CN111610407B - Cable aging state evaluation method and device based on naive Bayes - Google Patents
Cable aging state evaluation method and device based on naive Bayes Download PDFInfo
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Abstract
The invention discloses a cable aging state evaluation method and device based on naive Bayes, wherein the method comprises the following steps: collecting relevant parameters of a cable to be evaluated; inputting relevant parameters of the cable into a pre-constructed naive Bayes classifier, and evaluating the aging state of the cable; the pre-constructed naive Bayes classifier is constructed by the following method: acquiring relevant parameters and cable aging states of the existing cable as training data; and calculating prior probability of each attribute variable based on a training data set by taking the relevant parameters of the cable in the training data as the attribute variables of the naive Bayes classification model and the aging state of the cable as class variables, and establishing a naive Bayes classifier. The method can accurately evaluate the aging state of the cable at lower calculation cost based on a small amount of samples, and provides targeted guidance for the operation and maintenance of the cable.
Description
Technical Field
The invention belongs to the technical field of power equipment maintenance, and particularly relates to a cable aging state evaluation method and device.
Background
Cables are very important electrical devices that fail to cause a power outage to the consumer. If the aging state of the cable can be evaluated before the cable breaks down, the cable can be overhauled and replaced in time, the fault loss can be reduced, and the power supply reliability is improved.
The cable aging state evaluation method mainly comprises two categories: 1) The slicing method comprises the steps of manufacturing cable sample slices, and detecting the cable sample slices, such as physical and chemical property detection: the cable aging degree is judged according to physicochemical detection results, such as a Fourier transform infrared spectrometer, differential calorimetric scanning analysis, X-ray diffraction analysis, scanning electron microscope analysis and the like. For example, in a method for judging the aging state of the insulating water tree of the XLPE cable disclosed in the publication No. CN 102778638A, the aging degree of the insulating water tree of the cable is comprehensively evaluated and diagnosed by carrying out the physicochemical test and then according to data such as dielectric loss peak, low-frequency conductance, thickness change of lamella, radical disappearance and the like; after the cable sample is sliced, the test results are visual, but the requirements of actual cable operation and maintenance are difficult to meet; 2) And the mathematical method is used for establishing a mathematical model by analyzing all influence factors of the cable aging and evaluating the cable aging state. For example, in a cable state evaluation method based on a probabilistic neural network algorithm disclosed in publication No. CN 106251059A, a multi-source information database is constructed according to multiple categories of information sources such as basic information, operation inspection information, family defects and the like of cable equipment; and preprocessing the database under the incomplete information by using a factor analysis method, providing a multi-source information fusion algorithm based on a probabilistic neural network, and comprehensively evaluating the overall state of the cable under the incomplete information condition. For example, a power cable state evaluation method based on the momentum BP neural network disclosed in publication No. CN 109272190A establishes a momentum BP neural network into which momentum factors are introduced, and evaluates a power cable running state. However, the neural network needs a large amount of data to train, and practical operation experience shows that the relevant sample data of cable aging is less. How to establish an accurate evaluation model by analyzing less sample data is a problem worthy of study.
Disclosure of Invention
The invention aims to: aiming at the defects of the prior art, the invention provides a cable aging state evaluation method and device based on naive Bayes, which can evaluate the cable aging state according to cable aging influence factors and provide guidance for operation and maintenance of cables.
The technical scheme is as follows: in order to realize the purpose, the invention adopts the following technical scheme:
in a first aspect, a cable aging state evaluation method based on naive Bayes is provided, which comprises the following steps:
collecting relevant parameters of a cable to be evaluated;
inputting relevant parameters of the cable to be evaluated into a pre-constructed naive Bayes classifier, and evaluating the aging state of the cable; the pre-constructed naive Bayes classifier is constructed by the following method:
acquiring relevant parameters and cable aging states of the existing cable as training data;
and (3) taking the relevant parameters of the cable in the training data as the attribute variables of the naive Bayes classification model, taking the cable aging state as the class variables, calculating the prior probability of each attribute variable, and establishing a naive Bayes classifier.
