Intelligent detection method for operation state of electrical equipment

Abstract

The invention provides an intelligent detection method for the running state of electrical equipment. The method is applied to the field of data detection. The method comprises the following steps: acquiring multi-dimensional monitoring data of the mixed gas circuit breaker; determining the change collaboration degree of the monitoring data in each dimension based on the monitoring data of the multiple dimensions; determining the abnormal performance possibility of each data point in the monitoring data based on the change cooperation degree and the occurrence probability of the monitoring data; determining an abnormal data point in the monitoring data based on the abnormal performance possibility; and judging the running state of the mixed gas circuit breaker based on the abnormal data points. According to the method, the redundancy of the algorithm can be reduced, the accuracy of abnormal data identification can be improved, and the accuracy of the operation state detection of the mixed gas circuit breaker can also be improved.

Description

Technical Field

[0001] This application relates to the field of data detection, and more specifically, to an intelligent detection method for the operating state of electrical equipment. Background Art

[0002] Common electrical equipment includes generators, transformers, power lines, and circuit breakers, etc., which are used to make the power system operate normally and transmit power normally in the power system. According to different functions, structures, production materials, etc., the usage environment requirements of different types of electrical equipment are different. The usage environment includes low-pressure environment, low-temperature environment, dedicated power supply, etc. For example, low-temperature-resistant cables, hybrid gas circuit breakers, etc. can be used in low-pressure environments and low-temperature environments due to materials or protective sleeves.

[0003] During the operation of electrical equipment, monitoring software is usually used to monitor and display environmental parameters. When the parameters are abnormal, audible and visual alarm information is issued to prompt the source of the abnormal information, and hierarchical protection measures can be taken if necessary.

[0004] In the process of identifying abnormal data based on the Markov clustering algorithm, since the Markov algorithm itself needs to perform iterative operations to stretch the differences between different data points, but the transition probabilities between some abnormal data and normal data are small, some overlapping clusters will be lost after iteration, which will interfere with abnormal identification. At the same time, the abnormal changes of data are random, and when any dimension data has abnormal changes, it is very likely to drive other dimension data, including the operating state of the electrical equipment itself, to change. Therefore, it is impossible to accurately judge the transition relationship between each data and accurately judge the operating state of the electrical equipment only based on the frequency. Summary of the Invention

[0005] This application provides an intelligent detection method for the operating state of electrical equipment. By analyzing the abnormal possibility of each data point, the abnormal data point can be quickly determined, and the operating state of the hybrid gas circuit breaker can be judged through the abnormal data point. In this way, it can not only reduce the redundancy of the algorithm, improve the accuracy of abnormal data identification, but also improve the accuracy of judging the operating state of the hybrid gas circuit breaker.

[0006] An embodiment of this application provides an intelligent detection method for the operating state of electrical equipment, and the method includes: Obtain monitoring data of multiple dimensions of the circuit breaker through sensors; For the first dimension among the multiple dimensions, construct a data window that matches the target data point at any moment in the first dimension; wherein, the first dimension is any one of the multiple dimensions; based on the first data point set of the monitoring data in the first dimension and the second data point sets respectively corresponding to the monitoring data in each second dimension, determine the correlation of co-variation generated by the first dimension; wherein, the second dimension is any one of the multiple dimensions and the state indication data, and the second dimension is different from the first dimension; based on the target data point and the first data point set, determine the possibility coefficient that the target data point is an outlier; use the possibility coefficient to adjust the correlation of co-variation generated by the first dimension to obtain the co-variation degree of change of the first dimension; Based on the co-variation degree of change and the occurrence probability of the monitoring data, determine the possibility of abnormal performance of each data point in the monitoring data; Based on the possibility of abnormal performance, determine the abnormal data points in the monitoring data; Based on the abnormal data points, detect the operating state of the circuit breaker.

