Online intelligent monitoring method and system for power transmission line

Abstract

The invention relates to the technical field of data processing, in particular to an online intelligent monitoring method and system for a power transmission line, and the method comprises the steps: carrying out the clustering of multi-dimensional data points, obtaining a plurality of clustering clusters, and calculating the abnormal degree of the clustering clusters; generating an early warning signal in response to that the abnormal degree is greater than a preset threshold value; the multi-dimensional data points comprise the temperature, the humidity and the wind speed of the power transmission line; wherein for any clustering cluster, the weighted Euclidean distance between each data point contained in the clustering cluster and the clustering center of the clustering cluster is calculated and normalized to obtain a normalization result, and the mean value of the cumulative sum of the normalization results of all the clustering clusters is used as the abnormal degree. The method has the effect of improving the fault recognition accuracy of the power transmission line.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to an online intelligent monitoring method and system for power transmission lines. Background Art

[0002] In modern power systems, transmission lines are an important part of power transmission, and their safe and stable operation directly affects the reliability of power supply. However, traditional transmission line detection methods have many limitations, such as low frequency of manual inspections, slow response speed, and insufficient ability to identify hidden dangers. These problems make it difficult to detect line faults in a timely manner, increasing the operational risks of the power system.

[0003] In recent years, with the rapid development of smart grids, online monitoring technology has been widely used. By using sensors to realize real-time monitoring of transmission lines, and by comprehensively analyzing multi-dimensional data of environmental factors such as temperature, humidity, wind speed, etc., a basis for fault prediction is provided. For example, the patent application document with publication number CN118209817A discloses a method and system for identifying transmission line faults based on environmental characteristics. The system fuses the acquired multi-dimensional data and performs dimensionality reduction processing on the initial multi-dimensional fusion features to obtain the target multi-dimensional fusion features, and realizes the fault identification of the transmission line according to the target multi-dimensional fusion features.

[0004] For the multidimensional data of power transmission lines, due to the different distribution characteristics of data in different dimensions, dimensionality reduction processing can only retain some features, thereby ignoring other features, resulting in inaccurate fault identification results for power transmission lines. The iterative self-organizing clustering algorithm is an algorithm for clustering multidimensional data. By clustering multidimensional data, the characteristics of each dimension can be accurately obtained, thereby making the fault identification results for power transmission lines more accurate.

[0005] In the clustering process, the traditional iterative self-organizing clustering algorithm calculates the Euclidean distance between any multidimensional data point and the cluster center to update the clustering cluster. However, due to the different distribution characteristics of different dimensions of the multidimensional data of the transmission line, directly using the Euclidean distance to iteratively update the clustering cluster will misclassify some multidimensional data points into other clusters, resulting in inaccurate clustering results, which further affects the fault identification results of the transmission line. Summary of the invention

[0006] In order to solve the technical problem of large errors in fault identification of power transmission lines, the present application provides a method and system for online intelligent monitoring of power transmission lines.

[0007] In a first aspect, the present application provides a method for online intelligent monitoring of a power transmission line, which adopts the following technical solution: A method for online intelligent monitoring of a power transmission line comprises the steps of: clustering multi-dimensional data points to obtain a plurality of clusters, calculating the abnormality of the clusters; generating an early warning signal in response to the abnormality being greater than a preset threshold; the multi-dimensional data points include: the temperature, humidity and wind speed of the power transmission line; wherein, for any cluster, the weighted Euclidean distance between each data point included in the cluster and the cluster center of the cluster is calculated and normalized to obtain a normalized result, and the average of the accumulated normalized results of all clusters is taken as the abnormality; the calculation formula of the weighted Euclidean distance is: , Indicates The first Data points and The weighted Euclidean distance between the cluster centers of the clusters, Indicates Data points and The cluster center of the cluster is in The difference in dimensions, Indicates The weighting coefficients of the dimensions, Indicates the total number of dimensions.

[0008] The beneficial effects are: the weighted Euclidean distance is obtained by weighting the dimensions such as temperature, humidity, and wind speed using their respective weights, and the degree of abnormality is calculated based on the weighted Euclidean distance to reflect the abnormal situation of the transmission line monitoring data, so as to achieve the purpose of real-time monitoring of the status of the transmission line. By monitoring key parameters such as temperature, humidity, and wind speed, abnormal situations can be discovered in time, thereby effectively preventing power system failures and improving the safety and reliability of power grid operation.

