Relay balance anomaly detection method and system

Abstract

The invention relates to the technical field of data processing, in particular to a relay balance anomaly detection method and system. The method comprises the steps of collecting current data, obtaining each extreme point in the local range of each current data, obtaining the distribution density of each extreme point in the local range of each current data, obtaining the noise expression degree of each current data based on the distribution density, and obtaining the noise expression degree of each current data according to the noise expression degree of each current data. The method comprises the following steps: dividing a current data sequence into a plurality of segments, filtering data in each segment by using a Vondrak filtering algorithm to obtain a filtered current data sequence, obtaining abnormal data according to the filtered current data sequence, and further judging the abnormal condition of the relay. And the accuracy of subsequent anomaly detection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a relay balance anomaly detection method and system. Background Art

[0002] As a common electrical protection device, relays are usually used to protect circuits or monitor abnormal current conditions in power systems. During the operation of the relay, the relay's ability to balance current is crucial to ensuring the normal operation of the circuit. In the process of detecting abnormalities in relay balance, it is necessary to first obtain the current data during the relay's operation. The accuracy of the current data greatly affects the accuracy of relay balance abnormality detection. Therefore, it is necessary to clean the current data during the relay's operation to improve the accuracy of subsequent abnormality detection.

[0003] The patent document with the current authorization announcement number CN110188315B proposes a data processing method for clock taming, including: obtaining the best straight line equation model of the observed data according to the random sampling consistency algorithm; calculating the straight-line distance of each observed data to the best straight-line equation model; assigning a corresponding weight coefficient to each observed data according to the straight-line distance corresponding to each observed data; using the weight coefficient corresponding to each observed data as the weight of the observed data in the Vondrak filtering method, and filtering the observed data using the Vondrak filtering method to obtain a filtering curve about the observed data.

[0004] Since the current data collected during the working process of the relay will be affected by noise to varying degrees, that is, the noise performance of the current data in different time periods will be different, when using the Vondrak filtering algorithm to denoise the current data, if the same smoothing factor is used to filter all current data, it may not be possible to achieve a good filtering effect for all current data, or frequently using the smoothing error method to determine the smoothing factor for each current data will result in excessive calculations, resulting in low filtering efficiency, and ultimately resulting in poor efficiency in detecting abnormalities in relay balance. Summary of the invention

[0005] In order to solve the technical problem that the Vondrak filtering algorithm may not achieve a good filtering effect on all current data when using the same smoothing factor to filter all current data, the present invention provides a relay balance anomaly detection method and system.

[0006] In a first aspect, the present invention provides a relay balance abnormality detection method, which adopts the following technical solution: A method for detecting a relay balance anomaly comprises the steps of: Collect current data; obtain the distribution density of each extreme point in the local range of each current data , represents the distribution density of the jth extreme point in the local range of the i-th current data; represents the value of the jth extreme point in the local range of the i-th current data; as well as Respectively represent the values ​​of the previous extreme point and the next extreme point of the jth extreme point in the local range of the i-th current data; as well as Respectively represent the sampling time corresponding to the next extreme point and the previous extreme point of the jth extreme point in the local range of the i-th current data; max() represents the maximum value function; the variance of the distribution density of all extreme points in the local range of each current data is taken as the noise performance degree of each current data; According to the noise performance degree of each current data, the current data is divided into several segments; the data in each segment is filtered to obtain a filtered current data sequence; according to the filtered current data sequence, abnormal data is obtained; according to the abnormal data, the abnormal situation of the relay is judged.

[0007] The innovation of the present invention lies in that it can obtain the noise performance degree of each current data by analyzing the changing characteristics of the current data during the operation of the relay, and then segment the current data according to the noise performance degree, so that the current data in each segment is subjected to the same noise performance degree, and the same smoothing factor is applied to filter in different segments, which can significantly improve the accuracy and effect of Vondrak filtering.

[0008] Preferably, the step of obtaining each extreme point in a local range of each current data includes: A local range of each current data is obtained, and an extreme value point in the local range of each current data is obtained using a peak detection algorithm.

[0009] Preferably, the obtaining of the local range of each current data includes: The number of sampling moments N is preset, and the current data at N sampling moments before the sampling moment corresponding to the i-th current data and the current data at N sampling moments after the sampling moment are used as the local range of the i-th current data.

[0010] This facilitates the subsequent analysis of the noise performance level of each current data based on the data in the local range of each current data.

