A lightning arrester performance data collection method and system

Abstract

The present invention relates to the field of data acquisition technology, and specifically to a lightning arrester performance data acquisition method and system. The method of the present invention obtains the overall change degree of output current data by analyzing the volatility of output current data; performs cluster analysis on the output current data to obtain target cluster clusters; analyzes the consistency between adjacent target cluster clusters in combination with the overall change degree to obtain abnormal cluster clusters; analyzes the consistency between abnormal cluster clusters and adjacent target cluster clusters to obtain the resistor fault timing position of the output current data; based on the resistor fault timing position, analyzes the stability of the output current data after the resistor fault timing position to determine whether the lightning arrester resistor is abnormal. The method of the present invention takes into account the influence of the instability of the input current when making abnormal judgments on the lightning arrester resistor, improves the accuracy of the judgment of the resistor fault timing position, and thus improves the accuracy and reliability of the lightning arrester resistor performance evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and in particular to a method and system for acquiring performance data of a lightning arrester. Background Art

[0002] Lightning arresters are used to protect power systems, communication equipment and other facilities from lightning. Lightning arresters reduce the direct impact of lightning on equipment and improve the stability and reliability of the system by directing lightning current into the ground or dispersing it into the atmosphere. The resistor is located at the top or high up of the lightning arrester and is part of the guide device, responsible for rapid discharge when lightning strikes. These resistors are usually made of metal materials and can withstand and guide large currents. However, because lightning is an instantaneous current, the resistor needs to withstand a very high current in a short period of time. At the same time, the temperature of the resistor will rise. In a high temperature environment, thermal aging may occur, its characteristics will change, and the resistance value may increase or decrease, affecting the normal operation of the lightning arrester. Therefore, it is necessary to evaluate the performance of the resistor to determine the service life of the resistor.

[0003] In the prior art, the performance of the lightning arrester resistor is evaluated and judged mainly by simulating lightning strikes on the resistor in a laboratory environment, and then collecting the changes in the current data passing through the resistor to evaluate its performance. When analyzing the current data of the resistor, since the current of the simulated lightning strike is very large, the transient current passing through the resistor is also very large, but because the current changes during the simulated lightning strike, the current data is very volatile; therefore, when using the traditional data anomaly analysis method to analyze the current data of the resistor, the changes in the simulated lightning strike current will cause the abnormality of the current data to increase, thereby interfering with the performance evaluation of the resistor, resulting in inaccurate performance evaluation of the resistor. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method and system for collecting lightning arrester performance data.

[0005] According to the first aspect of the embodiment of the present application, a method for collecting lightning arrester performance data is provided, and the technical solution adopted is as follows:

[0006] Set up the experimental device to collect the output current data of the lightning arrester resistor;

[0007] Analyzing the volatility of the output current data to obtain the overall degree of change of the output current data;

[0008] Performing cluster analysis on the output current data to obtain a target cluster of the output current data;

[0009] In combination with the overall change degree, the consistency between the adjacent target clusters is analyzed to obtain abnormal clusters;

[0010] Analyzing the consistency between the abnormal cluster and the adjacent target cluster to obtain the resistor fault time sequence position of the output current data;

[0011] Based on the timing position of the resistor fault, the stability of the output current data after the timing position of the resistor fault is analyzed to determine whether the resistor of the lightning arrester is abnormal.

[0012] In some embodiments of the present invention, an experimental device is arranged to collect output current data of a lightning arrester resistor, and the output current data is arranged according to a time series cycle to obtain a current time series cycle data sequence.

[0013] In some embodiments of the present invention, the fluctuation difference between the output current data of adjacent timing periods in the current timing cycle data sequence is analyzed, and the change rate of the output current data is analyzed to obtain the overall change degree of the output current data.

[0014] In some embodiments of the present invention, analyzing the volatility of the output current data to obtain the overall degree of change of the output current data includes:

[0015] Decomposing the output current data using an STL algorithm to obtain a residual term of the output current data;

[0016] Using the PCA algorithm to obtain the principal component function of the residual term, and obtaining the rate of change of the principal component function;

[0017] Analyze the residual terms of the output current data of adjacent time series periods in the current time series period data sequence The distance relationship is combined with the change rate to obtain the overall change degree of the output current data.