[0009] Optional, weighting factor The calculation formula is: ,in, represents the total number of clusters, Indicates The first cluster The total number of data points in each dimension, Indicates The total number of data points involved in clustering of the dimension, Indicates The first The periodic strength of the dimension, Represents the standard normalization function.

[0010] The beneficial effect is that the weight of the dimension is calculated according to the periodicity strength of the same dimension in all clusters. The stronger the periodicity strength, the greater the weight corresponding to the dimension. is the confidence level of the periodicity strength of the kth cluster. The larger its value is, the more data points there are in the cluster, and the higher the confidence level of the periodicity strength of the cluster is. The number of data points and the periodicity strength are comprehensively considered.

[0011] Optional, weighting factor The calculation formula is: ,in, represents the total number of clusters, Indicates The first The periodic strength of the dimension, Represents the standard normalization function.

[0012] The beneficial effects are: it is suitable for application scenarios where the data points in the clusters are evenly distributed or the number of data points has little effect on the periodicity intensity, and it simplifies the calculation process of the weighted coefficients, making it easier to implement and calculate.

[0013] Optionally, the periodicity strength is calculated as: ,in, Indicates The periodic intensity of the dimension, Indicates The total number of data points in the dimensions, Indicates Dimension The data value of the data point; Indicates The mean of all data points in the dimension; Indicates The standard deviation of the data values ​​of all data points in the dimension, Indicates An exponential function with base .

[0014] The beneficial effects are: through Quantify the periodicity strength, The larger the value of , the stronger the periodicity of the dimension, and vice versa, the weaker the periodicity of the dimension; Indicates that the dimension The greater the difference between the data point and the dimension data, the greater the value of the dimension. The greater the difference between the data point and the data of that dimension. It indicates the average level of the difference between all data points in this dimension and the data of this dimension. The smaller its value is, the closer all data points in this dimension are. The consistency of all data points in this cluster is higher, that is, the periodic characteristics of the data of this dimension are more obvious.

[0015] Optionally, the periodicity strength is calculated as: ,in, Indicates The periodic intensity of the dimension, Indicates The total number of data points in the dimensions, Indicates Dimension The data value of the data point; Indicates The mean of all data points in the dimension; Indicates The standard deviation of the data values ​​of all data points in the dimension, Indicates The standard deviation of all data points at the time of difference in the dimension, Indicates An exponential function with base .

[0016] The beneficial effects are: It indicates the average level of the difference between all data points in this dimension and the data of this dimension. The smaller its value is, the closer all data points in this dimension are. The consistency of all data points in this cluster is high, that is, the more obvious the periodic characteristics of the data in this dimension are. It indicates the degree of fluctuation of the differential moments of all data points of the dimension data. The smaller the value, the smaller the degree of fluctuation of the differential moments of all data points of the dimension data. The closer the differential moments are, the closer the time intervals of the dimension data are, that is, the stronger the periodicity of the dimension.

[0017] Optionally, during the clustering process, for any cluster in any iteration round, the data points are sorted in ascending order, and the difference values ​​of adjacent data points at corresponding moments are calculated to obtain a first-order difference sequence.

[0018] Optionally, multi-dimensional data points are clustered to obtain multiple clusters, including the following steps: in each clustering round, for any data point, the data point is divided into the cluster where the cluster center with the smallest initial weighted Euclidean distance to the data point is located, all data points are divided, and then the cluster center of each cluster is updated according to the data values ​​of all data points in different dimensions in each cluster, until the preset clustering end condition is reached and the clustering is stopped, thereby obtaining multiple clusters.

[0019] In the second aspect, the present application provides an online intelligent monitoring system for transmission lines, which adopts the following technical solutions: A transmission line online intelligent monitoring system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the transmission line online intelligent monitoring method described above is implemented.

[0020] The beneficial effect is: the above-mentioned online intelligent monitoring method for transmission lines is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0021] This application has the following technical effects: 1. Use the respective weights of temperature, humidity, wind speed and other dimensions to weight the dimension to obtain the weighted Euclidean distance. Calculate the degree of abnormality based on the weighted Euclidean distance to reflect the abnormal situation of the transmission line monitoring data, so as to achieve the purpose of real-time monitoring of the transmission line status. By monitoring key parameters such as temperature, humidity, wind speed, etc., abnormal situations can be discovered in time, thereby effectively preventing power system failures and improving the safety and reliability of power grid operation.