[0007] Preferably, the determining the correlation of co-variation generated by the first dimension based on the first data point set of the monitoring data in the first dimension and the second data point sets respectively corresponding to the monitoring data in each second dimension includes: Calculate the covariance between the first data point set and the second data point sets; Take the sum of the distribution variances of the first data point set and the second data point sets as the denominator; Take the cumulative result of the ratio of the covariance to the denominator over all second dimensions as the correlation of co-variation generated by the first dimension.

[0008] Preferably, the determining the possibility coefficient that the target data point is an outlier based on the target data point and the first data point set includes: Take the ratio of the mean value of the data window where the target data point is located to the maximum data value of the first data point set as the possibility coefficient.

[0009] Preferably, the determining the possibility of abnormal performance of each data point in the monitoring data based on the co-variation degree of change and the occurrence probability of the monitoring data includes: Based on the co-variation degree of change and the occurrence probability of the monitoring data, determine the transition probability between any two data points in the same dimension of the monitoring data; Based on the transition probability between any two data points in the same dimension, determine the possibility of abnormal performance of each data point in the monitoring data.

[0010] Preferably, determining the transition probability between any two data points in the same dimension of the monitoring data based on the change coordination degree and the occurrence probability of the monitoring data includes: In the data window to which the first moment of the first dimension belongs and the data window to which the second moment of the first dimension belongs, respectively determine the data points in the same order; wherein, the first moment and the second moment are any two moments; Based on the data points in the same order and the corresponding change coordination degree, determine the local similarity degree between the data points in the same order; Based on the occurrence probability of the monitoring data and the local similarity degree, determine the transition probability between the first data point and the second data point; wherein, the first data point is the data value of the monitoring data at the first moment of the first dimension, and the second data point is the data value of the monitoring data at the second moment of the first dimension.

[0011] Preferably, determining the local similarity degree between the data points in the same order includes: Calculate the difference between the data points in the same order in the data windows where the two moments are located as the first difference; Calculate the difference between the change coordination degrees of the data points in the same order in the data windows where the two moments are located as the second difference; Take the cumulative result of the reciprocal of the absolute value of the product of the first difference and the second difference on the data window as the local similarity degree.

[0012] Preferably, determining the transition probability between any two data points in the same dimension of the monitoring data based on the occurrence probability of the monitoring data and the local similarity degree includes: Obtain the probability of the second data point appearing in the monitoring data; Determine the distance between the first moment and the second moment; Calculate the ratio of the probability to the distance, and take the product of the ratio and the local similarity as the transition probability between the first data point and the second data point.

[0013] Preferably, determining the possibility of abnormal performance of each data point in the monitoring data based on the transition probability between any two data points in the same dimension includes: Based on the transition probability between any two data points in the same dimension, construct a transition matrix; Determine the mean square error between the elements in the same order in the current column and any row of the transition matrix; Determine the mean value of the mean square error based on the mean square error between the current column and the elements in each row at the same order. Normalize the difference between the mean square error and the mean value to obtain the possibility of abnormal performance of the data points corresponding to the current column.

[0014] Preferably, the determining abnormal data points in the monitoring data based on the possibility of abnormal performance includes: In the monitoring data, the data points corresponding to the columns with the possibility of abnormal performance greater than the set threshold are abnormal data points; the data points corresponding to the columns with the possibility of abnormal performance less than or equal to the set threshold are not abnormal data points. Generate and output the operation status detection result and the warning information based on the abnormal data points.

[0015] The beneficial effects of this application are as follows: In this solution, after obtaining the monitoring data of multiple dimensions of the hybrid gas circuit breaker, the degree of change coordination of the monitoring data in multiple dimensions is analyzed by combining the state indication data; in this way, it is possible to avoid misjudging the normal data change as abnormal due to only analyzing the change of a single dimension data to identify the abnormal degree. Then, by combining the degree of change coordination with the occurrence probability of the monitoring data, the possibility of abnormal performance of each data point is analyzed. In this way, it is possible to avoid the randomness of abnormal changes resulting in the inability to accurately judge the transfer relationship between each data, thus making the subsequent detection of abnormal data more accurate. Finally, through the possibility of abnormal performance of each data point, the abnormal data points in the monitoring data can be quickly determined, so as to detect the operation status of the hybrid gas circuit breaker according to the abnormal data; in this way, it is possible to reduce the redundancy of the algorithm, improve the accuracy of abnormal data recognition, and improve the accuracy of supporting the judgment of the operation status of the hybrid gas circuit breaker. Description of the Drawings