[0011] Preferably, the current data is divided into several segments according to the noise performance level of each current data, including: A threshold parameter is preset to divide the current data sequence into several segments; wherein the absolute value of the difference between the mean value of the noise performance level of all data in each segment and the noise performance level of the first data in the next segment is less than the preset threshold parameter T.

[0012] Each current data is segmented according to its noise performance level, so that the current data in each segment is subjected to the same noise performance level, which facilitates subsequent individual filtering of each segment.

[0013] Preferably, filtering the data in each segment to obtain a filtered current data sequence includes: The number of arrays m is preset. According to the number of arrays m, the current data in each segment is divided into several arrays in turn. The smoothing error method is used to obtain the smoothing factor of the first array in each segment. According to the smoothing factor of the first array in each segment, Vondrak filtering is used to filter each array in each segment to obtain a filtered current data sequence.

[0014] The obtained filtered current data sequence is more consistent with the fluctuation of the real current data and eliminates the influence of noise data.

[0015] Preferably, obtaining abnormal data according to the filtered current data sequence includes: The preset current fluctuation range is [20-40mA]. If the value of any current data in the filtered current data sequence is not within the current fluctuation range, the current data is abnormal data.

[0016] The accuracy of abnormal data identification is improved and the influence of noise on abnormal data is avoided.

[0017] Preferably, the collecting current data includes: The preset sampling time is 2 seconds / time, and the total sampling is one hour. A current sensor is installed on the relay, and each current data is collected during the operation of the relay.

[0018] In a second aspect, the present invention provides a relay balance anomaly detection system, which adopts the following technical solution: A relay balance anomaly detection system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned relay balance anomaly detection method is implemented.

[0019] By adopting the above technical solution, the above relay balance anomaly detection method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0020] The present invention has the following technical effects: The purpose of the present invention is to obtain the noise performance level of each current data by analyzing the change characteristics of the current data during the operation of the relay, and then segment the current data according to the noise performance level, so that the current data in each segment is subjected to the same noise performance level, and the same smoothing factor is applied to filter in different segments, which can significantly improve the accuracy and effect of Vondrak filtering. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0022] Figure 1 It is a flow chart of a method for detecting a relay balance anomaly in an embodiment of the present invention; Figure 2 A schematic diagram of a horizontal rectangle formed by an extreme point and its adjacent extreme points before and after it. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0025] The embodiment of the present invention discloses a method for detecting abnormal balance of a relay, referring to Figure 1 , comprising steps S1 to S3: S1: Collect current data.

[0026] It should be noted that, firstly, it is necessary to obtain the current data of the relay during operation, and judge the abnormality of the relay according to the abnormality of the current data.

[0027] In the implementation of the present invention, the sampling time is preset to 2 seconds / time, and the data is collected for a total of one hour. A current sensor is installed on the relay. During the operation of the relay, each current data is collected, and the current data is sorted in sequence to obtain a current data sequence.

[0028] S2: Obtain each extreme point in the local range of each current data, obtain the distribution density of each extreme point in the local range of each current data, and obtain the noise performance degree of each current data based on the distribution density.

[0029] It should be noted that since the current data collected during the working process of the relay will be affected by noise to varying degrees, that is, the noise performance of the current data in different time periods will be different, when using the Vondrak filtering algorithm to denoise the current data, if the same smoothing factor is used to filter all current data, it may not be possible to achieve a good filtering effect for all current data, or frequently using the smoothing error method to determine the smoothing factor for each current data will result in excessive calculation and thus low filtering efficiency, which in turn leads to poor efficiency in detecting abnormalities in the balance of the relay; therefore, this step will divide the current data sequence into several segments according to the changing characteristics of the current data during the working process of the relay, so that the current data in each segment is subject to the same degree of noise performance, then using different smoothing factors for Vondrak filtering in different segments can effectively solve the problem of poor filtering effect caused by using the same smoothing factor, thereby significantly improving the filtering accuracy and efficiency of the Vondrak filtering algorithm for the relay current data sequence.

[0030] It should be further explained that, during the operation of the relay, the contacts (the contacts of the main circuit in the relay are subjected to electromagnetic attraction or release force to connect or disconnect the mechanism) may experience multiple bounces, which will cause noise in the current data. Such noise manifests as random fluctuations in a local range. Therefore, the random volatility of the data in the local range of the current data can be used to obtain the degree of noise performance of the current data.

