CN116184265A - Lightning arrester leakage current detection method and system based on multi-classification SVM - Google Patents
Lightning arrester leakage current detection method and system based on multi-classification SVM Download PDFInfo
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Abstract
The invention provides a lightning arrester leakage current detection method and system based on SVM, and belongs to the technical field of metal oxide lightning arrester live detection. The metal oxide lightning arrester has the beneficial effects of effectively identifying the operation working conditions of the metal oxide lightning arrester, and the specific scheme is as follows: the zinc oxide arrester leakage current detection method based on the multi-classification SVM comprises the following steps: the system comprises a signal acquisition unit, a characteristic processing unit, a model building and analyzing unit and a display and alarm unit. Acquiring original leakage current signal data based on an electromagnetic current transformer; extracting key characteristic quantities influencing the running state of the arrester through principal component analysis and attribute reduction, and constructing a zinc oxide arrester running condition characteristic sample data set; and marking the characteristic samples through a multi-classification SVM algorithm to form test samples, and evaluating indexes such as leakage current amplitude, effective value, harmonic distortion rate and the like of the lightning arrester through an SVM algorithm model so as to identify the defect degree of the insulation state of the lightning arrester.
Description
Technical Field
The invention relates to a lightning arrester leakage current detection method and system based on a multi-classification SVM, and belongs to the technical field of metal oxide lightning arrester live detection.
Background
The metal oxide lightning arrester is widely applied to transmission and distribution networks. The problems of aging, damp and the like of the lightning arrester are outstanding under the influence of severe environments such as high temperature difference, high humidity and the like and the operation life, and the general survey of the health condition of the metal oxide lightning arrester is required in the annual spring overhaul of power grid companies. The obvious increase of the leakage current of the metal oxide arrester is an obvious sign of the deterioration of the arrester, the change condition of the leakage current of the arrester is monitored in real time, and the real-time monitoring of the running state of the arrester and the early effective early warning of faults are realized. At present, the transformer substation evaluates the quality of the lightning arrester by adopting a traditional access type current measuring means, maintenance personnel need to wait nearby a line, and the mode of taking reference voltage from the secondary side not only has the hidden trouble of short circuit, but also has the conditions of poor contact of temporary wiring and unstable long-distance wireless signals, and the efficiency is very low. Therefore, a new live test method of the lightning arrester without field detection needs to be researched, so that the purpose of judging the insulation state of the lightning arrester can be achieved.
Disclosure of Invention
The invention aims to provide a lightning arrester leakage current detection method and system based on a multi-classification SVM, which take a non-contact current transformer as a means to determine the leakage current condition in the operation process of the lightning arrester so as to solve the problem of lightning arrester data sampling.
The invention aims to achieve the aim, and the aim is achieved by the following technical scheme:
a lightning arrester leakage current detection method and system based on SVM comprises the following steps:
step 1: acquiring leakage current data through a non-contact electromagnetic current sensor, and constructing a leakage current original data set;
step 2: preprocessing the collected original data set to form a sample set, extracting the data characteristics of equipment measuring points, calculating the data of each subharmonic of leakage current, and processing the data into three-dimensional characteristic vectors to form an equipment characteristic data set;
step 3: constructing a lightning arrester running condition test sample set based on the characteristic data set, training and testing by adopting a branch vector machine algorithm to form a leakage current detection support vector machine algorithm model;
step 4: and identifying the operation condition of the lightning arrester through a leakage current detection support vector machine algorithm model, classifying leakage current signals, outputting the result, and alarming if the leakage current signals are abnormal.
Preferably, the specific way of preprocessing the raw data to construct a sample set is as follows: processing the original data by a principal component analysis and attribute reduction method to obtain feature data, and respectively carrying out normalization processing on the feature data to obtain a minimum feature data set; and constructing a lightning arrester leakage current training sample set by taking the minimum characteristic data set as a leakage current test data set.
Preferably, the lightning arrester leakage current training sample set is divided into a training set, a testing set and a verification set; training the training set by using a support vector machine algorithm through the training set and the verification set to obtain a classification algorithm model for detecting leakage current of the lightning arrester of the support vector machine; and determining the accuracy of the support vector machine algorithm model to the detection of the leakage current of the lightning arrester through the test set.
Preferably, the specific steps for identifying the operation condition of the lightning arrester are as follows: comparing the output result of the leakage current detection support vector machine algorithm model with the original MOA rated parameter; and classifying leakage current signals according to comparison results, wherein the leakage current signals comprise normal operation, general faults, serious faults and critical faults.
