CN111157850A - Mean value clustering-based power grid line fault identification method - Google Patents

Mean value clustering-based power grid line fault identification method Download PDF

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CN111157850A
CN111157850A CN202010044361.1A CN202010044361A CN111157850A CN 111157850 A CN111157850 A CN 111157850A CN 202010044361 A CN202010044361 A CN 202010044361A CN 111157850 A CN111157850 A CN 111157850A
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fault
line
formula
mean value
line fault
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杨帆
邓一帆
李东东
徐波
林顺富
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Shanghai Electric Power University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
    • G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
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Abstract

The invention relates to a mean value clustering-based power grid line fault identification method, which extracts four abnormal state data according to a monthly report and abnormal working state information stored by a system, wherein the abnormal state of a line comprises a single-phase line short-circuit fault, a line re-trip after reclosing, a three-phase line short-circuit fault and a regulation misinformation. And extracting kurtosis, average distance percentage and variation coefficient fault characteristic quantity by fully mining and analyzing typical false alarm waveforms of all faults. And classifying the samples by using a mean value clustering algorithm, so as to realize fault identification of the line according to the abnormal state current data. Compared with the prior art, the method has the advantages of rapid diagnosis, accurate fault identification and the like.

Description

Mean value clustering-based power grid line fault identification method
Technical Field
The invention relates to the technical field of intelligent power grid fault identification, in particular to a mean value clustering-based power grid line fault identification method.
Background
The power grid dispatching fault warning system is an important support for guaranteeing safe and stable operation and dispatching of a power grid, timely identification is made on abnormal fault accidents by monitoring various data and indexes of the power grid in real time, and dispatching and fault processing real-time basis is provided for dispatching personnel and operation and maintenance personnel.
With the development of an electric power system, higher requirements are provided for automation and intellectualization of system operation, the traditional dispatching fault alarm system excessively depends on manual processing and cannot meet the requirements for power grid development, so that an intelligent fault identification system needs to be researched.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a mean value clustering-based power grid line fault identification method.
The purpose of the invention can be realized by the following technical scheme:
a power grid line fault identification method based on mean value clustering comprises the following steps:
step 1: extracting kurtosis, average distance percentage and variation coefficient as fault characteristic quantities according to waveform characteristics of each typical state data of the line, and constructing an intelligent line fault identification characteristic database;
step 2: clustering and dividing the identification feature database by adopting a K mean algorithm;
and step 3: and visually representing the recognition characteristic database after the clustering division is finished through a TSNE dimension reduction algorithm, and comparing an actual result with a visual result of the clustering division by operation and maintenance personnel to diagnose and recognize the network line fault.
Further, the kurtosis in step1 is calculated as:
Figure BDA0002368851450000021
in the formula, QXIs kurtosis, X, of a faulty sampleiIn order to sample the data at the sample points,
Figure BDA0002368851450000027
is the average value of the fault samples, and n is the number of sampling points.
Further, the calculation formula of the average distance percentage in step1 is as follows:
Figure BDA0002368851450000022
in the formula, YXIs the average distance percentage of the failed samples.
Further, the calculation formula of the coefficient of variation in step1 is:
Figure BDA0002368851450000023
in the formula, VXIs the coefficient of variation of the fault sample.
Further, in the K-means algorithm in step2, an euclidean distance calculation formula between any two of the state information quantities for each sample point is as follows:
Figure BDA0002368851450000024
in the formula, d (i, j)2Is the Euclidean distance, D, between any two of the quantities of state information for each sample pointiAnd DjRespectively, any two state information quantities of the state information quantities of each sample point.
Further, the line fault intelligent identification feature database in the step1 describes a formula as follows:
Figure BDA0002368851450000025
in the formula, D is a line fault intelligent identification characteristic database, D1,D2,…,DnThe individual elements in the feature database are intelligently identified for the line fault.
Further, the typical state data of the line in the step1 include a mediation false alarm data, a single-phase line short circuit fault data, a line reclosing re-trip fault data and a three-phase line short circuit fault data.
Further, the formula for calculating the conditional probability in the TSNE dimension reduction algorithm in step3 is as follows:
Figure BDA0002368851450000026
in the formula, qj|iFor conditional probabilities in TSNE dimension reduction algorithms, di，dj，dk，dlAnd intelligently identifying each element in the characteristic database for the line fault after dimension reduction.
Compared with the prior art, the invention has the following advantages:
(1) the method selects the TSNE dimension reduction to perform the clustering algorithm to perform the visualization of the clustering result of the high-dimensional characteristic quantity, and the TSNE is a nonlinear dimension reduction algorithm for finding the data intrinsic relation through the probability distribution of random walk on a neighborhood map for the two-dimensional visualization research. Through visual display, the division effect of fault identification can be clearly and visually displayed, and rapid diagnosis of operation and maintenance personnel is facilitated.
(2) Each fault sample comprises line current remote measurement values within 3s before and after the fault, 3000 sampling data points are counted, firstly, the waveform characteristics of each fault sample point are fully considered, three groups of characteristic quantities of kurtosis, average distance percentage and coefficient of variation are extracted, and the data characteristics of each fault type are fully mined; then, the invention adopts a mean value clustering algorithm to classify the data, thereby realizing the automatic classification and identification of the fault types.