[0018] In some embodiments of the present invention, cluster analysis is performed on the output current data to obtain a target cluster of the output current data, including:

[0019] Extracting the maximum value point of the output current data in each time sequence period in the current time sequence period data sequence;

[0020] Arrange the maximum value points in time sequence to obtain a current maximum value data sequence;

[0021] A hierarchical clustering algorithm is used to perform cluster analysis on the current maximum value data sequence to obtain a target cluster of the output current data.

[0022] In some embodiments of the present invention, combining the overall change degree, analyzing the consistency between adjacent target clusters to obtain abnormal clusters includes:

[0023] Analyzing the cluster change degree of the output current data contained in the target cluster, and combining the overall change degree to obtain the consistency of the cluster change degree;

[0024] Analyzing the difference in amplitude between the target clusters before and after the target cluster in time sequence to obtain a first consistency of the amplitude;

[0025] According to the consistency of the cluster change degree and the first consistency of the amplitude, combined with the number of the output current data in the target cluster, the possibility that the target cluster is an abnormal cluster is obtained;

[0026] According to the possibility that the target cluster is an abnormal cluster, a possibility threshold is set to obtain the abnormal cluster.

[0027] In some embodiments of the present invention, analyzing the consistency between the abnormal cluster and the adjacent target cluster to obtain the resistor fault timing position of the output current data includes:

[0028] Analyze the difference in average amplitude between the abnormal cluster and the target clusters adjacent to each other in time sequence to obtain a second consistency of amplitude;

[0029] According to the second consistency of the amplitude, combined with the possibility that the target cluster is an abnormal cluster, the possibility that the abnormal cluster is a timing position of a resistor fault is obtained;

[0030] The abnormal cluster is selected as the abnormal cluster with the greatest possibility of being the resistor failure timing position, and its corresponding timing position is the resistor failure timing position of the output current data.

[0031] In some embodiments of the present invention, based on the timing position of the resistor fault, analyzing the stability of the output current data after the timing position of the resistor fault, and judging whether the lightning arrester resistor is abnormal, includes:

[0032] Based on the timing position of the resistor fault, analyzing the correlation between the output current data after the timing position of the resistor fault in different timing periods to obtain the abnormality degree of the resistor;

[0033] According to the abnormality level, an abnormality level threshold is set to determine whether the lightning arrester resistor is abnormal.

[0034] According to a second aspect of an embodiment of the present application, a lightning arrester performance data acquisition system is provided, comprising: a memory and a processor, wherein:

[0035] The memory is used to store program code;

[0036] The processor is used to read the program code stored in the memory and execute any of the methods described above.

[0037] In some embodiments of the present invention, the processor comprises:

[0038] Current sensor module, used to collect output current data of lightning arrester resistor;

[0039] An overall variation degree analysis module, used for analyzing the volatility of the output current data to obtain the overall variation degree of the output current data;

[0040] A cluster analysis module, used for performing cluster analysis on the output current data to obtain a target cluster of the output current data;

[0041] An abnormal cluster analysis module is used to analyze the consistency between adjacent target clusters in combination with the overall change degree to obtain abnormal clusters;

[0042] An abnormal position judgment module is used to analyze the consistency between the abnormal cluster and the adjacent target cluster to obtain the resistor fault time sequence position of the output current data;

[0043] The resistor performance evaluation module is used to analyze the stability of the output current data after the resistor fault timing position based on the resistor fault timing position, and determine whether the lightning arrester resistor is abnormal.

[0044] Compared with the prior art, the lightning arrester performance data collection method and system provided by the present invention have the following beneficial effects:

[0045] The present invention arranges an experimental device for collecting the output current data of the lightning arrester resistor to provide a data basis for subsequent analysis. The volatility of the output current data is analyzed to obtain the overall degree of change of the output current data for subsequent abnormal clustering analysis. The output current data is clustered to obtain the target clustering cluster of the output current data. Different target clustering clusters represent different data changes. Similar output current data are clustered together by clustering to improve the accuracy of subsequent abnormal analysis of the lightning arrester resistor. Combined with the overall degree of change, the consistency between adjacent target clustering clusters is analyzed to obtain the abnormal clustering cluster; the consistency between the abnormal clustering cluster and the adjacent target clustering cluster is analyzed to obtain the resistor fault sequence position of the output current data; on the basis of obtaining the abnormal clustering cluster, based on the fact that the performance failure of the lightning arrester resistor is irreversible, and the input current fluctuation is only accidental, the change characteristics of the abnormal clustering cluster neighborhood are further analyzed to screen the abnormal clustering cluster, remove the influence caused by the input current fluctuation, improve the accuracy of the resistor fault sequence position judgment, and provide a basis for subsequent abnormal judgment of the lightning arrester resistor. Finally, based on the timing position of the resistor fault, the stability of the output current data after the timing position of the resistor fault is analyzed to determine whether the lightning arrester resistor is abnormal, thereby improving the accuracy and reliability of the lightning arrester resistor performance evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 A basic flow chart of a method for collecting lightning arrester performance data provided by an embodiment of the present invention;

