CN112114214A - Transformer fault diagnosis method - Google Patents
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Abstract
The invention discloses a transformer fault diagnosis method, which comprises the steps of collecting historical preventive test data and fault data of a transformer in a power system and preprocessing the historical preventive test data and the fault data; analyzing the correlation among the preprocessed data items by utilizing a correlation analysis strategy to obtain a characteristic quantity set; optimizing the support vector machine by combining an ant lion optimization strategy to obtain an optimized support vector machine model; and carrying out fault diagnosis on the transformer by using the support vector machine model. The method combines a large amount of preventive test data and historical fault data of the transformer, analyzes the relevance among the data, extracts fault information in the transformer pre-test data, establishes a training data set and a test data set, and performs transformer fault diagnosis by using the support vector machine model optimized by the ant lion, so that the method has the advantages of high accuracy and short training time, and has the practical application value of engineering.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a transformer fault diagnosis method.
Background
Whether the power system can safely and stably operate and avoid accidents as much as possible depends on power equipment in the system to a great extent, and once the equipment fails, the safe operation state of the power grid is possibly damaged. Therefore, the safe operation of the power equipment is the first line of defense for avoiding major accidents of the power grid, the power transformer is used as the junction equipment of the power system, the cost is high, the maintenance cost is high, whether the operation condition of the transformer is stable, whether the transformer breaks down or not and whether the transformer is in a healthy state or not directly influences whether the power grid can safely and stably operate or not.
The transformer preventive test detects and evaluates each characteristic quantity aiming at the operation condition of the transformer, brings massive pre-test data, represents the instant condition of each characteristic parameter generated in the operation process of the transformer, can reflect the current overall operation condition of the transformer, and particularly reflects various fault conditions of the transformer under the current operation condition. The fault type can be judged through preventive test data of the transformer. Most of the current common fault diagnosis methods are intelligent algorithms based on operation data, and the problems that the data application amount is insufficient and the associated data cannot be selected efficiently exist. How to efficiently apply the transformer pre-test data and adopt the improved intelligent algorithm to realize the rapid and efficient specific diagnosis of the transformer fault is also the key problem of the current research.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the transformer fault diagnosis method provided by the invention can solve the problem of insufficient application of a large amount of preventive test data of the power enterprise operation information platform.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of collecting historical preventive test data and fault data of a transformer in a power system and preprocessing the historical preventive test data and the fault data; analyzing the correlation among the preprocessed data items by utilizing a correlation analysis strategy to obtain a characteristic quantity set; optimizing the support vector machine by combining an ant lion optimization strategy to obtain an optimized support vector machine model; and carrying out fault diagnosis on the transformer by using the support vector machine model.
As a preferable aspect of the transformer fault diagnosis method of the present invention, wherein: the historical preventative test data comprises nameplate information, oil monitoring data and chromatographic monitoring data; the fault data comprises equipment numbers, fault time and fault types.
As a preferable aspect of the transformer fault diagnosis method of the present invention, wherein: preprocessing the data includes removing erroneous data and supplementing partially missing data by interpolation.
As a preferable aspect of the transformer fault diagnosis method of the present invention, wherein: respectively calculating the correlation between the data items by using a Spearman correlation coefficient and a Kendall correlation coefficient; counting, analyzing and calculating results and dividing various characteristic quantities into five types; and taking the occurrence frequency of the correlation as the standard of the characteristic quantity classification, and combining two correlation results to comprehensively analyze to obtain the characteristic quantity set for fault diagnosis.
As a preferable aspect of the transformer fault diagnosis method of the present invention, wherein: the Spearman correlation coefficients include,
wherein R isiAnd SiIs the value grade of the observed value i,
and
is the average grade of the two variables and N is the total number of observations.
As a preferable aspect of the transformer fault diagnosis method of the present invention, wherein: the Kendall correlation coefficients include,
wherein, P is the numerical logarithm with consistent arrangement relation, and n is the number of statistical vectors.
As a preferable aspect of the transformer fault diagnosis method of the present invention, wherein: screening and obtaining fault result data from each transformer pre-test data center and classifying according to the fault types; coding the fault result data, and dividing the data into a training set and a test set according to the proportion of 10: 1; initializing parameters of the ant lion optimization strategy; iteratively updating the positions of the ants and the ant lions based on the ant lions optimization strategy, calculating the fitness value of each ant and the ant lions by using a fitness function, and finally outputting the optimal (C, g) parameter value according to the end condition; and extracting the (C, g) parameter values as parameters of the support vector machine, constructing the support vector machine model by combining a support vector machine strategy and the training set, analyzing and calculating the test set by using the optimized support vector machine model, and outputting a fault diagnosis result.