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FIG. 1 is a flow chart of a method of the present invention;
fig. 2 is a waveform diagram of reclosing re-trip fluctuation in a typical line abnormal state according to an embodiment of the present invention;
FIG. 3 is a waveform illustrating a three-phase fault in an abnormal line condition in accordance with an exemplary embodiment of the present invention;
FIG. 4 is a waveform diagram illustrating a fault-report during a typical line anomaly condition in accordance with an embodiment of the present invention;
FIG. 5 is a waveform illustrating a single phase fault trip coincidence in an exemplary line anomaly condition in accordance with an embodiment of the present invention;
FIG. 6 is a TSNE dimension reduction visualization diagram of the clustering result of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
The method of the invention has a flow chart as shown in fig. 1, and specifically comprises the following process steps:
1. constructing intelligent line fault recognition feature database
Four typical types of line anomaly conditions are illustrated in fig. 2, 3, 4 and 5, where telemetry monitored current amplitudes are analyzed. The amplitude, the period and the frequency of the current fluctuation degree under the same fault are similar, and the fluctuation difference under different faults is larger, so that fault characteristic quantities such as kurtosis, average distance percentage, variation coefficient and the like are extracted according to the waveform characteristics of each typical state.
Wherein, the kurtosis calculation formula is as follows:
Figure BDA0002368851450000041
in the formula, QXIs kurtosis, X, of a faulty sampleiIn order to sample the data at the sample points,
Figure BDA0002368851450000042
is the average value of the fault samples, and n is the number of sampling points.
Wherein, the average distance percentage calculation formula is as follows:
Figure BDA0002368851450000043
in the formula, YXIs the average distance percentage of the failed samples.
Wherein, the coefficient of variation formula is as follows:
Figure BDA0002368851450000044
in the formula, VXIs the coefficient of variation of the fault sample.
By the above feature quantity construction method, an intelligent alarm technical feature database is constructed:
Figure BDA0002368851450000045
in the formula, D is a line fault intelligent identification characteristic database, D1,D2,…,DnThe individual elements in the feature database are intelligently identified for the line fault.
2. Intelligent line fault identification technology
2.1, clustering and dividing the current fault multi-dimensional sample database by adopting a K mean algorithm
The mean clustering algorithm is a clustering algorithm based on Euclidean distance partitioning. The clustering algorithm classifies the same characteristics of the established fault current multi-dimensional samples into one class, divides the fault current multi-dimensional samples into different classes according to different characteristics, realizes the intelligent fault identification technology, and obtains the state information quantity D of each sample point1、D2……DkI.e. the amount of status information D of any two fault samplesiAnd DjThe Euclidean distance formula is as follows:
Figure BDA0002368851450000046
in the formula, d (i, j)2Is the Euclidean distance, D, between any two of the quantities of state information for each sample pointiAnd DjRespectively, any two state information quantities of the state information quantities of each sample point.
The K-means clustering algorithm comprises the following calculation steps:
(1) and randomly setting K-4 sample points from a fault sample database as an initial clustering center.
(2) And finding out a cluster suitable for each sample by calculating the Euclidean distance from the randomly set central point.
(3) And calculating the optimal clustering center of the cluster through the divided clusters.
(4) Repeating the steps 2 and 3 until the central point of the cluster is unchanged or reaches a set iteration number or reaches a set error range.
2.2 visualization of Fault identification
And the TSNE is selected for dimensionality reduction, the clustering algorithm is used for visualizing the clustering result of the high-dimensional characteristic quantity, and the TSNE is a nonlinear dimensionality reduction algorithm for finding the data intrinsic relation through the probability distribution of random walk on a neighborhood graph for two-dimensional visualization research. Through visual display, the division effect of fault identification can be clearly and visually displayed, the rapid diagnosis of operation and maintenance personnel is facilitated, and the specific algorithm is as follows:
(1) random adjacency embedding (SNE) begins by converting the high-dimensional euclidean distances between data points to conditional probabilities representing similarity, Di、DjConditional probability p between (arbitrary two feature quantities)j|iGiven by:
Figure BDA0002368851450000051
wherein sigmaiIs the data point xiA gaussian variance at the center.
(2) For d of lower dimensioniAnd djData, q can be calculated as wellj|iTo achieve dimension reduction. Set its variance
Figure BDA0002368851450000055
Figure BDA0002368851450000054
With a conditional probability of qj|i：
Figure BDA0002368851450000052
(3) T-SNE adopts T distribution, so as toThe problem of light data congestion of a low-dimensional space is solved. Thus p isj|iThe formula after adopting T distribution is as follows:
Figure BDA0002368851450000053
in the formula, qj|iFor conditional probabilities in TSNE dimension reduction algorithms, di，dj，dk，dlAnd intelligently identifying each element in the characteristic database for the line fault after dimension reduction.
The practical implementation scheme is as follows:
the invention selects 44 groups of sample data, each sample contains fault information within 3s before and after the fault, and 3000 sampling data points are provided. The fault detection method comprises the following steps of respectively obtaining 21 groups of debugging and false alarm data, 10 groups of single-phase line short-circuit fault data, 6 groups of line reclosing and then tripping fault data and 7 groups of three-phase line short-circuit fault data.
Step 1: constructing a line fault intelligent identification feature database:
step 2: clustering and dividing a current fault multi-dimensional sample database by adopting a K mean algorithm;
step 3: the visual result of current fault data clustering by the TSNE dimension reduction algorithm is shown in FIG. 6
As shown in the figure, the scheme can rapidly and clearly identify each current fault state, the clustering division result of the scheme is consistent with the actual result, and operation and maintenance personnel can rapidly diagnose and identify the current fault.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (8)