[0016] Figure 1 It is a schematic diagram of the implementation process of an intelligent detection method for the operation status of electrical equipment provided by an embodiment of this application. Detailed Description of the Embodiment

[0017] Next, the technical solutions in this application will be clearly and elaborately described with reference to the drawings. Among them, in the description of the embodiments of this application, unless otherwise specified, " / " means "or". For example, A / B can mean A or B: "and / or" in the text is just a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "a plurality of" means two or more than two.

[0018] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0019] The technical solutions provided by the embodiments of the present application will be introduced below. The embodiments of the present application provide an intelligent detection method for the operating state of electrical equipment. Refer to Figure 1 , Figure 1 which is a schematic diagram of the implementation process of an intelligent detection method for the operating state of electrical equipment provided by the embodiments of the present application. The method includes: 101. Obtain monitoring data of multiple dimensions of the hybrid gas circuit breaker.

[0020] Here, the electrical equipment may be a dedicated circuit breaker, a low-temperature-resistant cable, or a hybrid gas circuit breaker. In this embodiment, taking the hybrid gas circuit breaker as an example, monitoring data of multiple dimensions are obtained through a variety of sensors, and the sensors include but are not limited to temperature and humidity sensors, voltage sensors, current sensors, and density sensors; the monitoring data of multiple dimensions include but are not limited to temperature, humidity, voltage, current, and gas density.

[0021] The hybrid gas circuit breaker collects the above various types of relevant data through corresponding sensors, records the relevant historical data within 24 hours, and stores the monitoring data of multiple dimensions collected in a computer or a host computer. In other embodiments, it can also be uploaded and stored in a cloud server.

[0022] 102. Determine the change coordination degree of the monitoring data in multiple dimensions based on the change response trends of the data under different dimensions.

[0023] Here, the change coordination degree is used to represent the change relationship between the change of the monitoring data of one dimension and the data of other dimensions. Through the monitoring data of multiple dimensions, the change coordination degree of the data points at any moment in each dimension is determined.

[0024] Taking any one dimension of the hybrid gas circuit breaker as a reference dimension and the remaining dimensions as control dimensions, analyze the change response trends between the monitoring data of each dimension in the control dimensions and other control dimensions as well as the reference dimension, so as to reflect the change coordination degree of the monitoring data of each dimension through this change response trend.

[0025] Since the hybrid gas circuit breaker has extremely high requirements for the operating environment, any abnormal change in any dimension data may affect the working efficiency and safety of the hybrid gas circuit breaker to varying degrees. And the data fluctuations generated by the monitoring data of any dimension are to a certain extent normal fluctuations. Therefore, analyzing only the changes in a single dimension data to identify the degree of abnormality will misjudge normal data changes as abnormalities and reduce the accuracy of anomaly detection.

[0026] In the embodiment of the present application, by monitoring the degree of change coordination of data in multiple dimensions, the problem of misjudging normal data changes as abnormalities due to only analyzing the changes in a single dimension data to identify the degree of abnormality is avoided.

[0027] In some embodiments, by analyzing the co-variation relationship of the corresponding data between multiple dimensions, combined with the local and global values of the data points themselves in the dimension, the degree of change coordination of any data point within the dimension is determined.

[0028] Specifically, when abnormal changes occur in the monitoring data of any dimension, it is very likely to drive the monitoring data of other dimensions, including the operating state of the hybrid gas circuit breaker itself, to change. Therefore, by analyzing the change relationship between the monitoring data changes of any dimension and the data of other dimensions, the possibility of abnormal changes in the monitoring data of any dimension can be judged.

