A method and system for detecting abnormal balance of a relay

Abstract

The present invention relates to the technical field of data processing, and particularly relates to a method and system for detecting abnormal balance of a relay. The method includes 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 performance degree of each current data based on the distribution density, dividing the current data sequence into several segments according to the noise performance degree of each current data, using the Vondrak filtering algorithm to filter the data in each segment to obtain a filtered current data sequence, obtaining abnormal data according to the filtered current data sequence, and then judging the abnormal situation of the relay. The present invention improves the filtering effect and further improves the accuracy of subsequent abnormal detection.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a method and system for detecting abnormal relay balance. Background Art

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

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

[0004] Since the current data collected during the operation 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. Then, when using the Vondrak filtering algorithm to denoise the current data, if the same size of smoothing factor is used to filter all the current data, it may not achieve a good filtering effect for all the current data. Or, frequently using the smoothing error method to determine the smoothing factor for each current data will result in an excessive amount of calculation, thus leading to low filtering efficiency, and further resulting in poor efficiency in detecting abnormal relay balance ultimately. Summary of the Invention

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

[0006] In the first aspect, the present invention provides a method for detecting abnormal relay balance, adopting the following technical solution:

[0007] A method for detecting abnormal relay balance includes steps:

[0008] 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 j-th extreme point in the local range of the i-th current data; represents the value of the j-th extreme point in the local range of the i-th current data; and respectively represent the values of the previous extreme point and the next extreme point of the j-th extreme point in the local range of the i-th current data; and respectively represent the sampling times corresponding to the next extreme point and the previous extreme point of the j-th extreme point in the local range of the i-th current data; max() represents the maximum value function; the variance of the distribution densities of all extreme points in the local range of each current data is used as the noise performance degree of each current data;

[0009] According to the noise performance degree of each current data, divide the current data into several segments; filter the data in each segment to obtain a filtered current data sequence; obtain abnormal data according to the filtered current data sequence; judge the abnormal situation of the relay according to the abnormal data.

[0010] The innovation of the present invention lies in being able to analyze the change characteristics of current data during the operation of the relay, obtain the noise performance degree of each current data, and then segment it according to the noise performance degree of each current data, so that the noise performance degree of the current data in each segment is the same, and the same smoothing factor is applied for filtering in different segments, which can significantly improve the accuracy and effect of Vondrak filtering.

[0011] Preferably, the obtaining of each extreme point in the local range of each current data includes:

[0012] Obtain the local range of each current data, and use the peak detection algorithm to obtain the extreme points in the local range of each current data.

[0013] Preferably, the obtaining of the local range of each current data includes:

[0014] Preset the number of sampling times N, and use the current data at N sampling times before the sampling time corresponding to the i-th current data and the current data at N sampling times after it as the local range of the i-th current data.

[0015] It is convenient to subsequently analyze the noise performance degree of each current data according to the data in the local range of each current data.

[0016] Preferably, dividing the current data into several segments according to the noise performance degree of each current data, including:

[0017] Preset a threshold parameter to divide the current data sequence into several segments; wherein, the absolute value of the difference between the mean value of the noise performance degrees of all data in each segment and the noise performance degree of the first data in its next segment is less than the preset threshold parameter T.

[0018] Dividing the current data according to the noise performance degree of each current data, so that the current data in each segment is subject to the same noise performance degree, which is convenient for subsequent separate filtering of each segment.

[0019] Preferably, filtering the data in each segment to obtain a filtered current data sequence, including:

[0020] Preset the number of arrays m. According to the number of arrays m, sequentially divide the current data in each segment into several arrays, obtain the smoothing factor of the first array in each segment using the smoothing error method, and use Vondrak filtering to filter each array in each segment according to the smoothing factor of the first array in each segment to obtain a filtered current data sequence.

[0021] The obtained filtered current data sequence more conforms to the fluctuation of the true current data and excludes the influence of noise data.

[0022] Preferably, obtaining abnormal data according to the filtered current data sequence, including:

[0023] Preset the current fluctuation range as [20 - 40 mA]. If the value of any current data in the filtered current data sequence is not within the current fluctuation range, this current data is abnormal data.

[0024] Improve the accuracy of abnormal data identification and avoid the influence of noise on abnormal data.

[0025] Preferably, collecting current data, including:

[0026] Preset the sampling time as 2 seconds / time, and collect for one hour in total. Install a current sensor on the relay and collect each current data during the operation of the relay.