As a preferable aspect of the transformer fault diagnosis method of the present invention, wherein: the support vector machine model includes a model of a support vector machine,
K(xi,xj)＝exp(-g||xi-xj||2)
wherein, omega represents hyperplane normal vector, C represents punishment factor, punishment degree of control error division sample, n represents sample number, xi represents relaxation factor, indicates allowable error division rate under linear inseparable condition, yiRepresents the sample output, and yi∈{-1,1}，xiRepresenting the sample input, b representing the threshold, and g being a gaussian radial basis function parameter.
As a preferable aspect of the transformer fault diagnosis method of the present invention, wherein: the fault types include normal operation, medium and low temperature overheating, high temperature overheating, low energy discharge and high energy discharge.
As a preferable aspect of the transformer fault diagnosis method of the present invention, wherein: the ant lion optimization strategy includes that ants seek food in a search space through random walk behavior, and ant lions capture the ants with traps, as follows,
xi＝[0,cumsum(2r(t)-1)]
wherein x isiIs the ant i wandering position coordinate, cumsum represents the calculation cumulative sum, t represents the iteration, and r (t) represents the random value.
The invention has the beneficial effects that: the method is based on an electric power company integrated data platform and an operation and maintenance data platform, the relevance between each item of data and a fault is obtained through data mining analysis, a characteristic set is obtained, an improved intelligent algorithm is adopted to diagnose the fault of the transformer, and the method has important significance for the operation and maintenance of the transformer; on the other hand, the method combines a large amount of preventive test data and historical fault data of the transformer, analyzes the relevance among the data, extracts fault information in the transformer pre-test data, establishes a training data set and a test data set, and utilizes the ant lion optimized support vector machine model to diagnose the fault of the transformer, so that the method has the advantages of high accuracy and short training time, and has the practical engineering application value.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow chart of a transformer fault diagnosis method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a ant lion optimized support vector machine model of the transformer fault diagnosis method according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 and 2, a transformer fault diagnosis method is provided for a first embodiment of the present invention, and includes:
s1: historical preventive test data and fault data of a transformer in a power system are collected and preprocessed. Wherein, it is required to be noted that:
historical preventative test data includes nameplate information, oil monitoring data, chromatographic monitoring data;
the fault data comprises equipment number, fault time and fault type;
the fault types comprise normal operation, medium and low temperature overheating, high temperature overheating, low energy discharge and high energy discharge;
the nameplate information comprises equipment type, equipment name, voltage, model and delivery date;
the oil monitoring information mainly reflects the electrical, chemical and physical properties of the insulating oil, including test date, appearance (oil), acid value, pH value, flash point (closed), breakdown voltage resistance, volume resistivity and dielectric loss factor;
the chromatographic monitoring data is mainly obtained by analyzing the concentration and the composition of gas dissolved in the insulating oil of the oil-immersed transformer and comprises test date, gas components and moisture;
the gas components include methane, ethane, ethylene, acetylene, total hydrocarbons, hydrogen, carbon monoxide, carbon dioxide.
Specifically, preprocessing the data includes:
and eliminating wrong data, and supplementing partial missing data by using an interpolation method.
S2: and analyzing the correlation among the preprocessed data items by using a correlation analysis strategy to obtain a characteristic quantity set. Referring to fig. 2, the steps to be described are:
respectively calculating the correlation between the data items by using a Spearman correlation coefficient and a Kendall correlation coefficient;
counting, analyzing and calculating results and dividing various characteristic quantities into five types;
and (3) taking the occurrence frequency of the correlation as the standard of characteristic quantity classification, and combining two correlation results to comprehensively analyze to obtain a characteristic quantity set for fault diagnosis.
Specifically, Spearman correlation coefficients include:
wherein R isiAnd SiIs the value grade of the observed value i,
and
is the average grade of the two variables, N is the total number of observations;
the Kendall correlation coefficients include,
wherein, P is the numerical logarithm with consistent arrangement relation, and n is the number of statistical vectors.