[0029] Among them, any data point at any moment in the reference dimension is used as the target data point. The data window matching the target data point can be constructed by taking the target data point as the center and a preset time length as the radius to construct a data window matching the target data point; among them, the preset time length can be a custom-set time period; in this way, by constructing a corresponding data window for the target data point, it is convenient to analyze the local prominence degree of the target data point in the data window.

[0030] By analyzing the data change response trends between the data in the current dimension and other control dimensions and reference dimensions, the degree of change coordination of the data at any moment in any dimension is obtained as shown in formula (1): (1); Among them, x and y respectively represent any two dimensions, respectively represent the data point sets corresponding to the dimensions (for example, the first data point set and the second data point set); xt represents a moment t in dimension x; represents the mean value of the monitored data values within the data window where the moment t is located in dimension x, represents the maximum value of the data in the entire dimension; N represents the total number of dimensions.

[0031] cov() represents the covariance operation, representing the covariance of the data point sets corresponding to any two dimensions x and y in the corresponding dimensions. representing the data point sets corresponding to two dimensions x and y and the sum value of the variances.

[0032] Among them, the larger the value, the higher the degree of change coordination at the current moment t, the greater the possibility of abnormal data change, and the greater the value of the possibility coefficient ; while the covariance and variance distribution reflect the correlation of the data distributions between two data point sets. The larger the covariance, the stronger the positive correlation between the data in the two data point sets, and the smaller the variance, the more concentrated the distribution of elements in the two data point sets, and the greater the correlation of the data. In this way, by analyzing the co-variation relationship of the corresponding data between dimensions and combining the numerical sizes of the data points themselves and globally in the dimensions, the accuracy of anomaly detection can be improved.

[0033] 103. Based on the degree of change coordination and the occurrence probability of the monitoring data, determine the possibility of abnormal performance of each data point in the monitoring data.

[0034] Here, after obtaining the degree of change coordination of the data points of any dimension at any moment, combined with the occurrence probability of this data, analyze the possibility that each data point is an abnormal data, that is, the possibility of abnormal performance of each data point. Among them, the occurrence probability of the monitoring data can be obtained through the number of times any data point appears in the overall monitoring data and the time period corresponding to the monitoring data.

[0035] In some possible implementation manners, determine the transition probability between different data points of the same dimension through the degree of change coordination and the occurrence frequency of the monitoring data. Then, through the transition probability between different data points of the same dimension, obtain the possibility of abnormal performance of each data point, so as to reduce the inability to accurately judge the transition relationship between data points caused by the randomness of abnormal changes, thereby improving the accuracy of subsequent abnormal data monitoring.

[0036] Here, the occurrence probability of the monitoring data includes the occurrence frequencies of the data points at different moments in the same dimension. For example, for different moments t and r in dimension x, obtain the occurrence probability of the data point at moment t in the monitoring data and the occurrence probability of the data point at moment r in the monitoring data. After obtaining the degree of change coordination of the data points of any moment in dimension x, analyze the local similarity degree between the data points in the same order through the degree of change coordination of each data point within the window where the first moment is located. By combining the occurrence probability of the monitoring data with this local similarity degree, the transition probability between any two data points of the same dimension can be accurately obtained.

[0037] In some possible implementation manners, after obtaining the change coordination degree of any data point, in the process of using the Markov algorithm to perform anomaly detection on the data points of any dimension, it is necessary to construct a Markov chain and obtain the transition probabilities between various data points. The transition probability represents the similarity relationship between any two data points, and according to the algorithm principle, the difference between relatively similar data points is amplified to identify the abnormal components in the data. However, in the original algorithm, when constructing the Markov chain, it is obtained according to the frequency of occurrence of each pair of adjacent states (different data points), and the abnormal changes of the data are random, and the transition relationship between each data point cannot be accurately judged only according to the frequency.