Preferably, the leakage current data includes a harmonic distortion rate of a current and a harmonic of a resistive current in the leakage current; the kth current harmonic distortion rate
The calculation formula is as follows:
wherein:
the kth harmonic representing the resistive current in the leakage current,/th harmonic>
The nth harmonic wave of the resistive current in the leakage current is represented, and the value range of n is 1-k;
the harmonic calculation formula of the resistive current in the kth leakage current is as follows:
wherein:
nonlinear resistor representing leakage current flow, +.>
Indicating the angular frequency of the system>
Time is indicated.
Preferably, the three-dimensional fault feature vector of the support vector machine discriminant model includes a harmonic of the resistive current in the first leakage current, a harmonic of the resistive current in the third leakage current, and a harmonic of the resistive current in the fifth leakage current.
A lightning arrester leakage current detection system based on a multi-classification support vector machine comprises a current measurement unit, a characteristic processing unit, a model building and analyzing unit and a display and alarm unit;
the current signal acquisition unit is used for measuring the resistive leakage current of the lightning arrester and judging the state of the valve plate by monitoring the amplitude change of the resistive leakage current;
the characteristic processing unit is used for preprocessing an original data set and carrying out numerical processing and normalization processing; then, feature selection is carried out, in the feature selection, feature extraction is carried out by utilizing a principal component analysis method, feature dimension reduction is carried out on an original data set through attribute reduction, and the feature data set after dimension reduction is used as a training data set for subsequent use;
the model building and analyzing unit is used for building a support vector machine algorithm model, training and testing the support vector machine algorithm model, and identifying the support vector machine algorithm model after training and testing;
the display module in the display and alarm unit is communicated with the model building and analyzing unit and the alarm unit, the judging result of the characteristic data set is displayed, the characteristic data in the characteristic processing unit is displayed, the leakage current signal is compared with the leakage current alarm value, and if the leakage current signal is larger than the leakage current alarm value, the alarm unit is controlled to send out an audible and visual alarm signal.
The invention has the advantages that: (1) According to the lightning arrester leakage current detection method based on the multi-classification SVM, non-contact type leakage current measurement of the metal oxide lightning arrester is achieved through an electromagnetic current transformer. The lightning arrester leakage current under the operation voltage is measured in a non-contact mode, collected data are transmitted to the central processing unit through communication, and maintenance personnel can safely read the real-time data of the lightning arrester at a place far away from a line to be tested, so that time and labor are saved.
(2) Through the lightning arrester leakage current detection system based on the multi-classification SVM, SVM machine learning algorithm is adopted, fault training data set data are classified according to technical standards, training set is trained by support vector machine algorithm, and the effect of the support vector machine algorithm model on detecting leakage current is determined by using test set, so that the quality of the lightning arrester is detected.
(3) The method and the system can monitor the actual running state of the zinc oxide arrester of the transformer substation in real time, and a user can timely acquire data and predict faults through the cloud server, so that the faults are further removed, and the running safety and efficiency of the power grid are improved. The lightning arrester with fault hidden danger is convenient for an maintainer to replace in time, the power failure times are reduced, and the safety production of the power system and the personal safety of the operators in the operation and inspection of the power grid company are ensured.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Fig. 1 is a system flow diagram of the present invention.
Fig. 2 is a schematic diagram of the system architecture of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A lightning arrester leakage current detection method and system based on SVM comprises the following steps:
step 1: acquiring leakage current data through a non-contact electromagnetic current sensor, and constructing a leakage current original data set; the leakage current data comprises a current harmonic distortion rate and a harmonic of resistive current in the leakage current; the kth timeHarmonic distortion rate of current
The calculation formula is as follows:
wherein:
the kth harmonic representing the resistive current in the leakage current,/th harmonic>
The nth harmonic wave of the resistive current in the leakage current is represented, and the value range of n is 1-k;
the harmonic calculation formula of the resistive current in the kth leakage current is as follows:
wherein:
nonlinear resistor representing leakage current flow, +.>
Indicating the angular frequency of the system>
Time is indicated.
The three-dimensional fault feature vector of the support vector machine discriminant model comprises a harmonic of resistive current in the first leakage current, a harmonic of resistive current in the third leakage current and a harmonic of resistive current in the fifth leakage current.
Step 2: preprocessing the collected original data set to form a sample set, extracting the data characteristics of equipment measuring points, calculating the data of each subharmonic of leakage current, and processing the data into three-dimensional characteristic vectors to form an equipment characteristic data set; the specific way of preprocessing the original data to construct a sample set is as follows: processing the original data by a principal component analysis and attribute reduction method to obtain feature data, and respectively carrying out normalization processing on the feature data to obtain a minimum feature data set; and constructing a lightning arrester leakage current training sample set by taking the minimum characteristic data set as a leakage current test data set.