[0022] 2. Calculate the weight of the dimension based on the periodicity strength of the same dimension in all clusters. The stronger the periodicity strength, the greater the weight corresponding to the dimension. The number of data points and the periodicity strength are comprehensively considered in the calculation of the weight coefficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become easily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-restrictive manner, and the same or corresponding numbers represent the same or corresponding parts.

[0024] Figure 1 It is a method flow chart of a method for online intelligent monitoring of transmission lines in an embodiment of the present application.

[0025] Figure 2 It is a method flow chart of step S1 in a method for online intelligent monitoring of a power transmission line according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0027] It should be understood that when the terms "first", "second", etc. are used in the claims, specification and drawings of the present application, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the specification and claims of the present application indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections.

[0028] The present application discloses an online intelligent monitoring method for a power transmission line. Figure 1 , including steps S1-S2, which are as follows: S1: Cluster the multi-dimensional data points to obtain multiple clusters, and calculate the abnormality degree of the clusters.

[0029] In one embodiment, a temperature sensor, a humidity sensor, and a wind speed tester are arranged on the transmission tower to collect the temperature, humidity, and wind speed on the transmission line. The preset collection cycle is 2 seconds. The data of each dimension collected at the same time is regarded as a multi-dimensional data point.

[0030] The multi-dimensional data points are clustered to obtain multiple clusters. This application uses an iterative self-organizing clustering algorithm for clustering.

[0031] The iterative self-organizing clustering algorithm continuously distributes data points to the clusters where the nearest cluster center is located through multiple clustering rounds, thereby realizing continuous optimization of cluster clusters. Among them, each clustering round will recalculate the cluster center of each cluster until the cluster center no longer changes or reaches a preset number of iterations. The iterative self-organizing clustering algorithm is a prior art and will not be described in detail here.

[0032] In the process of continuous iteration of clustering rounds, the traditional iterative self-organizing clustering algorithm calculates the Euclidean distance between any data point and all cluster centers, and divides the data point into the cluster where the cluster center with the smallest Euclidean distance is located. However, since different dimensions have different effects on clustering results, it is necessary to use the weights of all dimensions to weight the dimension, so as to obtain the weighted Euclidean distance from the data point to all cluster centers.

[0033] Specifically, in the process of continuous iteration of clustering rounds, in order to facilitate distinction and description, the weighted Euclidean distance between any data point in the cluster and the cluster center of any cluster cluster is defined as the initial weighted Euclidean distance, denoted as After clustering is completed, the weighted Euclidean distance from a data point in any cluster to the cluster center of the cluster is defined as the weighted Euclidean distance, denoted as .

[0034] The calculation formula for the initial weighted Euclidean distance is: , Indicates Data points and The initial weighted Euclidean distance of the cluster centers of the clusters, Indicates Data points and The cluster center of the cluster is in The difference in dimensions, Indicates The weighting coefficients of the dimensions, Represents the total number of dimensions. The calculation method of the weighted coefficient is the same as that of the subsequent weighted coefficients, which will not be described here. The mean of all data points in the cluster is used as the cluster center of the cluster. The data point is not necessarily in in a cluster.

[0035] Reference Figure 2 , step S1 includes steps S10 to S12, which are specifically as follows: S10: In each clustering round, for any data point, the data point is divided into the cluster where the cluster center with the smallest initial weighted Euclidean distance to the data point is located, and all data points are divided. Then, the cluster center of each cluster is updated according to the data values ​​of all data points in different dimensions in each cluster.

[0036] S11: After the last iteration is completed, clustering is completed and the clustering result is obtained.

[0037] The clustering end condition may be that the cluster center no longer changes or the number of clustering iterations reaches a preset number, and clustering is stopped. The preset number of clustering iterations may be determined by the implementer according to the specific implementation situation, and this application does not specifically limit it. For example, the number of clustering iterations is 100 times.

[0038] S12: For any cluster, calculate the weighted Euclidean distance between each data point contained in the cluster and the cluster center of the cluster and normalize them to obtain the normalized result, traverse all clusters, and take the mean of the sum of the normalized results of all clusters as the abnormality degree.

[0039] In one embodiment, the calculation of the abnormality degree can be expressed by a mathematical expression as follows: ; In the formula, Indicates the abnormality of the transmission line monitoring data. The total number of clusters representing the clustering results. represents the cluster number, Indicates The standard deviation of the weighted Euclidean distance between all data points in a cluster and the cluster center of the cluster, Represents the standard normalization function.