[0038] According to the analysis, for the data points of any dimension, if abnormal changes occur, different degrees of responses will appear in the monitoring data of other dimensions. Therefore, the relationship between any two data points at the same moment in the same dimension can be obtained by combining the data changes in other dimensions at the same moment, that is, the change coordination degree. At the same time, since the data points with anomalies will have a large difference from the data points at other moments in the local time period, therefore, by analyzing the time difference between the data of two time points and the data difference in the local area, the transition probability between any two data points in the same dimension in the Markov chain can be obtained. As shown in formula (2): (2); Wherein, t and r respectively represent any two moments (i.e., the first moment and the second moment) in dimension x, represents the monitoring data value at moment r, represents the data value corresponding to dimension x at moment r the probability of occurrence in the monitoring data, represents the time interval between moment t and moment r, represents the difference between the values of the a-th data points in the same order in the windows where moment t and moment r are located, represents the difference between the change coordination degrees of the a-th data points in the same order in the data windows where moment t and moment r are located.

[0039] Wherein, represents the numerical difference of the data points in the same order in the corresponding data windows of two moments after being weighted by the coordination degree; the smaller this difference is, the more similar the local data distribution degree between any two data points in the same order is, and the local similarity the larger the value of, the higher the probability of the data t transferring to the data r; at the same time, the smaller the time interval between the two moments, the smaller the probability that the data states at the two moments change, the greater the probability that they are in the same state, and the greater the transition probability; and the data value corresponding to moment r The higher the probability of occurrence in the monitoring data, the higher the probability that the monitoring data changes to after the state transition; that is the larger the value of, the greater the probability that the monitoring data value at time t changes to the monitoring data value at time r after the state transition.

[0040] In this way, by analyzing the numerical values and the differences in the degree of cooperation between data points at different times in the same dimension, and combining the theory of distance and probability based on the algorithm itself, the corrected transition probability between any two data is obtained. This operation avoids the randomness of abnormal changes, which makes it impossible to accurately judge the transition relationship between each data according to the frequency, and makes the subsequent detection of abnormal data more accurate.

[0041] Furthermore, based on the transition probability between any two data points in the same dimension, the possibility of abnormal performance of each data point in the monitoring data is determined.

[0042] Here, the possibility of abnormal performance of each data point is used to represent the probability that each data point is an abnormal data point. In this way, the higher the possibility of abnormal performance, the higher the probability that the data point is an abnormal data point, and the lower the possibility of abnormal performance, the lower the probability that the data point is an abnormal data point. After obtaining the transition probability between any two data points, by constructing a transition matrix; calculating the mean square error between the elements in the same order in different columns and rows of the transition matrix, and the mean value of the mean square error, so as to further determine the possibility of abnormal performance of each data point according to the mean square error and the mean value. In this way, by combining the degree of change cooperation with the probability of occurrence of the monitoring data, the transition probability between any two data can be accurately obtained, thus avoiding the inability to accurately judge the transition relationship between data due to the randomness of abnormal data changes, and then being able to facilitate the subsequent determination of abnormal data points.

[0043] In some possible implementation manners, a transition matrix is constructed through the transition probability, and the possibility of abnormal performance of each data point can be quickly calculated from the transition probabilities in the transition matrix, which can be achieved through the following process: First, based on the transition probability between any two data points in the same dimension, a transition matrix is constructed.

[0044] Here, after obtaining the transition probability between any two data, a transition matrix is constructed therefrom; at this time, both the horizontal and vertical axes of the matrix are in accordance with the time sequence positions corresponding to the data.

[0045] Secondly, the mean square error between the elements in the same order in the current column and any row of the transition matrix is determined.

[0046] Here, taking the current column as the nth column as an example, in this transition matrix, first determine the elements in the nth column and those in any row that are in the same order. For example, the elements in the nth column and the elements in the same row that are both in the first position, both in the second position, both in the third position, and so on. Calculate the mean square error between each pair of elements in the same order respectively. For example, the mean square error between the nth column and the element in the ith position in any row.