Wherein, the related parameters of the cable comprise: cable running time, whether the cable has an earth fault, cable partial discharge and cable dielectric loss.
Further, when calculating the prior probability of each attribute variable, calculating the prior probability by using frequency of laplace correction for the discrete variable, and calculating the prior probability by using gaussian distribution for the continuous variable.
The frequency of laplace correction is adopted for the discrete variable to calculate the prior probability, and the specific formula is as follows:
wherein the variable X is discrete t A total of m values, i.e. { x } t1 ,x t2 ,…,x tm P (c) is the probability value of the cable status category c, P (x) ti C) is a discrete variable X when the cable status category is c t Has a value of x ti I is more than or equal to 1 and less than or equal to m, D c Indicates the number of cable status categories c, D indicates the total number,discrete variable X representing cable state as class c t Has a value of x ti N denotes the number of possible cable state classes, N t Represents variable X t The number of possible categories.
The calculating the prior probability by adopting the Gaussian distribution on the continuous variable comprises the following steps:
for continuous variable X j Calculating a continuous variable X when the class is c by using Gaussian distribution j Has a value of x jk The specific formula of the probability value of (2) is as follows:
wherein P (x) jk | c) is when the class is c, the continuous variable X j Value of (A)Is x jk The probability value of (d) is the average value, δ is the standard deviation, both are obtained by the variables in the training data, and the calculation formula is as follows:
wherein x is j1 ,x j2 ,…,x jn Representing continuous variables X in training data j Of (i) i.e. X j ={x j1 ,x j2 ,…,x jn }。
The expression of the naive Bayes classifier is as follows:
wherein c represents the cable aging state category, y is the value set of c, argmax is a function, if there is a result x 0 = argmax (f (x)), this means that when the function f (x) takes x = x 0 Then, the maximum value of the value range of f (x) is obtained, and the argmax function returns x which maximizes the value of f (x), so that h nb (x) Is shown to makeThe category c with the largest value.
Preferably, the method further comprises: and preprocessing the cable data, wherein the preprocessing comprises deleting missing data and abnormal data, the abnormal data are detected by using description statistics in the SPSS, and the data with the Z score larger than 3 or smaller than-3 are regarded as the abnormal data.
In a second aspect, a naive bayes-based cable aging state evaluation apparatus is provided, which includes:
the system comprises a parameter acquisition module, a parameter evaluation module and a parameter analysis module, wherein the parameter acquisition module is used for collecting relevant parameters of a cable to be evaluated and acquiring relevant parameters and a cable aging state of an existing cable, and the relevant parameters and the cable aging state of the existing cable form training data;
the model building module is used for building a cable aging state evaluation model based on a naive Bayesian classification model according to relevant parameters and an aging state of a cable in training data, wherein the cable aging state evaluation model takes the relevant parameters of the cable in the training data as attribute variables of the naive Bayesian classification model, takes the cable aging state as class variables, and calculates the prior probability of each attribute variable;
and the state evaluation module is used for evaluating the aging state of the cable according to the relevant parameters of the cable to be evaluated and the cable aging state evaluation model.
Wherein the relevant parameters of the cable include: cable running time, whether the cable has a ground fault, cable partial discharge and cable dielectric loss.
Further, when the model building module calculates the prior probability of each attribute variable, the prior probability is calculated by using the frequency of laplacian correction on the discrete variable, and the prior probability is calculated by using gaussian distribution on the continuous variable.
Has the advantages that: the invention evaluates the aging state of the cable by analyzing possible influence factors of the cable aging fault by using a naive Bayes classification method. Naive Bayes is a typical sample machine learning method, a posterior probability is deduced through a prior probability, evaluation and judgment are made according to the posterior probability, compared with the prior art, the method does not need to slice a cable sample, the required sample size is small, the method is quick and simple, and the aging state of the cable is evaluated in a grading way by analyzing the cable detection state quantity, the operation time, whether the cable has a ground fault or not, and the like, so that more targeted guidance can be provided for the operation and maintenance of the cable.