[0031] If the distribution of extreme points in the local range of current data is more regular, it means that the random volatility is lower, which means that the current data is more likely to be normal data fluctuations. Conversely, the more irregular the distribution of extreme points is, the higher the random volatility is, which means that the current data is more likely to be noise data. Therefore, the regularity of the distribution of extreme points in the local range of current data is quantified using the consistency of the distribution density of each extreme point in the local range of current data. If the consistency of the distribution density of each extreme point in the local range of current data is higher, it means that the distribution of extreme points in the local range of current data is more regular, and the current data is normal data.

[0032] In the embodiment of the present invention, the number of sampling moments N is preset, the current data at N sampling moments before the sampling moment corresponding to the i-th current data and the current data at N sampling moments after the sampling moment are used as the local range of the i-th current data, and the extreme value point in the local range of the i-th current data is obtained using the peak detection algorithm; Get the distribution density of the jth extreme point in the local range of the i-th current data: ; In the formula, represents the distribution density of the jth extreme point in the local range of the i-th current data; represents the value of the jth extreme point in the local range of the i-th current data; represents the value of the previous extreme point of the jth extreme point in the local range of the i-th current data; represents the value of the next extreme point after the jth extreme point in the local range of the i-th current data; represents the sampling time corresponding to the next extreme point after the jth extreme point in the local range of the i-th current data; represents the sampling time corresponding to the previous extreme point of the jth extreme point in the local range of the i-th current data; max() represents the maximum value function; The value represents the inverse of the area of ​​the horizontal rectangle formed by the jth extreme point in the local range of the i-th current data and its two adjacent extreme points before and after. The larger the value, the greater the distribution density of the jth extreme point in the local range of the i-th current data. The length and width of the horizontal rectangle are the difference between the data value and the sampling time. See Figure 2 .

[0033] The variance of the distribution density of all extreme points in the local range of the i-th current data is taken as the noise performance degree of the i-th current data. The larger the value, the worse the consistency of the distribution density of the extreme points, and the greater the noise performance degree of the i-th current data.

[0034] S3: Divide the current data sequence into several segments according to the noise performance level of each current data.

[0035] It should be noted that after obtaining the noise performance level of each current data in the current data sequence, the current data sequence can be segmented according to the noise performance level of the current data, so that the noise impact on the current data in each segment is consistent, which facilitates the determination of the smoothing factor of the subsequent Vondrak filtering algorithm.

[0036] In the embodiment of the present invention, a threshold parameter T=0.03 is preset, and the data segment formed by the first data point in the current data sequence is recorded as the first initial data segment, and the second data point in the current data sequence is recorded as the first data point to be added; if the absolute value of the difference between the mean value of the noise performance degree of all data points in the first initial data segment and the noise performance degree of the first data point to be added is greater than or equal to the preset threshold parameter T, then the data segment formed after the first data point to be added is classified into the first initial data segment is recorded as the first updated iterative data segment; the next data point of the first data point to be added, Recorded as the second data point to be added; if the absolute value of the difference between the mean value of the noise performance degree of all data points in the first updated iterative data segment and the noise performance degree of the second data point to be added is greater than or equal to the preset threshold parameter T, then the second data point to be added is classified into the data segment formed after the first updated iterative data segment, and recorded as the second updated iterative data segment; and so on, until the absolute value of the difference between the mean value of the noise performance degree of all data points in the latest updated iterative data segment and the noise performance degree of the latest data point to be added is less than the preset threshold parameter T, then the latest updated iterative data segment is recorded as the first segment; Record the latest data point to be added as the second initial data segment, and record the next data point of the latest data point to be added as the first data point to be added; if the absolute value of the difference between the mean value of the noise performance degree of all data points in the second initial data segment and the noise performance degree of the first data point to be added is greater than or equal to the preset threshold parameter T, then the first data point to be added is included in the second initial data segment after the data segment formed is recorded as the first updated iterative data segment; record the next data point of the first data point to be added as the second data point to be added; if the absolute value of the difference between the mean value of the noise performance degree of all data points in the first updated iterative data segment and the noise performance degree of the second data point to be added is greater than or equal to the preset threshold parameter T, then the second data point to be added is included in the data segment formed after the first updated iterative data segment is recorded as the second updated iterative data segment; and so on, until the absolute value of the difference between the mean value of the noise performance degree of all data points in the latest updated iterative data segment and the noise performance degree of the latest data point to be added is less than the preset threshold parameter T, then the latest updated iterative data segment is recorded as the second segment; In this way, the current data sequence is divided into several segments.

[0037] It should be noted that, in other embodiments, the value of the threshold parameter T can be set according to specific implementation conditions.

[0038] S4: Use the Vondrak filtering algorithm to filter the data in each segment to obtain a filtered current data sequence, obtain abnormal data based on the filtered current data sequence, and then judge the abnormal situation of the relay.