Step 3: constructing a lightning arrester running condition test sample set based on the characteristic data set, training and testing by adopting a branch vector machine algorithm to form a leakage current detection support vector machine algorithm model; the lightning arrester leakage current training sample set is divided into a training data packet set, a test data set and a verification data set; training the training set by using a support vector machine algorithm through the training set and the verification set to obtain a classification algorithm model for detecting leakage current of the lightning arrester of the support vector machine; and determining the accuracy of the support vector machine algorithm model to the detection of the leakage current of the lightning arrester through the test set.
Step 4: and identifying the operation condition of the lightning arrester through a leakage current detection support vector machine algorithm model, classifying leakage current signals, outputting the result, and alarming if the leakage current signals are abnormal. When the applied voltage is smaller than the reference voltage, the MOA is equivalent to a very large resistor, and the change of the resistance value is very small; as the voltage applied to the MOA approaches or exceeds the reference voltage, its nonlinear resistance decreases rapidly and the resistive current component increases rapidly. Aging of the metal oxide valve plate can cause the nonlinear characteristics of the metal oxide valve plate to be poor, so that the higher harmonic component of the resistive current is obviously increased, and the fundamental component is relatively reduced. The higher harmonic component of MOA resistive leakage current is the basis for judging the aging condition of the metal oxide valve plate.
The specific steps for identifying the operation working condition of the lightning arrester are as follows: comparing the output result of the leakage current detection support vector machine algorithm model with the original MOA rated parameter; and classifying leakage current signals according to comparison results, wherein the leakage current signals comprise normal operation, general faults, serious faults and critical faults.
A lightning arrester leakage current detection system based on a multi-classification support vector machine comprises a current measurement unit, a characteristic processing unit, a model building and analyzing unit and a display and alarm unit;
the current signal acquisition unit is used for measuring the resistive leakage current of the lightning arrester and judging the state of the valve plate by monitoring the amplitude change of the resistive leakage current;
the characteristic processing unit preprocesses the original data set and determines the effective value of the leakage current, the maximum value of the current and the total harmonic distortion rate of the leakage current as selected characteristic parameters. Considering that the signal parameters have the characteristic of high coupling, the main component analysis characteristic extraction method is adopted to extract the fault characteristics in order to extract the key fault characteristics and save calculation force. And (5) normalizing fault characteristic parameters. Considering the difference of the dimensions of the fault characteristic parameters, the direct input as the model can influence the output of the model, so that the fault characteristic vector of each dimension is normalized. The parameters of the model affect the diagnostic performance of the model. Optimizing the diagnosis model parameters by using a grid search method, and taking the parameters of the optimal diagnosis rate of the training set as final diagnosis model parameters.
The model building and analyzing unit builds a support vector machine algorithm model, trains and tests the support vector machine algorithm model, and is used for measuring leakage current of the lightning arrester through the support vector machine algorithm model after training and testing, and judging the state of a zinc oxide valve plate of the lightning arrester;
the display and alarm unit compares and analyzes the collected leakage current signals with preset alarm values, judges whether the insulation state of the lightning arrester is abnormal, displays data and judgment results on a monitoring center screen, and controls the alarm unit to timely send alarm signals when the lightning arrester operates abnormally.
To sum up: through arrester leakage current non-contact wireless detecting system, the non-contact arrester leakage current measuring unit in this scheme can accomplish the real-time supervision of treating the survey line current value under the condition of operating voltage to can connect the characteristic processing unit on the backstage central processing unit and upload data through wireless mode, solve a series of problems that traditional access type current measuring mode brought, also reduced the cost when having improved efficiency. The classification model establishment and analysis unit can directly access the feature processing unit training data set, and output results at the display unit, and carry out audible and visual alarm if necessary. Maintenance personnel do not need to measure on site, and data can be monitored in real time at a far end, so that the working efficiency is greatly improved.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A lightning arrester leakage current detection method based on a multi-classification SVM is characterized by comprising the following steps:
step 1: acquiring leakage current data through a non-contact electromagnetic current sensor, and constructing a leakage current original data set;
step 2: preprocessing the collected original data set to form a sample set, extracting the data characteristics of equipment measuring points, calculating the data of each subharmonic of leakage current, and processing the data into three-dimensional characteristic vectors to form an equipment characteristic data set;
step 3: constructing a lightning arrester running condition test sample set based on the characteristic data set, training and testing by adopting a branch vector machine algorithm to form a leakage current detection support vector machine algorithm model;
step 4: and identifying the operation condition of the lightning arrester through a leakage current detection support vector machine algorithm model, classifying leakage current signals, outputting the result, and alarming if the leakage current signals are abnormal.
