under possible conditions, arranges the maintenance of the early warning transformer as soon as possible, and has good application prospect.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and therefore, the scope of the present invention should be determined by the scope of the claims.
Claims (1)
1. A transformer fault identification method based on transformer related operation data is characterized in that: the method comprises the following steps:
reading transformer fault diagnosis information including transformer oil test data, insulation oil data and fault data from a database server;
numbering the data types of the transformer oil test data, the insulation test data and the insulation oil data according to data names, and respectively setting the transformer oil test data, the insulation test data and the insulation oil data to A, B, C, wherein the data types are respectively according to A1~An、B1~Bm、C1~CkNumbering; setting the fault type of the transformer to be F, and pressing the fault type of the transformer to be F1~FtNumbering;
step three, missing value filling and denoising are carried out on the data with the serial number A, B, C, and then normalization processing is carried out on the data according to the types;
fourthly, carrying out weight analysis on different data types of data by using a principal component analysis method to obtain weight coefficients, and solving a characteristic root Evalaue and a characteristic vector Eectror according to the covariance matrix, wherein the value after the characteristic root normalization is the weight omega of the relevant operation data of each transformerjiI.e. the contribution rate of each feature root;
fifthly, training data by using a weighting support vector machine method to obtain a classifier model;
the classifier model is as follows:
in the formula: k is a radical ofiIs hyperplane normal vector, C is penalty factor, n is sample number, ξiIs a relaxation factor and represents the allowable error rate under the linear irreducible condition; y isiIs the sample output, and yi∈{-1,1};xiThe sample input quantity is; b is a threshold value;
introducing a Lagrange multiplier algorithm for solving to obtain an optimized objective function:
α in the above formulai、αjIs Lagrange multiplier, xi、xjAs sample input amount, yi、yjTaking the output quantity of the sample, and taking the following formula as a constraint condition;
SVM by introducing non-linear mappingRn→ H, map the samples to a new data setTransforming an optimization objective function into
K(xi,xj)=exp(-γ||xi-xj||2)
in the formula xi、xjTaking the sample input quantity as gamma, and taking the gamma as a radial basis kernel function vector;
step six, weighting the support vector machine in the step five;
wherein the steps of defining and k-weighting the weighted support vector machine are as follows:
giving a certain weight to each feature in the data set according to the criterion is called feature weighting, and the formula for expanding the standard Euclidean distance by using the feature weight vector omega is as follows:
wherein d isω(xi,xj) Representing two samples xiAnd xjWeighted euclidean distance of, xikRepresents a sample xiOf the kth feature, ω ═ ω (ω ═ ω)1,ω2…ωn) Is a weight vector, ωk0(k ═ 1, … n) is the importance weight corresponding to each feature;
the support vector machine constructed based on the feature weighting kernel function is called a feature weighting support vector machine, and the feature weighting kernel function is defined as follows:
let K be the kernel function defined at X,p is an n-th order linear transformation matrix for a given input space, where n is the dimension of the input space, a feature weighting kernel function KpIs defined as
The linear transformation matrix P, also called the feature weighting matrix, is of the form:
omega in the above formulajiThe weight of each type of data obtained in the step (3) and the step (4) is obtained;
the weighting form of the kernel function, i.e. the radial basis function weighting form, is selected as follows:
namely, it is
Kp(xi,xj)=exp(-γ((xi-xj)TPPT(xi-xj))2);
And step seven, inputting the relevant operation data of the transformer used by the training classifier and the fault type corresponding to the relevant operation data into the weighting support vector machine obtained in the step six to obtain the classifier, continuously inputting test data to adjust and improve the parameters of the classifier, and finally inputting the data required to be predicted to predict the fault.
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105629109A (en) * | 2015-12-30 | 2016-06-01 | 西安工程大学 | ARTI-neural network-based oil-immersed transformer fault diagnosis method |
CN107831300A (en) * | 2017-10-20 | 2018-03-23 | 广东电网有限责任公司河源供电局 | A kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection deteriorates appraisal procedure |
CN108680811A (en) * | 2018-06-29 | 2018-10-19 | 广东工业大学 | A kind of transformer fault state evaluating method |
CN109239516A (en) * | 2018-09-05 | 2019-01-18 | 国网山西省电力公司检修分公司 | Merge the transformer fault layering diagnostic method of a variety of intelligent diagnostics models |
CN109669087A (en) * | 2019-01-31 | 2019-04-23 | 国网河南省电力公司 | A kind of method for diagnosing fault of power transformer based on Multi-source Information Fusion |
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CN105335759B (en) * | 2015-11-12 | 2019-07-16 | 南方电网科学研究院有限责任公司 | Transformer fault detection method based on probability model generation |
CN106934421B (en) * | 2017-03-16 | 2020-11-06 | 山东大学 | Power transformer fault detection method based on 2DPCA and SVM |
CN109030790A (en) * | 2018-08-21 | 2018-12-18 | 华北电力大学(保定) | A kind of method for diagnosing fault of power transformer and device |
CN109164343B (en) * | 2018-08-30 | 2020-11-06 | 西华大学 | Transformer fault diagnosis method based on characteristic information quantization and weighted KNN |
CN109670676A (en) * | 2018-11-26 | 2019-04-23 | 安徽继远软件有限公司 | Distributing net platform region method for prewarning risk and system based on Support Vector data description |
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Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105629109A (en) * | 2015-12-30 | 2016-06-01 | 西安工程大学 | ARTI-neural network-based oil-immersed transformer fault diagnosis method |
CN107831300A (en) * | 2017-10-20 | 2018-03-23 | 广东电网有限责任公司河源供电局 | A kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection deteriorates appraisal procedure |
CN108680811A (en) * | 2018-06-29 | 2018-10-19 | 广东工业大学 | A kind of transformer fault state evaluating method |
CN109239516A (en) * | 2018-09-05 | 2019-01-18 | 国网山西省电力公司检修分公司 | Merge the transformer fault layering diagnostic method of a variety of intelligent diagnostics models |
CN109669087A (en) * | 2019-01-31 | 2019-04-23 | 国网河南省电力公司 | A kind of method for diagnosing fault of power transformer based on Multi-source Information Fusion |
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