Abnormal value identification method for relay parameters

Abstract

The invention relates to the field of data processing, in particular to a relay parameter abnormal value identification method. The method comprises the following steps: acquiring historical normal two-dimensional parameters of a relay, clustering the historical normal two-dimensional parameters to obtain a plurality of parameter clusters, and differentiating the two-dimensional parameters in the parameter clusters to obtain a fluctuation cluster; real-time two-dimensional parameters of the relay are collected, the real-time two-dimensional parameters are real-time current and real-time voltage, a parameter cluster and a fluctuation cluster corresponding to the real-time two-dimensional parameters are determined according to time labels of the real-time two-dimensional parameters, and a first abnormal degree and a second abnormal degree are calculated; the overall abnormal degree Q of the relay is calculated, and when Q is larger than a threshold value, relay parameters are abnormal. The problem that the accuracy of abnormal value identification of relay parameters cannot be remarkably improved in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more specifically, to a method for identifying abnormal values ​​of relay parameters. Background Art

[0002] The research background of the relay parameter outlier identification method based on big data is mainly due to the high requirements of the power system for safety and reliability. With the development of smart grids and the Internet of Things, a large amount of operating data is generated in the application of relays in protection and control. By analyzing a large amount of relay data, the working status of the relay can be monitored in real time, and outliers can be identified in time, so as to warn potential faults in advance, reduce downtime and economic losses. At the same time, this method can also optimize maintenance strategies, improve the efficiency and life of relays, and promote the development of intelligent operation and maintenance. Through in-depth research, the safety and stability of the power system can be further improved.

[0003] At present, a patent application document with publication number CN117454292A discloses a method and device for identifying abnormal values ​​of relay parameters. The patent application document calculates the correlation matrix based on the dissimilarity matrix formed by the distance between the parameters to determine the correlation between the parameters. And by normalizing the correlation matrix to obtain the correlation probability matrix, the abnormal probability of each parameter is calculated according to the correlation between the parameters, making full use of the correlation between the parameters. Avoid false detection and improve the accuracy of abnormal value identification. The solution in the above patent application document only considers the correlation between the relay parameters and avoids false detection, but it is easy to cause missed detection, and its detection method is relatively simple, which cannot significantly improve the accuracy of abnormal value identification of relay parameters. Summary of the invention

[0004] In order to solve the problem in the prior art that the accuracy of abnormal value identification of relay parameters cannot be significantly improved, the present invention provides solutions in the following aspects.

[0005] The present invention provides a method for identifying abnormal values ​​of relay parameters, comprising: Collect the historical normal two-dimensional parameters of the relay, cluster the historical normal two-dimensional parameters to obtain multiple parameter clusters, differentiate the two-dimensional parameters in the parameter cluster to obtain a fluctuation cluster, the historical normal two-dimensional parameters are the historical normal current and the historical normal voltage, and the fluctuation cluster includes current fluctuation and voltage fluctuation; collect the real-time two-dimensional parameters of the relay, the real-time two-dimensional parameters are the real-time current and the real-time voltage, determine the corresponding parameter cluster and fluctuation cluster according to the time label of the real-time two-dimensional parameters, and calculate the first abnormality degree and the second degree of abnormality , ,in, Represents the real-time current in real-time two-dimensional parameters, Represents the current mean value in the parameter cluster corresponding to the real-time two-dimensional parameter, represents the current variance in the parameter cluster corresponding to the real-time two-dimensional parameter, Represents the real-time voltage in real-time two-dimensional parameters, Represents the voltage mean value in the parameter cluster corresponding to the real-time two-dimensional parameter, Represents the voltage variance in the parameter cluster corresponding to the real-time two-dimensional parameter, It represents the difference between the real-time current in the real-time two-dimensional parameters and the current collected at the previous moment. represents the mean current fluctuation in the corresponding fluctuation cluster in the real-time two-dimensional parameter, represents the current fluctuation variance in the corresponding fluctuation cluster in the real-time two-dimensional parameter, It represents the difference between the real-time voltage in the real-time two-dimensional parameters and the voltage collected at the previous moment. Represents the mean voltage fluctuation in the fluctuation cluster corresponding to the real-time two-dimensional parameter, Represents the voltage fluctuation variance in the fluctuation cluster corresponding to the real-time two-dimensional parameter; calculates the overall abnormality of the relay ,when When the value is greater than the threshold, the relay parameters are abnormal, among which, , Indicates the degree of parameter confusion, Indicates the degree of fluctuation disorder. Indicates the first abnormality level, Indicates the second abnormality degree; the parameter chaos degree and the fluctuation chaos degree respectively characterize the distribution of the parameter cluster and the fluctuation cluster corresponding to the real-time two-dimensional parameters.