[0048] Figure 2 A schematic diagram of a basic flow chart of a method for obtaining abnormal clustering clusters provided by an embodiment of the present invention;

[0049] Figure 3 A basic flow chart of a method for dividing output current data into resistor fault timing locations provided by an embodiment of the present invention;

[0050] Figure 4 A basic flow chart of a method for determining whether a lightning arrester resistor is abnormal provided by an embodiment of the present invention;

[0051] Figure 5 A schematic diagram of the basic composition of a lightning arrester performance data acquisition system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a lightning arrester performance data collection method and system proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. Terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element.

[0054] Because lightning is an instantaneous current, the resistor needs to withstand a very high current in a short period of time. At the same time, the temperature of the resistor will rise. In a high temperature environment, thermal aging may occur, its characteristics will change, and the resistance value may increase or decrease, affecting the normal operation of the lightning arrester. Therefore, it is necessary to evaluate the performance of the lightning arrester resistor to determine the service life of the lightning arrester resistor.

[0055] In a laboratory environment, the lightning arrester resistor is continuously shocked with electric shocks, and the output current data of the lightning arrester resistor is collected. By analyzing the changes in the output current data at different times, it is determined whether the lightning arrester resistor fails after multiple electric shocks, and the degree of the fault is determined based on the changes in the output current data.

[0056] The specific scheme of the lightning arrester performance data collection method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0057] See also Figure 1 , which shows the basic process of a lightning arrester performance data collection method provided by an embodiment of the present invention.

[0058] like Figure 1 As shown, a lightning arrester performance data collection method provided by an embodiment of the present invention specifically includes:

[0059] S100: Set up the experimental device to collect the output current data of the lightning arrester resistor.

[0060] The lightning arrester is set in a position parallel to the ground of the protected equipment. All kinds of lightning arresters have a common characteristic, that is, they are in a low-resistance state under high current and in a high-resistance state under low current. When a lightning strike occurs, when the lightning wave overcurrent is transmitted along the line to the lightning arrester installation point, the lightning arrester will operate and be in a low-resistance state because the current acting on the lightning arrester is very high at this time, thereby limiting the overcurrent and discharging the large current caused by the overcurrent into the ground, so that the equipment connected in parallel with it is protected from damage by the overcurrent. After the lightning intrusion wave disappears, the line resumes the normally transmitted power frequency current, which is low relative to the lightning intrusion wave overcurrent, so the lightning arrester will be transformed into a high-resistance state, close to an open circuit. At this time, the existence of the lightning arrester will not respond to the transmission of normal power frequency current on the line.

[0061] In practical applications, it is difficult to measure the current of lightning because the current of lightning is too high, usually reaching several million volts. Therefore, in order to detect the stability of the lightning arrester, an experimental device is set up in a laboratory environment, and a high-voltage transformer is used to increase the current to 100,000V, that is, the power-on current is 100,000V, and then a current sensor is installed on the lightning arrester to collect the output current data of the lightning arrester resistor each time it is powered on. The experimental device is powered on, and the power-on process is instantaneous power-on. The power-on interval is 5 seconds each time, and the number of power-ons is 100 times. The output current data of the lightning arrester resistor is recorded each time. The output current data between the start of one power-on and the start of the next power-on is taken as a time sequence period. Further, the output current data is arranged according to the time sequence period to obtain the current time sequence period data sequence.

[0062] S200: Analyze the volatility of the output current data to obtain the overall variation degree of the output current data.

[0063] The output current data of the lightning arrester resistor obtained in step S100 is time-series cycle data, with five seconds as one data cycle. There are differences in the output current data in different cycles, which are caused by voltage changes and lightning arrester resistor failures. Because the voltage used to energize the lightning arrester resistor is very high, it is impossible to ensure that the voltage is the same every time, and during the test, the lightning arrester resistor will also change its resistance due to heat, so the monitored output current data will inevitably fluctuate.