S3: and optimizing the support vector machine by combining the ant lion optimization strategy to obtain an optimized support vector machine model. It should be noted that the ant lion optimization strategy includes:
ants seek food in the search space by random walk behavior, while ant lions capture ants with traps, as follows,
xi＝[0,cumsum(2r(t)-1)]
wherein x isiIs the coordinates of the walking position of the ant i, cumsum represents the calculation accumulated sum, t represents iteration, and r (t) represents a random value;
wherein rand is a random number over a uniform interval [0,1 ];
in the foraging process of ants, the position of each ant is stored in the matrix M under different statesAntThe method comprises the following steps:
wherein A isi,jRepresenting the position of the ith ant in the j dimension, n representing the number of the ants, and d representing the dimension of the parameter;
in the ALO algorithm, the position of each ant represents an attempted solution to the corresponding problem, and the matrix M is consideredAntThe positions of all ants generated in the optimization problem, i.e. all trial solutions corresponding to the problem, may be saved, and in the optimization process, the position of each ant (i.e. the trial solution) is evaluated using a fitness function, and the fitness value is saved in a matrix MOAIn (1), the following:
wherein f () represents a fitness function;
just as the ant positions correspond to the trial solution of the problem, in the ALO algorithm, the position of each lion represents a locally optimal solution of the problem, similar to the ant positions, using the matrix MAntLionAnd MOALThe position and fitness value of the ant lion are respectively stored:
wherein M isAntLionFor preserving the position of each ant lion, MOALFor storing the adaptation value, AL, corresponding to each ant lion positioni,jRepresenting the position of the ith ant lion in the j dimension, n representing the number of ant lions, d representing the dimension of a parameter, and f () representing a fitness function;
the random movement of each ant is limited by the search boundary of the target problem, once the random movement exceeds the search boundary to form an out-of-range problem, the obtained optimal solution loses practical significance, and in order to prevent the out-of-range problem, the random walking behavior of the ants is limited by adopting a maximum and minimum normalization mode:
wherein, ai、biRespectively is the minimum value and the maximum value of the ith parameter random walk,
the evaluation formulas are respectively the minimum value and the maximum value of the ith parameter random walk in the tth iteration, and the evaluation formulas are shown as formulas (5-8) and (5-9):
wherein,
indicates the position of the selected i-th ant lion in the t-th iteration, ct、dtThe minimum value and the maximum value of all variables of the ith dimension ant are expressed;
to obtain higher accuracy in the search process, the definition is:
wherein, T represents the maximum iteration times, w is a constant defined based on the current iteration condition, and the accuracy of the search can be adjusted;
in the process of random walking of ants, the ants randomly enter traps arranged on the ant lions, when the ants enter the traps and gradually reach the bottom of the pit to be caught by the ant lions, the process is the last stage of hunting, the ant lions enter the sand pit and slide to the ant lions to catch, in order to simulate the process, when the ants are more suitable than the ant lions catching the ant lions, the ants are caught by the ant lions, and then the positions of the ant lions are updated to the positions of the ants catching the ants for the last time; here, the update of the local optimal solution is realized, and is expressed by a logic language as follows:
for each ant, selecting one ant lion by Russian roulette, and recording as REIn the optimization process, the optimal solution obtained at each step is called elite lion, which has the best value in the problem, denoted as RAIn an iterative process, each ant will surround R simultaneouslyEAnd RARandom walk is performed, and the process is described as follows:
wherein,
indicating the ant positions obtained by roulette in the t-th iteration;
representing the random position of the elite lion in the t-th iteration.
When the adaptability of ants is stronger than that of ant lion REWhen it comes, the ant lion REThe position of (A) is replaced by an ant position, namely 'predation';
after searching all the ant lions, marking the ant lion with the best current fitness as the elite ant lion RA；
If the algorithm satisfies the stop condition, the current Elite lion RAThe position is the global optimal solution, and the output is the optimal solution of the problem;
if the algorithm does not meet the stop condition, a roulette is required.
S4: and carrying out fault diagnosis on the transformer by using a support vector machine model. It should be further noted that the step includes:
screening and obtaining fault result data from each transformer pre-test data center and classifying according to fault types;
coding fault result data, and dividing the data into a training set and a test set according to the proportion of 10: 1;
initializing parameters of the ant lion optimization strategy;
iteratively updating the positions of the ants and the ant lions based on an ant lions optimization strategy, calculating the fitness value of each ant and each ant lions by using a fitness function, and finally outputting the optimal (C, g) parameter value according to the end condition;
extracting (C, g) parameter values as parameters of a support vector machine, and constructing a support vector machine model by combining a strategy of the support vector machine and a training set;
and analyzing and calculating the test set by using the optimized support vector machine model, and outputting a fault diagnosis result.
Further, the support vector machine model includes:
K(xi,xj)＝exp(-g||xi-xj||2)
wherein, omega represents hyperplane normal vector, C represents punishment factor, punishment degree of control error sample, n represents sample quantity, xi represents relaxation factor, which refers to allowance under linear inseparable conditionError rate of allowance, yiRepresents the sample output, and yi∈{-1,1}，xiRepresenting the sample input, b representing the threshold, and g being a gaussian radial basis function parameter.
Preferably, in this embodiment, it should be further explained that the conventional fault diagnosis method for the transformer uses a self-contained spss decision tree for analysis, and the original method is not changed; the existing transformer fault diagnosis method based on the intelligent integrated algorithm integrates the traditional ratio method and the machine learning algorithm, and then carries out fault classification on each algorithm weight; the method is an improved support vector machine method, optimizes the parameters of the support vector machine and then classifies the faults, and the method has a difference in model selection, namely, optimizes the parameters of the support vector machine method to improve the accuracy of fault diagnosis.