Drawings
Fig. 1 is a general flowchart of a cable aging state evaluation method based on naive bayes according to the present invention.
Detailed Description
The technical scheme of the invention is further clarified by the following specific embodiments in combination with the attached drawings.
Referring to fig. 1, the cable aging state evaluation method based on naive bayes of the invention comprises the following steps:
and step A, collecting cable related data. The method comprises the following steps of cable running time, whether the cable has an earth fault or not, partial discharge of the cable, dielectric loss of the cable, aging state of the cable and the like.
As shown in Table 1, which is an example of data obtained in one embodiment, the total number of data collected is 50, 33 of which are slightly aged, 8 of which are moderately aged, and 9 of which are severely aged, and there are 4 variables in the table that may be related to cable aging, X respectively 1 ,X 2 ,X 3 ,X 4 . X for cable aging degree 5 And (4) showing.
TABLE 1 Cable related data
And B, preprocessing the data based on the cable related data acquired in the step A, mainly deleting missing data and abnormal data, wherein the abnormal data is detected by description statistics in SPSS, and regarding the data with the Z score larger than 3 or smaller than-3 as abnormal data.
Step C: and B, establishing a naive Bayes classifier based on the data preprocessed in the step B.
From the data shown in table 1, it can be seen that there are 4 variables in total, wherein the operation time, the partial discharge and the dielectric loss are continuous variables, and whether the ground fault occurs is a discrete variable.
Firstly, calculating the probability of cable aging state categories, wherein the number of cable aging states is 3, and the possible values of c are 1,2 and 3. According to the frequency of Laplace correction, calculating the prior probability of the cable aging state to obtain:
for discrete variables, the prior probability is calculated by using frequency of Laplace correction, and for variable X 2 (whether or not a ground fault occurs), the variable X is calculated based on the frequency of the Laplace correction 2 The prior conditional probability of (a) can be given as:
for the continuous type variables, the gaussian distribution parameters for each variable were found as shown in the following table:
TABLE 2 Gaussian distribution parameters of continuous variables
The conditional probability of each variable value when the cable states are in different categories (such as c =1, c =2 or c = 3) can be obtained through the Gaussian distribution function. For example for variable X 1 When the cable status category is c =1, the variable X is determined 1 Has a value of x 1k The conditional probability at this time:
for other cases of variables, a similar method can be used to determine the conditional probability.
Based on the cable aging state class probability and the conditional probability of each variable value, a naive Bayes classifier can be constructed, and the expression is as follows:
step D: inputting a cable state parameter (for example, the related parameter data of a certain cable is X) according to the naive Bayes classifier established in the step C 1 =235,X 2 =1,X 3 =14,X 4 = 0.08), according to a bayesian classifier, each when the cable state is c =1, c =2 and c =3, respectively, is calculatedThe results are as follows:
when c =1, the signal is transmitted,
when the ratio of c =2,
when c =3, the signal is transmitted,
comparing the above calculation results, it is understood that the maximum probability value is 151.6 (which is a relative probability) when c =3, and therefore, it is possible to judge that the state of the cable is c =3, that is, a severely aged state.
According to another embodiment of the present invention, there is provided a naive bayes-based cable aging state evaluation apparatus, including:
the parameter acquisition module is used for collecting related parameters of the cable; the cable-related parameters include: cable running time, whether the cable has an earth fault, cable partial discharge and cable dielectric loss.