[0064] Based on the above analysis, this embodiment first analyzes the volatility of the output current data to obtain the overall degree of change of the output current data. Further, by analyzing the fluctuation difference between the output current data of adjacent time series periods in the current time series periodic data sequence and analyzing the degree of change of the output current data, the overall degree of change of the output current data is obtained.

[0065] Specifically, in order to be able to represent the changes in the output current data, the STL algorithm is first used to decompose the output current data to obtain the decomposed data of the output current data. The decomposed data contains three items: periodic term, trend term, and residual term. Among them, the periodic term represents the periodic fluctuation of the output current data; the trend term represents the long-term trend or direction in the output current data, which represents the main movement direction of the time series, and shows the trend of the output current data gradually increasing, decreasing or stabilizing over time; the residual term represents the random fluctuation in the output current data after removing the periodic term and the trend term. This component usually includes error terms and other irregular factors in the output current data, such as outliers or unpredictable mutations. The excitation value of the original output current data can be judged based on the decomposed data. Therefore, the PCA algorithm is used to obtain the principal component function of the residual term, and the slope of the principal component function is obtained. The rate of change of the principal component function is the slope of the principal component function; the relationship between the residual terms of the output current data of adjacent time series periods in the current time series periodic data sequence is analyzed. The distance relationship, combined with the rate of change, gives the overall degree of change in the output current data.

[0066] It should be noted that the STL algorithm and the PCA algorithm are well known to those skilled in the art, and will not be further limited or elaborated in the embodiments of the present invention.

[0067] The calculation formula for the overall change degree of the output current data is:

[0068]

[0069] In the formula, Indicates the overall change degree of output current data; The slope of the principal component function representing the residual term; Indicates the first The residual term of the output current data of the first time cycle is The residual term of the output current data of the timing cycle Distance value; Indicates the number of timing cycles in the current timing cycle data sequence; Represents the function that takes the minimum value of the data value.

[0070] Indicates the rate of change of the residual term. The larger the value, the greater the overall change of the output current data. It represents the fluctuation difference between the residual items of the output current data in two adjacent time series periods in the current time series period data sequence. It represents the mean of the fluctuation difference between the residual items of the output current data in two adjacent time series periods in the current time series period data sequence. The smaller the value, the more similar the residual items of the output current data in the two time series periods are, indicating that the overall change degree of the output current data is smaller; divided by The scale that represents the fluctuation difference. The larger the ratio, the greater the overall change in the output current data.

[0071] So far, the overall variation degree of the output current data has been obtained.

[0072] The performance of the lightning arrester resistor will gradually weaken as the number of electric shocks increases, that is, the more electric shocks there are, the resistance of the lightning arrester resistor will change, and the output current data will show current decay in the timing. In order to describe the changes in the output current data of the lightning arrester resistor under different electric shocks, the abnormal current point is determined by comparing the changes in the timing of the output current data of different times. Specifically, steps S300 and S400 are included.

[0073] S300: performing cluster analysis on the output current data to obtain a target cluster of the output current data.

[0074] In order to determine the abnormal current point, the output current data is first clustered and analyzed to obtain the target cluster of the output current data. The specific operation is as follows: extract the maximum value points of the output current data in each time series period in the current time series period data sequence; arrange the maximum value points in time series order to obtain the current maximum value data sequence; for the current maximum value data sequence, use the hierarchical clustering algorithm to perform cluster analysis, set the number of clustering layers to 2 (preset value), and obtain multiple clusters of the current maximum value data sequence, which are recorded as target clusters, that is, the target cluster of the output current data is obtained.

[0075] The hierarchical clustering algorithm is an algorithm well known to those skilled in the art, and will not be further limited or elaborated on.

[0076] S400: Analyze the consistency between adjacent target clusters based on the overall change degree to obtain abnormal clusters.

[0077] After obtaining the target clusters of the output current data, the changes of the target clusters can be analyzed to determine the abnormal current points. Therefore, this embodiment analyzes the consistency between adjacent target clusters in combination with the overall change degree to obtain the abnormal clusters.

[0078] See also Figure 2 , which shows the basic process of a method for obtaining abnormal clustering clusters provided by an embodiment of the present invention.

[0079] like Figure 2As shown in the figure, combined with the overall change degree, the consistency between adjacent target clusters is analyzed to obtain abnormal clusters, including:

[0080] S401: Analyze the cluster change degree of the output current data contained in the target cluster, and combine the overall change degree to obtain the consistency of the cluster change degree.