[0047] Again, based on the mean square error between the current column and the elements in the same order in each row, determine the mean value of the mean square error.

[0048] Here, through the above process, after obtaining the mean square error between the current column and the elements in the same order in each row, further determine the mean value of these mean square errors.

[0049] Finally, perform a normalization process on the difference between the mean square error and the mean value to obtain the likelihood of abnormal performance of the data point corresponding to the current column.

[0050] Here, subtract the obtained multiple mean square errors from the mean value respectively to obtain multiple differences; calculate the reciprocal of each difference, and then calculate the mean value of the obtained multiple reciprocals. After that, perform a normalization process on this mean value to obtain the likelihood of abnormal performance of the data point corresponding to the current column. In this way, by determining the likelihood of abnormal performance generated by any data point, it is possible to avoid the problem that the transfer probability between abnormal data itself and normal data is small, and some overlapping clusters will be lost after iteration, which will interfere with abnormal recognition. At the same time, it avoids the problem of too low abnormal detection efficiency caused by directly performing multiple iterations based on the Markov algorithm process, reduces the redundancy of the algorithm, and improves the accuracy of abnormal recognition.

[0051] In some possible implementation manners, during the process of clustering data using the Markov algorithm, iterative operations of matrix exponentiation and normalization will be performed according to the transition probability, and a new normalized transition matrix will be obtained. According to the number of times the data points corresponding to any horizontal row appear simultaneously with other horizontal rows in several same vertical columns in the transition matrix, several clusters are obtained; theoretically, for normal data points, the overlapping clusters generated after the above steps are fewer, while abnormal data generates more overlapping clusters; however, the transfer probability between some abnormal data itself and normal data is small, and some overlapping clusters will be lost after iteration, which will interfere with abnormal recognition.

[0052] Through analysis, it can be known that the transition matrix is symmetric along the diagonal before normalizing the transition probabilities for each column. The generation premise of the overlapping clusters of any data point is that the matrix products between the corresponding column and several other rows have relatively small differences; and since the normalization is performed column by column, the closer the transition probabilities corresponding to the same order between the current column and any row are, the higher the symmetry between the values of the current column and any row is. This is manifested as, after performing matrix exponentiation and standardization to form a new matrix, there are more and relatively larger stretched transition probabilities corresponding to the same column, and thus the probability of generating more overlapping clusters in the end is greater. At the same time, it also indicates that the possibility of the data point corresponding to the current column showing abnormality is greater. From this, the possibility of the data point corresponding to the current column showing abnormality is as shown in formula (3): (3); In formula (3), is the possibility of showing abnormality corresponding to the nth column, represents the transition probability corresponding to the ith element of the nth column, represents the transition probability of the ith element in the mth row, represents the total number of rows in the matrix. MSE() represents the operation of mean square error, represents the mean square error between the ith element in the same order in the data of the nth column and the mth row (i.e., the ith transition probability in the transition matrix). The smaller this value is, the smaller the difference between the two sequences, and the greater the degree of symmetry it indicates. represents the mean value of the mean square error obtained from the current column n and all rows.

[0053] Among them, represents the difference between the mean square error of the nth column and the mth row and the mean value of the mean square error obtained from the current column n and all rows. The smaller this difference is, the smaller the mutual difference of the matrix products between the corresponding column and several other rows, and the greater the possibility of generating overlapping clusters. That is represents the possibility that the data point corresponding to the nth column shows abnormal data. The larger this value is, the greater the possibility.

[0054] 104. Based on the possibility of showing abnormality, determine the abnormal data points in the monitoring data.

[0055] Here, after obtaining the possibility of showing abnormality of any data point, by judging the size relationship between this possibility of showing abnormality and a preset threshold, determine whether this data point is an abnormal data point. Obtain the data points that generate abnormalities according to the steps, and judge the operating state of the hybrid gas circuit breaker based on the abnormal data points.