[0006] This method identifies normal parameter clusters and fluctuation clusters by clustering the historical normal two-dimensional parameters of the relay, and calculates two abnormality degree indicators based on the changes in the real-time two-dimensional parameters (current and voltage), so as to monitor whether the relay is abnormal in real time. Multiple parameter clusters are formed by clustering the two-dimensional parameters of historical normal current and voltage. These clusters represent normal current and voltage patterns under different working conditions. By differentiating the two-dimensional parameters in the parameter cluster, a fluctuation cluster is obtained, which can reflect the volatility of current and voltage at different time points. The current and voltage of the relay are collected in real time, and by comparing them with the historical parameter cluster and fluctuation cluster, it can be determined whether the real-time parameters fall within the normal range and whether the real-time fluctuation is normal. When calculating the first abnormality degree and the second degree of abnormality By comparing the real-time current and voltage with the mean and variance in the parameter cluster, the fluctuation of the relay outside the normal range can be more accurately identified. If the real-time data deviates far from the mean of the cluster, it indicates that there may be a risk of relay abnormality or failure. By comparing the difference between the real-time current and voltage and the data of the previous moment, combined with the mean and variance in the fluctuation cluster, the sudden change or abnormal fluctuation of the relay can be effectively detected. This can timely discover the sudden change of the relay state, especially those short-term fluctuations that may cause failures. Real-time calculation and monitoring of the first and second abnormality levels can help operation and maintenance personnel quickly locate the cause of the relay abnormality. For example, if the abnormality of the current is high and the voltage change is small, it may be a fault caused by uneven current load; if the voltage fluctuation is abnormal, it may be a power supply problem. Combined with the automatic calculation of the first and second abnormality levels, the intelligent monitoring of the relay can be realized, making the monitoring and analysis of the relay operation status more efficient and accurate. The advantage of this method is that through comprehensive monitoring of the working status of the relay, combined with cluster analysis and fluctuation analysis, it can not only accurately determine whether the relay is abnormal, but also give early warning, thereby reducing the probability of failure and ensuring the reliability and stability of the relay.

[0007] Preferably, after collecting the historical normal two-dimensional parameters of the relay, the method includes: performing missing completion processing on the historical normal two-dimensional parameters.

[0008] By completing the missing data, the integrity of the historical normal two-dimensional parameter data is ensured, avoiding erroneous clustering analysis or subsequent calculation deviations caused by incomplete data.

[0009] Preferably, the historical normal two-dimensional parameters are clustered to obtain a plurality of parameter clusters, including: clustering using an ordered sample clustering method.

[0010] The ordered sample clustering method can take into account the time sequence in the data, which is very important for the historical normal two-dimensional parameters of the relay (such as current and voltage). The working state of the relay is usually continuous in time. Ignoring this may lead to inaccurate clustering results, which in turn affects the effect of subsequent anomaly detection. The ordered sample clustering method effectively improves the temporal sequence of the clustering results by taking time series data as a key factor, so that the clustered parameter clusters can better reflect the actual operating state of the relay.

[0011] Preferably, the parameter disorder degree and the fluctuation disorder degree are specifically: the sum of squares of intra-cluster errors of the parameter cluster and the sum of squares of intra-cluster errors of the fluctuation cluster.

[0012] Preferably, after calculating the overall abnormality degree of the relay, the method includes: when the overall abnormality degree is less than a threshold value, dividing the real-time two-dimensional parameters into historical normal two-dimensional parameters, and dynamically updating the historical normal two-dimensional parameters.

[0013] Dynamically updating historical parameters can adjust the normal operating range in real time according to the latest operating status of the relay. When the relay is in normal working condition for a period of time and the abnormality is less than the threshold, the real-time data can become the new "normal" data. This dynamic update enables the system to better adapt to possible long-term changes in the relay and improve the adaptability and flexibility of the model.