[0081] The cluster change degree of the output current data contained in the target cluster is analyzed. The specific method for obtaining the cluster change degree is the same as the method for obtaining the overall change degree of the output current data in step S200, and will not be repeated again. The cluster change degree is combined with the overall change degree, that is, the ratio of the cluster change degree to the overall change degree is calculated to obtain the consistency of the cluster change degree.

[0082] S402: Analyze the difference in amplitude between the target clusters before and after the target clusters in time sequence to obtain a first consistency of the amplitude.

[0083] The difference in amplitude between the target clusters before and after the target cluster in time series is analyzed, that is, the absolute value of the difference between the average amplitude of the previous target cluster and the average amplitude of the next target cluster in time series is calculated to obtain the first consistency of the amplitude.

[0084] S403: According to the consistency of the cluster change degree and the first consistency of the amplitude, combined with the number of output current data in the target cluster, the possibility of the target cluster being an abnormal cluster is obtained.

[0085] Different target clusters represent different changes in output current data. The more output current data points in the target cluster, the more similar the current changes in the data segment. If the number of output current data points in the target cluster is smaller, the more isolated the output current data points are, the greater the abnormality. Therefore, based on the consistency of the degree of cluster change and the first consistency of the amplitude, combined with the number of output current data in the target cluster, the possibility of the target cluster being an abnormal cluster is obtained. The calculation formula for the possibility of the target cluster being an abnormal cluster is:

[0086] In the formula, Indicates The possibility that a target cluster is an abnormal cluster; Indicates The degree of change of the output current data in the target cluster, that is, The degree of cluster change of the target clusters; Indicates the overall change degree of output current data; Represents a constant, used to prevent the denominator from being zero, and the value can be 0.01; Indicates The number of output current data in the target clusters; Indicates The mean amplitude of all output current data in the target cluster; Indicates The mean amplitude of all output current data in the target cluster; represents the linear normalization function.

[0087] The ratio of the cluster change degree to the overall change degree of the output current data contained in the current target cluster indicates the consistency of the cluster change degree and the overall change degree. The larger the ratio, the more consistent the cluster change degree is under the current electric shock, indicating that the performance of the lightning arrester resistor may have changed, so the output current data has a large fluctuation, and the target cluster is more likely to be an abnormal cluster. The smaller the value of is, the smaller the number of output current data in the target cluster is, which means that the difference between it and the adjacent output current data is greater, which means that the output current data is more isolated, and the possibility that the target cluster is an abnormal cluster is greater; Indicates The average amplitude difference between the two adjacent target clusters before and after a target cluster. When the lightning arrester resistor fails, the output current will change. Therefore, the larger the average amplitude difference of the neighboring target clusters of the target cluster, the greater the possibility that the current target cluster is an abnormal cluster.

[0088] S404: According to the possibility that the target cluster is an abnormal cluster, a possibility threshold is set to obtain the abnormal cluster.

[0089] According to the probability of each target cluster being an abnormal cluster calculated in step S403, a probability threshold is set , the likelihood threshold can be preset as . When the target cluster is the probability of an abnormal cluster Greater than When , there is an abnormal point in the target cluster, which is taken as the cluster where the abnormal point in the current data is located and recorded as the abnormal cluster. Then there are multiple abnormal clusters in the output current data.

[0090] S500: Analyze the consistency between the abnormal cluster and the adjacent target cluster to obtain the resistor fault time sequence position of the output current data.

[0091] There are two reasons for the change in output current data. One is that the input current of the original UHV in the laboratory environment is unstable, causing the output current data to fluctuate. The other is that the lightning arrester resistor fails, causing the output current data to change differently during different electric shocks, resulting in fluctuations. The performance failure of the lightning arrester resistor is irreversible, so when an abnormality occurs, the monitored output current data will continue to be abnormal; and the input current fluctuation is only accidental, so the abnormal clusters are screened according to the changes in the output current data in the neighboring target clusters of the abnormal clusters to remove the impact caused by the input current fluctuations.

[0092] Based on the above analysis, this embodiment obtains the resistor fault timing position of the output current data by analyzing the consistency between the abnormal cluster and the adjacent target cluster.

[0093] See also Figure 3 , which shows the basic process of a resistor fault timing location method for dividing output current data provided by an embodiment of the present invention.

[0094] like Figure 3 As shown, the consistency between the abnormal cluster and the adjacent target cluster is analyzed to obtain the resistor fault timing position of the output current data, including:

[0095] S501: Analyze the difference in average amplitude between the abnormal cluster and the target clusters that are adjacent to each other in time sequence to obtain a second consistency of the amplitude.