And the model building module is used for building a cable aging state evaluation model based on the naive Bayesian classification model according to the relevant parameters and the aging state of the cable in the training data, wherein the cable aging state evaluation model takes the relevant parameters of the cable as the attribute variables of the naive Bayesian classification model, takes the cable aging state as the class variables, calculates the prior probability of each attribute variable, calculates the prior probability by adopting frequency of Laplace correction for the discrete variables, and calculates the prior probability by adopting Gaussian distribution for the continuous variables. Specifically, the calculating the prior probability by using the frequency of the laplace correction on the discrete variable includes:
setting discrete variable X t A total of m values, i.e. { x } t1 ,x t2 ,…,x tm Calculating class probability and discrete variable X by using frequency corrected by Laplace t Value of x ti The specific formula of the prior probability is as follows:
wherein P (c) is a class probability value, P (x) ti | c) is a discrete variable X when the cable status category is c t Has a value of x ti I is more than or equal to 1 and less than or equal to m, D c Indicates the number of cable status categories c, D indicates the total number,discrete variable X representing cable state as class c t Has a value of x ti N denotes the number of possible cable state classes, N t Represents the variable X t The number of possible categories.
Calculating the prior probability using a gaussian distribution for the continuous variables includes:
for continuous variable X j Of variable X j Obeying Gaussian distribution, calculating the continuous variable X when the category is c by adopting the Gaussian distribution j Has a value of x jk The specific formula of the probability value of (2) is as follows:
wherein P (x) jk C) is a continuous variable X when the class is c j Has a value of x jk The probability value of (a) is the mean value, delta is the standard deviation, both are obtained by the variable in the training data, assuming the continuous variable X in the training data j ={x j1 ,x j2 ,…,x jn And then:
the cable aging state evaluation model expression based on the naive Bayes classification model is as follows:
wherein c represents the cable aging state category, y is the value set of c, argmax is a function, if there is a result x 0 = argmax (f (x)), this means that when the function f (x) takes x = x 0 Then, the maximum value of the value range of f (x) is obtained, and the argmax function returns x which maximizes the value of f (x), so that h nb (x) Is shown to makeThe category c with the largest value.
And the state evaluation module is used for evaluating the aging state of the cable according to the relevant parameters of the cable to be evaluated and the cable aging state evaluation model. Specifically, a cable state parameter X to be evaluated is input into a cable aging state evaluation model, and each category c of the cable aging states is calculated when the cable aging states are respectively differentAnd taking the category c corresponding to the maximum probability value as the finally estimated cable aging state.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (3)
1. A cable aging state evaluation method based on naive Bayes is characterized by comprising the following steps:
collecting relevant parameters of a cable to be evaluated, wherein the relevant parameters of the cable comprise: cable running time, whether the cable has an earth fault or not, cable partial discharge and cable dielectric loss;
inputting relevant parameters of the cable to be evaluated into a pre-constructed naive Bayes classifier, and evaluating the aging state of the cable; the pre-constructed naive Bayes classifier is constructed by the following method:
acquiring relevant parameters and cable aging states of the existing cable as training data;
the method comprises the steps of taking relevant parameters of cables in training data as attribute variables of a naive Bayes classification model, taking a cable aging state as a class variable, calculating prior probability of each attribute variable based on a training data set, and establishing a naive Bayes classifier, wherein when the prior probability of each attribute variable is calculated, the prior probability is calculated by adopting frequency of Laplace correction on discrete variables, and the prior probability is calculated by adopting Gaussian distribution on continuous variables; the expression of the naive Bayes classifier is as follows:
wherein c represents the cable aging state category, y is the value set of c, argmax is a function, if there is a result x 0 = argmax (f (x)), when the function f (x) is x = x 0 Then, the maximum value of the value range of f (x) is obtained, and the argmax function returns x which maximizes the value of f (x), so that h nb (x) Is shown to makeThe category c with the largest value;
the prior probability is calculated by using the frequency of the laplace correction on the discrete variable, and the specific formula is as follows:
wherein, the discrete variable X t A total of m values, denoted as { x } t1 ,x t2 ,…,x tm P (c) is the probability value of the cable status category c, P (x) ti I c) is a discrete variable X when the cable status category is c t Has a value of x ti I is more than or equal to 1 and less than or equal to m, D c Indicates the number of cable status categories c, D indicates the total number,discrete variable X representing cable state as class c t Has a value of x ti N denotes the number of possible cable state classes, N t Represents the variable X t The number of possible categories;
the calculating the prior probability by adopting the Gaussian distribution on the continuous variable comprises the following steps:
for continuous variable X j Calculating the continuous variable X when the class is c by using Gaussian distribution j Has a value of x jk The specific formula of the probability value of (2) is as follows:
wherein P (x) jk C) is a continuous variable X when the cable status category is c j Has a value of x jk The probability value of (a) is the average value, δ is the standard deviation, both are obtained by the variables in the training data, and the calculation formula is as follows:
wherein x j1 ,x j2 ,…,x jn Representing continuous variables X in training data j Of (1).