Example 2
In order to better verify and explain the technical effects adopted in the method, the method selects the traditional transformer fault diagnosis method based on the intelligent integration algorithm and the method for carrying out comparison test, compares the test results by means of scientific demonstration, and verifies the real effect of the method.
The fault diagnosis accuracy of the traditional transformer fault diagnosis method based on the intelligent integrated algorithm is low, and in order to verify that the method has higher accuracy compared with the traditional method, the traditional method and the method are adopted to respectively carry out real-time measurement and comparison on transformers of the same type and operated by the same type of equipment in the embodiment.
And (3) testing environment: (1) selecting historical preventive test data and fault data of transformers in 100 groups of power systems, and respectively performing parameter processing by using two methods;
(2) MATLB software simulation, inputting the preprocessed 5 groups of parameters for testing;
the test results were as follows:
table 1: accuracy vs. data table.
Referring to table 1, it can be seen visually that under the test condition facing the same device, the fault identification accuracy of the conventional method is much lower than that of the method of the present invention, and the real effect of the method of the present invention is verified.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A transformer fault diagnosis method is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
collecting historical preventive test data and fault data of a transformer in a power system and preprocessing the historical preventive test data and the fault data;
analyzing the correlation among the preprocessed data items by utilizing a correlation analysis strategy to obtain a characteristic quantity set;
optimizing the support vector machine by combining an ant lion optimization strategy to obtain an optimized support vector machine model;
and carrying out fault diagnosis on the transformer by using the support vector machine model.
2. The transformer fault diagnosis method according to claim 1, characterized in that: the historical preventative test data comprises nameplate information, oil monitoring data and chromatographic monitoring data;
the fault data comprises equipment numbers, fault time and fault types.
3. The transformer fault diagnosis method according to claim 2, characterized in that: preprocessing the data includes removing erroneous data and supplementing partially missing data by interpolation.
4. The transformer fault diagnosis method according to claim 3, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
respectively calculating the correlation between the data items by using a Spearman correlation coefficient and a Kendall correlation coefficient;
counting, analyzing and calculating results and dividing various characteristic quantities into five types;
and taking the occurrence frequency of the correlation as the standard of the characteristic quantity classification, and combining two correlation results to comprehensively analyze to obtain the characteristic quantity set for fault diagnosis.
5. The transformer fault diagnosis method according to claim 4, characterized in that: the Spearman correlation coefficients include,
wherein R isiAnd SiIs the value grade of the observed value i,
and
is the average grade of the two variables and N is the total number of observations.
6. The transformer fault diagnosis method according to claim 5, characterized in that: the Kendall correlation coefficients include,
wherein, P is the numerical logarithm with consistent arrangement relation, and n is the number of statistical vectors.
7. The transformer fault diagnosis method according to any one of claims 1 to 6, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
screening and obtaining fault result data from each transformer pre-test data center and classifying according to the fault types;
coding the fault result data, and dividing the data into a training set and a test set according to the proportion of 10: 1;
initializing parameters of the ant lion optimization strategy;
iteratively updating the positions of the ants and the ant lions based on the ant lions optimization strategy, calculating the fitness value of each ant and the ant lions by using a fitness function, and finally outputting the optimal (C, g) parameter value according to the end condition;
extracting the (C, g) parameter values as parameters of the support vector machine, and combining a support vector machine strategy and the training set to construct the support vector machine model;
and analyzing and calculating the test set by using the optimized support vector machine model, and outputting a fault diagnosis result.
8. The transformer fault diagnosis method according to claim 7, characterized in that: the support vector machine model includes a model of a support vector machine,
K(xi,xj)＝exp(-g||xi-xj||2)
wherein, omega represents a hyperplane normal vector, C represents a penalty factor, controls the penalty degree of the wrong divided samples, and n represents the number of the samplesQuantity xi denotes the relaxation factor, meaning the allowable error rate in the case of linear inseparability, yiRepresents the sample output, and yi∈{-1,1}，xiRepresenting the sample input, b representing the threshold, and g being a gaussian radial basis function parameter.
9. The transformer fault diagnosis method according to claim 8, characterized in that: the fault types include normal operation, medium and low temperature overheating, high temperature overheating, low energy discharge and high energy discharge.
10. The transformer fault diagnosis method according to claim 9, characterized in that: the ant lion optimization strategy comprises the steps of,
ants seek food in the search space by random walk behavior, while lion captures the ants with traps, as follows,
xi＝[0,cumsum(2r(t)-1)]
wherein x isiIs the ant i wandering position coordinate, cumsum represents the calculation cumulative sum, t represents the iteration, and r (t) represents the random value.
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