[0096] Analyze the difference in average amplitude between the abnormal clustering cluster and the target clustering clusters adjacent to each other in time series, specifically, calculate the first absolute value of the difference in average amplitude between the previous target clustering cluster in time series and the abnormal clustering cluster, and calculate the second absolute value of the difference in average amplitude between the previous target clustering cluster and the next target clustering cluster in time series of the abnormal clustering cluster, and then calculate the sum of the first absolute value of the difference and the second absolute value of the difference to obtain the second consistency of the amplitude.

[0097] S502: According to the second consistency of the amplitude, combined with the possibility that the target cluster is an abnormal cluster, the possibility that the abnormal cluster is the timing position of the resistor fault is obtained.

[0098] According to the second consistency of the amplitude, combined with the possibility that the target cluster is an abnormal cluster, the possibility that the abnormal cluster is the timing position of the resistor fault is obtained. The calculation formula for the possibility that the abnormal cluster is the timing position of the resistor fault is:

[0099]

[0100] In the formula, Indicates The possibility that the abnormal clusters are the timing positions of the resistor faults; Indicates The possibility that an abnormal cluster is an abnormal cluster; Indicates The average amplitude of the target clusters, Indicates The average amplitude of the abnormal clusters, Indicates The average amplitude of the target clusters.

[0101] It indicates the difference in average amplitude between the abnormal cluster and the target clusters adjacent to it in time series. The larger the value is, the greater the difference between the previous target cluster and the two target clusters after it in time series is. This indicates that the target cluster after the abnormal cluster is more likely to be an abnormal cluster, and the abnormal cluster is more likely to be the timing position of the resistor fault. The larger the value of is, the greater the possibility that the abnormal cluster is the timing position of the resistor fault.

[0102] S503: Selecting the abnormal cluster with the greatest possibility of being the resistor failure timing position, and the corresponding timing position is the resistor failure timing position of the output current data.

[0103] After obtaining the possibility that all abnormal clusters are the timing positions of resistor faults, the abnormal cluster with the greatest possibility of being the timing position of resistor faults is selected, and its corresponding timing position is the timing position of the resistor fault of the output current data.

[0104] S600: Based on the timing position of the resistor fault, analyze the stability of the output current data after the timing position of the resistor fault, and determine whether the resistor of the lightning arrester is abnormal.

[0105] According to the above steps, the resistor fault position in the output current data is obtained, and then the abnormality of the lightning arrester resistor is determined according to the changes in different output current data. The embodiment of the present application is based on the resistor fault timing position, analyzes the stability of the output current data after the resistor fault timing position, and determines whether the lightning arrester resistor is abnormal.

[0106] See also Figure 4 , which shows the basic process of a method for determining whether a lightning arrester resistor is abnormal provided by an embodiment of the present invention.

[0107] like Figure 4As shown, based on the resistor fault timing position, the stability of the output current data after the resistor fault timing position is analyzed to determine whether the lightning arrester resistor is abnormal, including:

[0108] S601: Based on the timing position of the resistor fault, the correlation between the output current data after the timing position of the resistor fault and the different timing periods is analyzed to obtain the abnormality degree of the resistor.

[0109] Because the performance of the lightning arrester resistor changes, the impedance capacity to the current will decrease, which will manifest as unstable output current. Therefore, when evaluating the performance of the lightning arrester resistor, the instability of the output current is mainly obtained based on the change in the output current data between the timing periods after the resistor fault timing position. In an embodiment of the present invention, based on the resistor fault timing position, the correlation between the output current data after the resistor fault timing position between different timing periods is analyzed to obtain the abnormality degree of the resistor. The calculation formula for the abnormality degree of the resistor is:

[0110]

[0111] In the formula, Indicates the abnormality of the lightning arrester resistor; Indicates the first position after the resistor failure timing position The output current data of the first timing cycle is Pearson correlation coefficient between the output current data of the timing cycles; Indicates the number of timing cycles after the timing position of the resistor failure; Represents a constant, used to prevent the denominator from being zero, and the value can be 0.01; represents the linear normalization function.

[0112] It represents the reciprocal of the average Pearson correlation coefficient between the output current data of all timing periods after the timing position of the resistor fault. The larger the value is, the smaller the similarity between the output current data of all timing periods is, which means that the output current data of all timing periods is unstable, indicating that the abnormality of the lightning arrester resistor is greater.