1. A mean value clustering-based power grid line fault identification method is characterized by comprising the following steps:
step 1: extracting kurtosis, average distance percentage and variation coefficient as fault characteristic quantities according to waveform characteristics of each typical state data of the line, and constructing an intelligent line fault identification characteristic database;
step 2: clustering and dividing the identification feature database by adopting a K mean algorithm;
and step 3: and visually representing the recognition characteristic database after the clustering division is finished through a TSNE dimension reduction algorithm, and comparing an actual result with a visual result of the clustering division by operation and maintenance personnel to diagnose and recognize the network line fault.
2. The method for identifying the grid line fault based on the mean value clustering as claimed in claim 1, wherein the kurtosis in the step1 is calculated by the following formula:
Figure FDA0002368851440000011
in the formula, QXIs kurtosis, X, of a faulty sampleiIn order to sample the data at the sample points,
Figure FDA0002368851440000012
is the average value of the fault samples, and n is the number of sampling points.
3. The method for identifying the grid line fault based on the mean value clustering as claimed in claim 1, wherein the calculation formula of the average distance percentage in the step1 is as follows:
Figure FDA0002368851440000013
in the formula, YXIs the average distance percentage of the failed samples.
4. The method for identifying the grid line fault based on the mean value clustering as claimed in claim 1, wherein the coefficient of variation in the step1 is calculated by the following formula:
Figure FDA0002368851440000014
in the formula, VXIs the coefficient of variation of the fault sample.
5. The method for identifying the power grid line fault based on the mean value clustering as claimed in claim 1, wherein the Euclidean distance calculation formula between any two of the state information quantities for each sample point in the K-means algorithm in the step2 is as follows:
Figure FDA0002368851440000021
in the formula, d (i, j)2Is the Euclidean distance, D, between any two of the quantities of state information for each sample pointiAnd DjRespectively, any two state information quantities of the state information quantities of each sample point.
6. The method for identifying the line fault of the power grid based on the mean value clustering as claimed in claim 1, wherein the line fault intelligent identification feature database in the step1 is described by the formula:
Figure FDA0002368851440000022
in the formula, D is a line fault intelligent identification characteristic database, D1,D2,…,DnThe individual elements in the feature database are intelligently identified for the line fault.
7. The method for identifying the line fault of the power grid based on the mean value clustering as claimed in claim 1, wherein the typical state data of the line in the step1 comprises debugging false alarm data, single-phase line short-circuit fault data, line reclosing re-trip fault data and three-phase line short-circuit fault data.
8. The method for identifying the grid line fault based on the mean value clustering as claimed in claim 1, wherein the conditional probability in the TSNE dimension reduction algorithm in the step3 is calculated by the following formula:
Figure FDA0002368851440000023
in the formula, qj|iFor conditional probabilities in TSNE dimension reduction algorithms, di，dj，dk，dlAnd intelligently identifying each element in the characteristic database for the line fault after dimension reduction.
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