[0014] Preferably, the When the relay parameter is abnormal when it is greater than the threshold, it also includes: abnormal warning through the color of the fault prompt light and / or the sound of the fault prompt bell.

[0015] The beneficial effect of the present invention is that multiple parameter clusters are formed by clustering the two-dimensional parameters of historical normal current and voltage. These clusters represent normal current and voltage patterns under different working conditions. By differentiating the two-dimensional parameters in the parameter cluster, a fluctuation cluster is obtained, which can reflect the volatility of current and voltage at different time points. The current and voltage of the relay are collected in real time, and by comparing with the historical parameter cluster and the fluctuation cluster, it can be determined whether the real-time parameters fall within the normal range and whether the real-time fluctuation is normal. The advantage of this method is that by comprehensively monitoring the working state of the relay, combined with cluster analysis and fluctuation analysis, it is possible to accurately determine whether the relay is abnormal and to provide early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By reading 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 understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a flow chart of a method for identifying abnormal values ​​of relay parameters provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0019] Figure 1 1 is a flow chart of a method for identifying abnormal values ​​of relay parameters according to an embodiment of the present invention, comprising the following steps: S101, collecting historical normal two-dimensional parameters of the relay, clustering the historical normal two-dimensional parameters to obtain multiple parameter clusters, and differentiating the two-dimensional parameters in the parameter clusters to obtain fluctuation clusters.

[0020] The historical normal two-dimensional parameters are the historical normal current and the historical normal voltage, and the fluctuation cluster includes the current fluctuation and the voltage fluctuation.

[0021] In one embodiment, during the operation of the relay, the power demand is different at different stages of the day, so the parameter abnormalities at different stages are also different. For example, normal parameters at peak power consumption are abnormal parameters at low power consumption. If the same standard is used to define abnormalities for parameter values ​​at all times, the detection accuracy of abnormalities is low. Therefore, ordered sample clustering can be used for the historical normal two-dimensional parameters of the relay. The ordered sample clustering method effectively improves the temporal nature of the clustering results by taking time series data as a key factor.

[0022] The collecting of the historical normal two-dimensional parameters of the relay includes: collecting the historical normal two-dimensional parameters and the real-time two-dimensional parameters through a current sensor and a voltage sensor.

[0023] For example, the current sensor and voltage sensor collect relay parameters every ten seconds. The current and voltage collected in the past ten days are all normal parameters. All current and voltage data of these ten days are used as historical normal two-dimensional parameters. Then, according to the ordered sample clustering algorithm, the historical normal two-dimensional parameters are clustered. After clustering, these data can be divided into multiple cluster clusters according to time tags. For example, 0 o'clock to 8 o'clock, 11 o'clock to 1 o'clock, 17 o'clock to 19 o'clock, and 22 o'clock to 24 o'clock are a parameter cluster, called a peak cluster, and other times are another parameter cluster, a low peak cluster. Of course, the ordered sample clustering algorithm can also be clustered into multiple other clusters, which will not be repeated here.

[0024] After obtaining the parameter cluster, a fluctuation cluster can be obtained by differentiating the parameters in the parameter cluster in time sequence. For example, a parameter cluster has five two-dimensional parameters, namely (3A, 5V), (3.2A, 5.1V), (3.0A, 5.1V), (3.3A, 5.2V) and (2.9A, 5.1V), and the corresponding time labels are 15:10:00, 15:10:10, 15:10:20, 15:10:30 and 15:10:40. Then, after differentiating in time sequence, four fluctuation arrays can be obtained, namely (0.2A, 0.1V), (-0.2A, 0V), (0.3A, 0.1V) and (-0.4A, -0.1V). The first data in the fluctuation array is the current fluctuation, and the second data is the voltage fluctuation. The four fluctuation arrays together constitute the fluctuation cluster corresponding to the parameter cluster.

[0025] During the process of collecting current and voltage, the sensor may be affected by the external environment and occasionally fail to collect current and voltage, resulting in missing values. Therefore, after collecting the historical normal two-dimensional parameters of the relay, it is necessary to perform missing completion processing on the historical normal two-dimensional parameters, which can generally be performed by interpolation completion.