[0056] In some possible implementations, by comparing a preset threshold with the likelihood of abnormal performance, it is determined whether a data point in the monitoring data is an abnormal data point, and an alarm message is output for the abnormal data point. That is, step 104 above can be implemented by the following steps 141 and 142: 141. In the monitoring data, determine that a data point with a likelihood of abnormal performance greater than the preset threshold is the abnormal data point.

[0057] In an embodiment of the present application, a threshold of 0.7 can be set. Let the data points corresponding to the columns with a likelihood of abnormal performance greater than the set threshold be the abnormal data points; the data points corresponding to the columns with a likelihood of abnormal performance less than or equal to the set threshold are not abnormal data points. In this way, the abnormal data points in the monitoring data can be determined quickly and simply.

[0058] 142. Generate a running state detection result based on the abnormal data point.

[0059] Here, first analyze the dimension to which the abnormal data point belongs and the source of the abnormal data point; among them, the source of the data point is used to represent the generation location of the abnormal data point, which is convenient for positioning and adjusting the abnormal data point. Then, based on the dimension to which the abnormal data point belongs, generate a detection result that matches the dimension to which the abnormal data point belongs, and output an alarm message carrying the source; in this way, the alarm message is targeted. In this way, by judging the numerical relationship between the likelihood of abnormal performance of the data point and the preset threshold, the abnormal data point is determined, and an alarm message is output in time for the abnormal data point to indicate that the running state is abnormal, so that it is possible to not only have a lower computational complexity, but also accurately detect the abnormal data point and output the detection information and alarm message in a targeted manner. As an example, when the abnormal data point belongs to the dimension of temperature, the detection result can be that the temperature of the operating environment of the hybrid gas circuit breaker is abnormal, and the acquisition time corresponding to the abnormal data point and the abnormal temperature are jointly used as the alarm message.

[0060] In an embodiment of the present application, by monitoring the degree of change coordination of the monitoring data in multiple dimensions; in this way, it is possible to avoid misjudging normal data changes as abnormal due to only analyzing the changes in single-dimensional data to identify the degree of abnormality. Then, by combining the degree of change coordination with the occurrence probability of the monitoring data, the likelihood of abnormal performance of each data point is analyzed. In this way, it is possible to avoid the randomness of abnormal changes resulting in an inability to accurately judge the transfer relationship between each data, thereby making the subsequent detection of abnormal data more accurate. Finally, through the likelihood of abnormal performance of each data point, the abnormal data points in the monitoring data can be quickly determined; in this way, it is possible to not only reduce the redundancy of the algorithm, improve the accuracy of abnormal data recognition, but also improve the accuracy of the running state detection of the hybrid gas circuit breaker.

[0061] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

[0062] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the various embodiments, reference can be made to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. An intelligent detection method for the operating status of electrical equipment, characterized in that: The method comprises: Obtain monitoring data of circuit breakers in multiple dimensions through sensors; For a first dimension among the multiple dimensions, a data window is constructed that matches a target data point of the monitoring data at any moment in the first dimension; wherein the first dimension is any dimension among the multiple dimensions; based on a first data point set of the monitoring data in the first dimension and a second data point set corresponding to each second dimension of the monitoring data, the correlation of the coordinated change of the first dimension is determined; wherein the second dimension is any dimension among the multiple dimensions and the status indication data, and the second dimension is different from the first dimension; based on the target data point and the first data point set, a probability coefficient of the target data point being an outlier is determined; the correlation of the coordinated change of the first dimension is adjusted using the probability coefficient to obtain a degree of coordinated change of the first dimension; Determining the probability of abnormal performance of each data point in the monitoring data based on the degree of change coordination and the probability of occurrence of the monitoring data; Based on the likelihood of abnormal performance, determining abnormal data points in the monitoring data; Based on the abnormal data point, the operating status of the circuit breaker is detected.