[0027] In a second aspect, the present invention provides a relay balance abnormality detection system, adopting the following technical solution:

[0028] A relay balance abnormality detection system includes: a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned relay balance abnormality detection method is implemented.

[0029] By adopting the above technical solution, a computer program is generated from the above relay balance anomaly detection method and stored in a memory to be loaded and executed by a processor, so as to manufacture a terminal device according to the memory and the processor for convenient use.

[0030] The present invention has the following technical effects: The purpose of the present invention is to be able to analyze the change characteristics of current data during the operation of a relay, obtain the noise performance degree of each current data, and then segment it according to the noise performance degree of each current data, so that the noise performance degree of the current data in each segment is the same, and the same smoothing factor is applied for filtering in different segments, which can significantly improve the accuracy and effect of Vondrak filtering. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] By referring to the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understandable. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0032] Figure 1 is the flowchart of the method in an embodiment of a relay balance anomaly detection method of the present invention;

[0033] Figure 2 represents a schematic diagram of a horizontal rectangle formed by an extreme point and its adjacent extreme points before and after. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] It should be understood that when the claims, specifications, and drawings of the present invention use terms such as "first" and "second", they are only used to distinguish different objects and not to describe a specific order. The terms "including" and "comprising" used in the specifications 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 combinations.

[0036] An embodiment of the present invention discloses a relay balance anomaly detection method, referring to Figure 1 , including steps S1 - step S3:

[0037] S1: Collect current data.

[0038] It should be noted that first, it is necessary to obtain the current data during the operation of the relay, and judge the abnormal situation of the relay according to the abnormal situation of the current data.

[0039] In the implementation of the present invention, the preset sampling time is 2 seconds / time, and a total of one hour is collected. An 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 turn to obtain a current data sequence.

[0040] 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 based on the distribution density, obtain the noise performance degree of each current data.

[0041] It should be noted that since the current data collected during the operation of the relay will be affected by noise to varying degrees, that is, the noise performance degrees of the current data in different time periods will be different. Then, 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 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 lead to excessive computational complexity and thus low filtering efficiency, which will further lead to poor abnormal detection efficiency for the relay balance. Therefore, in this step, according to the change characteristics of the current data during the operation of the relay, the current data sequence is divided into several segments, so that the noise performance degrees of the current data in each segment are the same. Then, different smoothing factors are used for Vondrak filtering in different segments, which can effectively solve the problem of poor filtering effect caused by using the same smoothing factor, thus significantly improving the filtering accuracy and efficiency of the Vondrak filtering algorithm for the relay current data sequence.

[0042] It should be further noted that during the operation of the relay, the contact (inside the relay, the contact of the main circuit is affected by the electromagnetic suction or release force, and the mechanism that connects or disconnects it) may experience multiple bounces, which will cause noise in the current data. Such noise is manifested as random fluctuations within a local range. Therefore, the noise performance degree of the current data can be obtained by using the random volatility of the data within the local range of the current data.

[0043] If the extreme points in the local range of current data are distributed more regularly, it indicates lower random volatility, meaning the current data is more likely to be the fluctuation of normal data. Conversely, if the extreme points are distributed less regularly, it indicates higher random volatility, meaning 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 high, it indicates that the extreme points in the local range of current data are distributed more regularly, and the current data is normal data.

[0044] In the embodiment of the present invention, a preset number of sampling times N is set. The current data at N sampling times before the sampling time corresponding to the i-th current data and the current data at N sampling times after it are used as the local range of the i-th current data, and the peak detection algorithm is used to obtain the extreme points in the local range of the i-th current data.

[0045] Obtain the distribution density of the j-th extreme point in the local range of the i-th current data:

[0046] ;

[0047] In the formula, represents the distribution density of the j-th extreme point in the local range of the i-th current data; represents the value of the j-th extreme point in the local range of the i-th current data; represents the value of the previous extreme point of the j-th extreme point in the local range of the i-th current data; represents the value of the next extreme point of the j-th extreme point in the local range of the i-th current data; represents the sampling time corresponding to the next extreme point of the j-th extreme point in the local range of the i-th current data; represents the sampling time corresponding to the previous extreme point of the j-th extreme point in the local range of the i-th current data; max() represents the maximum value function; The value represents the reciprocal of the area of the horizontal rectangle formed by the j-th 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 j-th extreme point in the local range of the i-th current data. The length and width of the formed horizontal rectangle are the differences in data values and sampling times. See Figure 2 .