2. The naive bayes-based cable aging state evaluation method of claim 1, further comprising: and preprocessing the cable data, wherein the preprocessing comprises deleting missing data and abnormal data, the abnormal data are detected by using description statistics in the SPSS, and the data with the Z score larger than 3 or smaller than-3 are regarded as the abnormal data.
3. A cable aging state evaluation device based on naive Bayes, comprising:
the parameter acquisition module is used for collecting relevant parameters of a cable to be evaluated, and acquiring relevant parameters and a cable aging state of an existing cable, wherein the relevant parameters and the cable aging state of the existing cable form training data, and the relevant parameters of the cable comprise: cable running time, whether the cable has a ground fault, cable partial discharge and cable dielectric loss;
the model building module is used for building a cable aging state evaluation model based on a naive Bayesian classification model according to relevant parameters of cables in training data and cable aging states, wherein the cable aging state evaluation model takes the relevant parameters of the cables in the training data as attribute variables of the naive Bayesian classification model, the cable aging states as class variables, and the prior probability of each attribute variable is calculated, when the model building module calculates the prior probability of each attribute variable, the prior probability is calculated by adopting the frequency of Laplace correction on discrete variables, the prior probability is calculated by adopting Gaussian distribution on continuous variables, and the expression of the naive Bayes classifier is as follows:
wherein c represents the cable aging state category, y is the value set of c, argmax is a function, if there is a result x 0 = argmax (f (x)), this means that when the function f (x) takes x = x 0 Then, the maximum value of the value range of f (x) is obtained, and the argmax function returns x which maximizes the value of f (x), so that h nb (x) Is shown to makeThe category c with the largest value;
the state evaluation module is used for evaluating the aging state of the cable according to the related parameters of the cable to be evaluated and the cable aging state evaluation model;
the prior probability is calculated by using the frequency of the laplace correction on the discrete variable, and the specific formula is as follows:
wherein, the discrete variable X t A total of m values, denoted as { x } t1 ,x t2 ,…,x tm P (c) is the probability value of the cable status category c, P (x) ti I c) is a discrete variable X when the cable status category is c t Has a value of x ti I is not less than 1 and not more than m, D c Indicates the number of cable status categories c, D indicates the total number,discrete variable X representing cable state as class c t Has a value of x ti N denotes the number of possible cable state classes, N t Represents variable X t The number of possible categories;
the calculating the prior probability by adopting the Gaussian distribution on the continuous variable comprises the following steps:
for continuous variable X j Calculating the continuous variable X when the class is c by using Gaussian distribution j Has a value of x jk The specific formula of the probability value of (2) is as follows:
wherein P (x) jk I c) is a continuous variable X when the cable status category is c j Has a value of x jk The probability value of (a) is the average value, δ is the standard deviation, both are obtained by the variables in the training data, and the calculation formula is as follows:
wherein x is j1 ,x j2 ,…,x jn Representing continuous variables X in training data j Of (1).
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