[0113] S602: According to the abnormality level, an abnormality level threshold is set to determine whether the lightning arrester resistor is abnormal.

[0114] According to the abnormality of the lightning arrester resistor obtained by calculation in step S601, the abnormality threshold is set , the abnormality threshold can be preset as When the abnormality Greater than the abnormality threshold When the output current of the lightning arrester resistor is abnormal, it means that the output current of the lightning arrester resistor is abnormal; conversely, when the abnormal degree is Not greater than the abnormality threshold When , it means that there is no abnormality in the output current of the lightning arrester resistor.

[0115] Based on the same inventive concept as the above method, this embodiment also provides a lightning arrester performance data acquisition system.

[0116] See also Figure 5 , which shows the basic composition of a lightning arrester performance data acquisition system provided by an embodiment of the present invention.

[0117] like Figure 5 As shown, a lightning arrester performance data acquisition system includes: a memory 10 and a processor 20, wherein:

[0118] A memory 10, used for storing program codes;

[0119] The processor 20 is used to read the program code stored in the memory 10, and execute the layout of the experimental device to collect the output current data of the lightning arrester resistor; analyze the volatility of the output current data to obtain the overall change degree of the output current data; perform cluster analysis on the output current data to obtain the target cluster cluster of the output current data; analyze the consistency between adjacent target cluster clusters in combination with the overall change degree to obtain the abnormal cluster cluster; analyze the consistency between the abnormal cluster cluster and the adjacent target cluster cluster to obtain the resistor fault timing position of the output current data; based on the resistor fault timing position, analyze the stability of the output current data after the resistor fault timing position to determine whether the lightning arrester resistor is abnormal.

[0120] Furthermore, the processor 20 includes a current sensor module, an overall change degree analysis module, a cluster analysis module, an abnormal cluster analysis module, an abnormal position judgment module, and a resistor performance evaluation module, wherein:

[0121] The current sensor module 21 is used to collect the output current data of the lightning arrester resistor;

[0122] The overall variation degree analysis module 22 is used to analyze the volatility of the output current data and obtain the overall variation degree of the output current data;

[0123] A cluster analysis module 23, used to perform cluster analysis on the output current data to obtain a target cluster of the output current data;

[0124] The abnormal cluster analysis module 24 is used to analyze the consistency between adjacent target clusters in combination with the overall change degree to obtain abnormal clusters;

[0125] The abnormal position judgment module 25 is used to analyze the consistency between the abnormal cluster and the adjacent target cluster to obtain the resistor fault time sequence position of the output current data;

[0126] The resistor performance evaluation module 26 is used to analyze the stability of the output current data after the resistor fault timing position based on the resistor fault timing position, and determine whether the lightning arrester resistor is abnormal.

[0127] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0128] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A lightning arrester performance data collection method, characterized in that: The method comprises: Set up the experimental device to collect the output current data of the lightning arrester resistor; Analyzing the volatility of the output current data to obtain the overall degree of change of the output current data; Performing cluster analysis on the output current data to obtain a target cluster of the output current data; In combination with the overall change degree, the consistency between the adjacent target clusters is analyzed to obtain abnormal clusters; Analyzing the consistency between the abnormal cluster and the adjacent target cluster to obtain the resistor fault time sequence position of the output current data; Based on the timing position of the resistor fault, analyzing the stability of the output current data after the timing position of the resistor fault, and judging whether the resistor of the lightning arrester is abnormal; The experimental device is arranged to collect the output current data of the lightning arrester resistor, and the output current data is arranged according to the time series cycle to obtain the current time series cycle data sequence; Combined with the overall change degree, the consistency between the adjacent target clusters is analyzed to obtain abnormal clusters, including: Analyzing the cluster change degree of the output current data contained in the target cluster, and combining the overall change degree to obtain the consistency of the cluster change degree; Analyzing the difference in amplitude between the target clusters before and after the target cluster in time sequence to obtain a first consistency of the amplitude; According to the consistency of the cluster change degree and the first consistency of the amplitude, combined with the number of the output current data in the target cluster, the possibility that the target cluster is an abnormal cluster is obtained; According to the possibility that the target cluster is an abnormal cluster, a possibility threshold is set to obtain the abnormal cluster; Analyzing the consistency between the abnormal cluster and the adjacent target cluster to obtain the resistor fault time sequence position of the output current data includes: Analyze the difference in average amplitude between the abnormal cluster and the target clusters adjacent to each other in time sequence to obtain a second consistency of amplitude; According to the second consistency of the amplitude, combined with the possibility that the target cluster is an abnormal cluster, the possibility that the abnormal cluster is a timing position of a resistor fault is obtained; The abnormal cluster is selected as the abnormal cluster with the greatest possibility of being the resistor failure timing position, and its corresponding timing position is the resistor failure timing position of the output current data.