[0026] S102, collecting real-time two-dimensional parameters of the relay, the real-time two-dimensional parameters are real-time current and real-time voltage, determining the corresponding parameter cluster and fluctuation cluster according to the time tags of the real-time two-dimensional parameters, and calculating the first abnormality degree and the second abnormality degree.

[0027] In some embodiments, the first abnormality level is calculated and the second degree of abnormality , specifically: ,in, Represents the real-time current in real-time two-dimensional parameters, Represents the current mean value in the parameter cluster corresponding to the real-time two-dimensional parameter, represents the current variance in the parameter cluster corresponding to the real-time two-dimensional parameter, Represents the real-time voltage in real-time two-dimensional parameters, Represents the voltage mean value in the parameter cluster corresponding to the real-time two-dimensional parameter, Represents the voltage variance in the parameter cluster corresponding to the real-time two-dimensional parameter, It represents the difference between the real-time current in the real-time two-dimensional parameters and the current collected at the previous moment. represents the mean current fluctuation in the corresponding fluctuation cluster in the real-time two-dimensional parameter, represents the current fluctuation variance in the corresponding fluctuation cluster in the real-time two-dimensional parameter, It represents the difference between the real-time voltage in the real-time two-dimensional parameters and the voltage collected at the previous moment. Represents the mean voltage fluctuation in the fluctuation cluster corresponding to the real-time two-dimensional parameter, Represents the voltage fluctuation variance in the fluctuation cluster corresponding to the real-time two-dimensional parameter.

[0028] For example, in the parameter cluster obtained in S101, 0 o'clock to 8 o'clock is a peak cluster, and the time label of the real-time two-dimensional parameter is 06:25:20, which is within 0 o'clock to 8 o'clock, so it corresponds to the peak cluster from 0 o'clock to 8 o'clock. When calculating the first abnormality degree of the real-time two-dimensional parameter, the current mean, current variance, voltage mean and voltage variance in the peak cluster are used for calculation. The two-dimensional parameters in the peak cluster are differentiated according to the time sequence to obtain the corresponding peak fluctuation cluster. When calculating the second abnormality degree of the real-time two-dimensional parameter, the current fluctuation mean, current fluctuation variance, voltage fluctuation mean and voltage fluctuation variance in the peak fluctuation cluster are used for calculation.

[0029] S103, calculating the overall abnormality degree Q of the relay, when Q is greater than a threshold value, the relay parameters are abnormal.

[0030] When actually calculating the overall abnormality degree Q, in general, the sum of the relay parameter chaos degree and the parameter fluctuation chaos degree is constant. Often they have a greater weight, and because differential calculations will amplify the volatility of the data, the volatility of the volatility cluster will be more chaotic. Often more chaotic than the parameters of the parameter cluster So in some embodiments, the overall abnormality , Indicates the degree of parameter confusion, Indicates the degree of fluctuation disorder. Indicates the first abnormality level, Indicates the second abnormality degree; the parameter chaos degree and the fluctuation chaos degree respectively characterize the distribution of the parameter cluster and the fluctuation cluster corresponding to the real-time two-dimensional parameters.

[0031] The parameter disorder degree and the fluctuation disorder degree are specifically: the intra-cluster error sum of the parameter cluster and the intra-cluster error sum of the fluctuation cluster. It should be noted that calculating the intra-cluster error sum of the cluster cluster belongs to the known technology and will not be repeated here.

[0032] In addition, in order to dynamically update the historical normal two-dimensional parameters and improve adaptability and flexibility, after calculating the overall abnormality degree of the relay, it also includes: when the overall abnormality degree is less than a threshold, the real-time two-dimensional parameters are divided into the historical normal two-dimensional parameters, and the historical normal two-dimensional parameters are dynamically updated.

[0033] When the relay parameters are abnormal, in order to quickly convey the abnormal state of the relay, in some embodiments, when the overall abnormality When the relay parameter is abnormal when it is greater than the threshold, it also includes: abnormal warning through the color of the fault prompt light and / or the sound of the fault prompt bell.