[0048] The variance of the distribution densities of all extreme points in the local range of the i-th current data is used as the noise performance degree of the i-th current data. The larger the value, the worse the consistency of the distribution density of extreme points, and at this time, the noise performance degree of the i-th current data is greater.

[0049] S3: Divide the current data sequence into several segments according to the noise performance degree of each current data.

[0050] It should be noted that after obtaining the noise performance degree of each current data in the current data sequence, the current data sequence can be segmented according to the noise performance degree of the current data, so that the noise influence on the current data in each segment is consistent, which is convenient for determining the smoothing factor of the subsequent Vondrak filtering algorithm.

[0051] In the embodiment of the present invention, a threshold parameter T = 0.03 is preset. 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 degrees 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 incorporating the first data point to be added into the first initial data segment is recorded as the first updated iteration data segment; the next data point of the first data point to be added is recorded as the second data point to be added; if the absolute value of the difference between the mean value of the noise performance degrees of all data points in the first updated iteration 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 data segment formed after incorporating the second data point to be added into the first updated iteration data segment is recorded as the second updated iteration data segment; and so on, until the absolute value of the difference between the mean value of the noise performance degrees of all data points in the latest updated iteration 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 iteration data segment is recorded as the first segment;

[0052] Denote the latest data point to be added as the second initial data segment, and denote 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 of the noise performance degrees 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 denote the data segment formed after incorporating the first data point to be added into the second initial data segment as the first updated iteration data segment. Denote 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 of the noise performance degrees of all data points in the first updated iteration 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 denote the data segment formed after incorporating the second data point to be added into the first updated iteration data segment as the second updated iteration data segment. And so on, until the absolute value of the difference between the mean of the noise performance degrees of all data points in the latest updated iteration data segment and the noise performance degree of the latest data point to be added is less than the preset threshold parameter T, then denote the latest updated iteration data segment as the second segment.

[0053] And so on, divide the current data sequence into several segments.

[0054] It should be noted that in other embodiments, the value of the threshold parameter T can be set according to specific implementation situations.

[0055] S4: Use the Vondrak filtering algorithm to filter the data in each segment to obtain a filtered current data sequence. According to the filtered current data sequence, obtain abnormal data, and then judge the abnormal situation of the relay.

[0056] 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 respectively 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 and then divide the current data in each segment into multiple arrays to obtain several arrays for each segment. Then obtain the smoothing factor of the first array in each segment. Since the noise performance degree is the same in each segment, the smoothing factor of the first array in each segment can be used as the smoothing factor for the subsequent data in each segment to reduce the computational complexity of the algorithm.

[0057] In the embodiment of the present invention, preset the number of arrays m. According to the number of arrays m, sequentially divide the current data in each segment into several arrays. It should be noted that if the remaining current data in the segment does not satisfy m, then regard the remaining current data as an array. In the embodiment of the present invention, preset the number of arrays m = 10. In other embodiments, the implementer can preset the value of the number of arrays m according to the specific implementation.

[0058] The smoothing factor of the first array in each segment is obtained by using the smoothing error method. According to the smoothing factor of the first array in each segment, each array in each segment is filtered by using Vondrak filtering to obtain a filtered current data sequence.

[0059] The preset current fluctuation range is [20 - 40 mA]. If the value of any current data in the filtered current data sequence is not within the current fluctuation range, this current data is abnormal data, and the relay at the sampling moment corresponding to this current data is abnormal.

[0060] An embodiment of the present invention also discloses a relay balance abnormality detection system, including a processor and a memory. 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 the present invention is implemented.

[0061] The above system further includes other components well known to those skilled in the art, such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described in detail here.

[0062] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or device. For example, a computer-readable storage medium can 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 program, a module, or both. Any such computer storage medium can be a part of the device or accessible or connectable to the device.

[0063] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted during the practice of the present invention.

[0064] The above are all preferred embodiments of the present invention. The protection scope of the present invention is not limited by this. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within 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; 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 degree of all data in each segment and the noise performance degree of the first data in the next segment is less than the preset threshold parameter T; the data in each segment is filtered to obtain a filtered current data sequence; and abnormal data is obtained according to the filtered current data sequence; 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 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.

5. 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.

6. 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.

7. 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 6 is implemented.

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