2. The lightning arrester performance data collection method according to claim 1, characterized in that: The fluctuation difference between the output current data of adjacent time periods in the current time period data sequence is analyzed, and the change rate of the output current data is analyzed to obtain the overall change degree of the output current data.

3. The lightning arrester performance data collection method according to claim 2 is characterized in that: Analyzing the volatility of the output current data to obtain the overall degree of change of the output current data includes: Decomposing the output current data using an STL algorithm to obtain a residual term of the output current data; Using the PCA algorithm to obtain the principal component function of the residual term, and obtaining the rate of change of the principal component function; The DTW distance relationship between the residual items of the output current data of adjacent time series periods in the current time series period data sequence is analyzed, and combined with the change rate, the overall change degree of the output current data is obtained.

4. The lightning arrester performance data collection method according to claim 1 is characterized in that: Performing cluster analysis on the output current data to obtain a target cluster of the output current data includes: Extracting the maximum value point of the output current data in each time sequence period in the current time sequence period data sequence; Arrange the maximum value points in time sequence to obtain a current maximum value data sequence; A hierarchical clustering algorithm is used to perform cluster analysis on the current maximum value data sequence to obtain a target cluster of the output current data.

5. The lightning arrester performance data collection method according to claim 1, characterized in that: Based on the timing position of the resistor fault, analyzing the stability of the output current data after the timing position of the resistor fault, and judging whether the lightning arrester resistor is abnormal, including: Based on the timing position of the resistor fault, analyzing the correlation between the output current data after the timing position of the resistor fault in different timing periods to obtain the abnormality degree of the resistor; According to the abnormality level, an abnormality level threshold is set to determine whether the lightning arrester resistor is abnormal.

6. A lightning arrester performance data acquisition system, characterized in that: The system comprises: a memory and a processor, wherein: The memory is used to store program codes; The processor is configured to read the program code stored in the memory and execute the method according to any one of claims 1 to 5.

7. The lightning arrester performance data acquisition system according to claim 6, characterized in that: The processor comprises: Current sensor module, used to collect output current data of lightning arrester resistor; An overall variation degree analysis module, used for analyzing the volatility of the output current data to obtain the overall variation degree of the output current data; A cluster analysis module, used for performing cluster analysis on the output current data to obtain a target cluster of the output current data; An abnormal cluster analysis module is used to analyze the consistency between adjacent target clusters in combination with the overall change degree to obtain abnormal clusters; An abnormal position judgment module is used to analyze the consistency between the abnormal cluster and the adjacent target cluster to obtain the resistor fault time sequence position of the output current data; A resistor performance evaluation module, used to analyze the stability of the output current data after the resistor fault timing position based on the resistor fault timing position, and determine whether the lightning arrester resistor is abnormal; The experimental device is arranged to collect the output current data of the lightning arrester resistor, and the output current data is arranged according to the time series cycle to obtain the current time series cycle data sequence; Combined with the overall change degree, the consistency between the adjacent target clusters is analyzed to obtain abnormal clusters, including: Analyzing the cluster change degree of the output current data contained in the target cluster, and combining the overall change degree to obtain the consistency of the cluster change degree; Analyzing the difference in amplitude between the target clusters before and after the target cluster in time sequence to obtain a first consistency of the amplitude; According to the consistency of the cluster change degree and the first consistency of the amplitude, combined with the number of the output current data in the target cluster, the possibility that the target cluster is an abnormal cluster is obtained; According to the possibility that the target cluster is an abnormal cluster, a possibility threshold is set to obtain the abnormal cluster; Analyzing the consistency between the abnormal cluster and the adjacent target cluster to obtain the resistor fault time sequence position of the output current data includes: Analyze the difference in average amplitude between the abnormal cluster and the target clusters adjacent to each other in time sequence to obtain a second consistency of amplitude; According to the second consistency of the amplitude, combined with the possibility that the target cluster is an abnormal cluster, the possibility that the abnormal cluster is a timing position of a resistor fault is obtained; The abnormal cluster is selected as the abnormal cluster with the greatest possibility of being the resistor failure timing position, and its corresponding timing position is the resistor failure timing position of the output current data.

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