[0034] The present invention forms multiple parameter clusters by clustering analysis on the two-dimensional parameters of historical normal current and voltage. These clusters represent normal current and voltage patterns under different working conditions. By differentiating the two-dimensional parameters in the parameter cluster, a fluctuation cluster is obtained, which can reflect the volatility of current and voltage at different time points. The current and voltage of the relay are collected in real time, and by comparing with the historical parameter cluster and the fluctuation cluster, it can be determined whether the real-time parameters fall within the normal range and whether the real-time fluctuation is normal. The advantage of this method is that through comprehensive monitoring of the working state of the relay, combined with cluster analysis and fluctuation analysis, it can accurately determine whether the relay is abnormal, and can also provide early warning, significantly improving the accuracy of abnormal value identification of relay parameters.

[0035] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0036] 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.

Claims

1. A method for identifying abnormal values ​​of relay parameters, characterized in that: include: Collecting historical normal two-dimensional parameters of the relay, clustering the historical normal two-dimensional parameters to obtain multiple parameter clusters, and differentiating the two-dimensional parameters in the parameter clusters to obtain a fluctuation cluster, wherein the historical normal two-dimensional parameters are historical normal current and historical normal voltage, and the fluctuation cluster includes current fluctuation and voltage fluctuation; Collect the real-time two-dimensional parameters of the relay, which are the real-time current and real-time voltage, determine the corresponding parameter cluster and fluctuation cluster according to the time label of the real-time two-dimensional parameters, and calculate the first abnormality degree and the second degree of abnormality , ,in, Represents the real-time current in real-time two-dimensional parameters, Represents the current mean value in the parameter cluster corresponding to the real-time two-dimensional parameter, represents the current variance in the parameter cluster corresponding to the real-time two-dimensional parameter, Represents the real-time voltage in real-time two-dimensional parameters, Represents the voltage mean value in the parameter cluster corresponding to the real-time two-dimensional parameter, Represents the voltage variance in the parameter cluster corresponding to the real-time two-dimensional parameter, It represents the difference between the real-time current in the real-time two-dimensional parameters and the current collected at the previous moment. represents the mean current fluctuation in the corresponding fluctuation cluster in the real-time two-dimensional parameter, represents the current fluctuation variance in the corresponding fluctuation cluster in the real-time two-dimensional parameter, It represents the difference between the real-time voltage in the real-time two-dimensional parameters and the voltage collected at the previous moment. Represents the mean voltage fluctuation in the fluctuation cluster corresponding to the real-time two-dimensional parameter, Represents the voltage fluctuation variance in the corresponding fluctuation cluster in the real-time two-dimensional parameter; Calculate the overall abnormality of the relay ,when When the value is greater than the threshold, the relay parameters are abnormal, among which, , Indicates the degree of parameter confusion, Indicates the degree of fluctuation disorder. Indicates the first abnormality level, Indicates the second abnormality degree; the parameter chaos degree and the fluctuation chaos degree respectively characterize the distribution of the parameter cluster and the fluctuation cluster corresponding to the real-time two-dimensional parameters.

2. The method for identifying abnormal values ​​of relay parameters according to claim 1, characterized in that: After collecting the historical normal two-dimensional parameters of the relay, the method includes: performing missing completion processing on the historical normal two-dimensional parameters.

3. The method for identifying abnormal values ​​of relay parameters according to claim 1, characterized in that: After clustering the historical normal two-dimensional parameters, a plurality of parameter clusters are obtained, including: clustering by using an ordered sample clustering method.

4. The method for identifying abnormal values ​​of relay parameters according to claim 1, characterized in that: The parameter disorder degree and the fluctuation disorder degree are specifically: the intra-cluster error sum of the parameter cluster and the intra-cluster error sum of the fluctuation cluster.

5. The method for identifying abnormal values ​​of relay parameters according to claim 1, characterized in that: The collecting of the historical normal two-dimensional parameters of the relay includes: collecting the historical normal two-dimensional parameters and the real-time two-dimensional parameters through a current sensor and a voltage sensor.

6. The method for identifying abnormal values ​​of relay parameters according to claim 1, characterized in that: After calculating the overall abnormality degree of the relay, the method includes: when the overall abnormality degree is less than a threshold value, dividing the real-time two-dimensional parameters into historical normal two-dimensional parameters, and dynamically updating the historical normal two-dimensional parameters.

7. The method for identifying abnormal values ​​of relay parameters according to claim 1, characterized in that: When When the relay parameter is abnormal when it is greater than the threshold, it also includes: abnormal warning through the color of the fault prompt light and / or the sound of the fault prompt bell.

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