2. The method for intelligently detecting the operating status of electrical equipment according to claim 1, characterized in that: The determining, based on a first data point set of the monitoring data in the first dimension and a second data point set corresponding to each of the second dimensions of the monitoring data, a correlation of the coordinated change in the first dimension includes: calculating the covariance between the first set of data points and the second set of data points; The sum of the distribution variance of the first data point set and the distribution variance of the second data point set is used as the denominator; The cumulative result of the ratio of the covariance to the denominator in all second dimensions is taken as the correlation of the first dimension producing synergistic changes.

3. The method for intelligently detecting the operating status of electrical equipment according to claim 1, characterized in that: The determining, based on the target data point and the first set of data points, a probability coefficient that the target data point is an outlier includes: The ratio of the mean value of the data window where the target data point is located to the maximum data value of the first data point set is used as the likelihood coefficient.

4. The method for intelligently detecting the operating status of electrical equipment according to claim 1, characterized in that: The determining, based on the change coordination degree and the occurrence probability of the monitoring data, the possibility of abnormal performance of each data point in the monitoring data includes: Determining a transition probability between any two data points of the same dimension in the monitoring data based on the change coordination degree and the occurrence probability of the monitoring data; Based on the transition probability between any two data points of the same dimension, the possibility of abnormal performance of each data point in the monitoring data is determined.

5. The method for intelligently detecting the operating status of electrical equipment according to claim 4, characterized in that: The determining, based on the change coordination degree and the occurrence probability of the monitoring data, the transition probability between any two data points of the same dimension in the monitoring data includes: In a data window to which a first moment of the first dimension belongs, and in a data window to which a second moment of the first dimension belongs, respectively determining data points in the same order; wherein the first moment and the second moment are any two moments; Determining the local similarity between the data points in the same order based on the data points in the same order and the corresponding change coordination degrees; Based on the occurrence probability of the monitoring data and the local similarity, determine the transition probability between the first data point and the second data point; wherein the first data point is the data value of the monitoring data at the first moment of the first dimension, and the second data point is the data value of the monitoring data at the second moment of the first dimension.

6. The method for intelligently detecting the operating status of electrical equipment according to claim 5, characterized in that: Determining the local similarity between the data points in the same order includes: Calculate the difference between the data points of the same order in the data window at two moments as the first difference; Calculate the difference between the change coordination degrees of the data points in the same order in the data window at two moments as the second difference; The result of accumulating the reciprocal of the absolute value of the product of the first difference and the second difference over the data window is taken as the local similarity.

7. The method for intelligently detecting the operating status of electrical equipment according to claim 6, characterized in that: The determining, based on the occurrence probability of the monitoring data and the local similarity, the transition probability between any two data points of the same dimension in the monitoring data comprises: Obtaining a probability of the second data point appearing in the monitoring data; determining a distance between the first moment and the second moment; A ratio of the probability to the distance is calculated, and a product of the ratio and the local similarity is used as a transition probability between the first data point and the second data point.

8. An intelligent detection method for the operation status of electrical equipment according to any one of claims 5 to 7, characterized in that: The determining of the probability of abnormal performance of each data point in the monitoring data based on the transition probability between any two data points of the same dimension includes: Constructing a transfer matrix based on the transfer probability between any two data points of the same dimension; Determining the mean square error between elements in the same order in the current column and any row in the transfer matrix; Determining a mean of the mean square errors based on mean square errors between elements in the same order in the current column and each row; The difference between the mean square error and the mean is normalized to obtain the performance abnormality possibility of the data point corresponding to the current column.

9. The method for intelligently detecting the operating status of electrical equipment according to claim 1, characterized in that: The determining of abnormal data points in the monitoring data based on the abnormal performance probability includes: In the monitoring data, the data points corresponding to the columns whose abnormal probability is greater than the set threshold are considered abnormal data points; the data points corresponding to the columns whose abnormal probability is less than or equal to the set threshold are not considered abnormal data points; Based on the abnormal data points, operating status detection results and alarm information are generated and output.

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