[0039] It should be noted that after the current data sequence is segmented, it is necessary to perform Vondrak filtering on the current data in each segment to make the filtering result more accurate. Since this algorithm filters several data at a time, when filtering the current data in each segment, it is necessary to obtain the current data in each segment and divide it into multiple arrays to obtain several arrays for each segment, and then obtain the smoothing factor of the first array in each segment. Since the noise performance in each segment is consistent, the smoothing factor of the first array in each segment can be used as the smoothing factor for the data behind each segment to reduce the amount of calculation of the algorithm.

[0040] In an embodiment of the present invention, the number of arrays m is preset, and according to the number of arrays m, the current data in each segment is divided into several arrays in turn. It should be noted that if the remaining current data in the segment does not meet m, the remaining current data will be used as an array; in an embodiment of the present invention, the preset number of arrays m=10, and in other embodiments, the implementer can preset the value of the number of arrays m according to the specific implementation method.

[0041] The smoothing error method is used to obtain the smoothing factor of the first array in each segment. According to the smoothing factor of the first array in each segment, each array in each segment is filtered using Vondrak filtering to obtain a filtered current data sequence.

[0042] The preset current fluctuation range is [20-40mA]. If the value of any current data in the filtered current data sequence is not within the current fluctuation range, the current data is abnormal data, and the relay at the sampling time corresponding to the current data is abnormal.

[0043] The embodiment of the present invention further discloses a relay balance anomaly detection system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a relay balance anomaly detection method according to the present invention is implemented.

[0044] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0045] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high bandwidth memory, a hybrid storage cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0046] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0047] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting a relay balance anomaly, characterized in that: Includes steps: Collect current data; obtain the distribution density of each extreme point in the local range of each current data , represents the distribution density of the jth extreme point in the local range of the i-th current data; represents the value of the jth extreme point in the local range of the i-th current data; as well as Respectively represent the values ​​of the previous extreme point and the next extreme point of the jth extreme point in the local range of the i-th current data; as well as Respectively represent the sampling time corresponding to the next extreme point and the previous extreme point of the jth extreme point in the local range of the i-th current data; max() represents the maximum value function; the variance of the distribution density of all extreme points in the local range of each current data is taken as the noise performance degree of each current data; According to the noise performance degree of each current data, the current data is divided into several segments; the data in each segment is filtered to obtain a filtered current data sequence; according to the filtered current data sequence, abnormal data is obtained; according to the abnormal data, the abnormal situation of the relay is judged.

2. A relay balance abnormality detection method according to claim 1, characterized in that: The step of obtaining each extreme point in a local range of each current data comprises: A local range of each current data is obtained, and an extreme value point in the local range of each current data is obtained using a peak detection algorithm.

3. A relay balance abnormality detection method according to claim 2, characterized in that: The obtaining of the local range of each current data includes: The number of sampling moments N is preset, and the current data at N sampling moments before the sampling moment corresponding to the i-th current data and the current data at N sampling moments after the sampling moment are used as the local range of the i-th current data.

4. A relay balance abnormality detection method according to claim 1, characterized in that: The current data is divided into several segments according to the noise performance level of each current data, including: A threshold parameter is preset to divide the current data sequence into several segments; wherein the absolute value of the difference between the mean value of the noise performance level of all data in each segment and the noise performance level of the first data in the next segment is less than the preset threshold parameter T.

5. A relay balance abnormality detection method according to claim 4, characterized in that: The filtering of the data in each segment to obtain a filtered current data sequence includes: The number of arrays m is preset. According to the number of arrays m, the current data in each segment is divided into several arrays in turn. The smoothing error method is used to obtain the smoothing factor of the first array in each segment. According to the smoothing factor of the first array in each segment, Vondrak filtering is used to filter each array in each segment to obtain a filtered current data sequence.

6. A relay balance abnormality detection method according to claim 1, characterized in that: The step of obtaining abnormal data according to the filtered current data sequence includes: The preset current fluctuation range is [20-40mA]. If the value of any current data in the filtered current data sequence is not within the current fluctuation range, the current data is abnormal data.

7. A relay balance abnormality detection method according to claim 1, characterized in that: The collecting current data comprises: The preset sampling time is 2 seconds / time, and the total sampling is one hour. A current sensor is installed on the relay, and each current data is collected during the operation of the relay.

8. A relay balance anomaly detection system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a relay balance abnormality detection method according to any one of claims 1 to 7 is implemented.

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