2. The lightning arrester leakage current detection method based on the multi-classification SVM according to claim 1, wherein the specific way of preprocessing the raw data to construct a sample set is as follows: processing the original data by a principal component analysis and attribute reduction method to obtain feature data, and respectively carrying out normalization processing on the feature data to obtain a minimum feature data set; and constructing a lightning arrester leakage current training sample set by taking the minimum characteristic data set as a leakage current test data set.
3. The lightning arrester leakage current detection method based on the multi-classification SVM according to claim 2, wherein the lightning arrester leakage current training sample set is divided into a training set, a test set and a verification set; training the training set by using a support vector machine algorithm through the training set and the verification set to obtain a classification algorithm model for detecting leakage current of the lightning arrester of the support vector machine; and determining the accuracy of the support vector machine algorithm model to the detection of the leakage current of the lightning arrester through the test set.
4. The lightning arrester leakage current detection method based on the multi-classification SVM according to claim 1, wherein the specific steps of identifying the operation condition of the lightning arrester are as follows: comparing the output result of the leakage current detection support vector machine algorithm model with the original MOA rated parameter; and classifying leakage current signals according to comparison results, wherein the leakage current signals comprise normal operation, general faults, serious faults and critical faults.
5. The multi-classification SVM based lightning arrester leakage current detection method of claim 1, wherein the leakage current data includes a current harmonic distortion rate and a harmonic of resistive current in the leakage current; the kth current harmonic distortion rate
The calculation formula is as follows:
wherein:
indicating leakageThe kth harmonic of the resistive current in the current, < >>
The nth harmonic wave of the resistive current in the leakage current is represented, and the value range of n is 1-k;
the harmonic calculation formula of the resistive current in the kth leakage current is as follows:
wherein:
nonlinear resistor representing leakage current flow, +.>
Indicating the angular frequency of the system>
Time is indicated.
6. The method for detecting leakage current of lightning arrester based on multi-classification SVM according to claim 5, wherein the three-dimensional fault feature vector of the support vector machine discriminant model includes a harmonic of resistive current in the first leakage current, a harmonic of resistive current in the third leakage current, and a harmonic of resistive current in the fifth leakage current.
7. A lightning arrester leakage current detection system using the multi-classification SVM according to any of claims 1-6, characterized by comprising a current measurement unit, a feature processing unit, a model building and analysis unit and a display and alarm unit;
the current signal acquisition unit is used for measuring the resistive leakage current of the lightning arrester and judging the state of the valve plate by monitoring the amplitude change of the resistive leakage current;
the characteristic processing unit is used for preprocessing an original data set and carrying out numerical processing and normalization processing; then, feature selection is carried out, in the feature selection, feature extraction is carried out by utilizing a principal component analysis method, feature dimension reduction is carried out on an original data set through attribute reduction, and the feature data set after dimension reduction is used as a training data set for subsequent use;
the model building and analyzing unit is used for building a support vector machine algorithm model, training and testing the support vector machine algorithm model, and identifying the support vector machine algorithm model after training and testing;
the display module in the display and alarm unit is communicated with the model building and analyzing unit and the alarm unit, the judging result of the characteristic data set is displayed, the characteristic data in the characteristic processing unit is displayed, the leakage current signal is compared with the leakage current alarm value, and if the leakage current signal is larger than the leakage current alarm value, the alarm unit is controlled to send out an audible and visual alarm signal.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116879663A (en) * | 2023-09-06 | 2023-10-13 | 杭州易龙防雷科技有限公司 | SPD online life prediction system and prediction method based on multi-parameter monitoring |
| CN117435947A (en) * | 2023-12-20 | 2024-01-23 | 山东和兑智能科技有限公司 | Lightning arrester state monitoring system and method |
| CN118033350A (en) * | 2024-04-11 | 2024-05-14 | 江苏安之技科技发展有限公司 | Wireless acquisition and monitoring system and method for operating parameters of lightning arrester |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116879663A (en) * | 2023-09-06 | 2023-10-13 | 杭州易龙防雷科技有限公司 | SPD online life prediction system and prediction method based on multi-parameter monitoring |
| CN116879663B (en) * | 2023-09-06 | 2023-12-15 | 杭州易龙防雷科技有限公司 | SPD online life prediction system and prediction method based on multi-parameter monitoring |
| CN117435947A (en) * | 2023-12-20 | 2024-01-23 | 山东和兑智能科技有限公司 | Lightning arrester state monitoring system and method |
| CN118033350A (en) * | 2024-04-11 | 2024-05-14 | 江苏安之技科技发展有限公司 | Wireless acquisition and monitoring system and method for operating parameters of lightning arrester |
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