[0040] For any cluster, if The smaller the value, the more concentrated the data in the cluster is, and the lower the abnormality of the cluster is; the average abnormality of all clusters is taken as the abnormality of the transmission line monitoring data.

[0041] , Indicates The first Data points and The weighted Euclidean distance between the cluster centers of the clusters, Indicates Data points and The cluster center of the cluster is in The difference in dimensions, Indicates The weighting coefficients of the dimensions, Indicates the total number of dimensions.

[0042] In one embodiment, the weighting coefficient The calculation formula is: ,in, represents the total number of clusters, Indicates The first cluster The total number of data points in each dimension, Indicates The total number of data points involved in clustering of the dimension, Indicates The first The periodic strength of the dimension, Represents the standard normalization function.

[0043] The weight of the dimension is calculated according to the periodicity strength of the same dimension in all clusters. The stronger the periodicity strength, the greater the weight corresponding to the dimension. is the confidence level of the periodicity strength of the kth cluster. The larger its value is, the more data points there are in the cluster, and the higher the confidence level of the periodicity strength of the cluster is. The number of data points and the periodicity strength are comprehensively considered.

[0044] In one embodiment, the weighting coefficient The calculation formula can also be: ,in, represents the total number of clusters, Indicates The first The periodic strength of the dimension, This embodiment is suitable for application scenarios where the data points in the clusters are evenly distributed or the number of data points has little effect on the periodicity intensity, and simplifies the calculation process of the weighted coefficients, making it easier to implement and calculate.

[0045] In the clustering process, multidimensional data points with relatively close Euclidean distances will be clustered in the same cluster. Since the Euclidean distance is an intuitive distance measurement between multidimensional data points, for transmission line monitoring data, due to the differences in data of different dimensions, some multidimensional data are very close in Euclidean distance, but have large differences in some dimensions.

[0046] Therefore, for multidimensional data, different dimensions have different overall impacts on the data, and for data with obvious periodicity, traditional clustering algorithms cannot take periodicity into account well, resulting in a decrease in clustering effect. For example, for transmission line monitoring data, as time changes, the temperature and humidity have a certain periodicity, which has a greater impact on the clustering results. And for any dimension, the stronger the periodicity of the dimension, by increasing the weight of the dimension, data points with similar periods can be more effectively clustered in the same cluster cluster, optimizing the clustering results. At the same time, by increasing the weight of the dimension with strong periodicity, the impact of non-critical or noise dimensions can be relatively reduced, thereby improving the overall clustering accuracy.

[0047] Therefore, the present application obtains the periodicity strength of each dimension and obtains the weight of the dimension according to the periodicity strength of the dimension.

[0048] In one embodiment, the periodicity strength is calculated as follows: In the clustering process, for any cluster in any iteration round, the data points are sorted in ascending order, and the corresponding time of the next multidimensional data point is subtracted from the collection time of the previous multidimensional data point to obtain the differential time of the data point, that is, the differential value of the corresponding time of adjacent data points is calculated to obtain the first-order differential sequence.

[0049] Calculate the periodic intensity using the formula: ,in, Indicates The periodic intensity of the dimension, Indicates The total number of data points in the dimensions, Indicates Dimension The data value of the data point; Indicates The mean of all data points in the dimension; Indicates The standard deviation of the data values ​​of all data points in the dimension, Indicates The standard deviation of all data points at the time of difference in the dimension, Indicates An exponential function with base .

[0050] The larger the value of , the stronger the periodicity of the dimension, and vice versa, the weaker the periodicity of the dimension; Indicates that the dimension The greater the difference between the data point and the dimension data, the greater the value of the dimension. The greater the difference between the data point and the data of that dimension.

[0051] Depend on By transforming the formula, we can get: It indicates the average level of the difference between all data points in this dimension and the data of this dimension. The smaller its value is, the closer all data points in this dimension are. The consistency of all data points in this cluster is higher, that is, the periodic characteristics of the data of this dimension are more obvious.

[0052] It indicates the degree of fluctuation of the differential moments of all data points of the dimension data. The smaller the value, the smaller the degree of fluctuation of the differential moments of all data points of the dimension data. The closer the differential moments are, the closer the time intervals of the dimension data are, that is, the stronger the periodicity of the dimension.

[0053] At this point, the periodicity strength of any cluster in any cluster can be obtained.

[0054] In one embodiment, the calculation formula of the periodicity intensity may also be: ,in, Indicates The periodic intensity of the dimension, Indicates The total number of data points in the dimensions, Indicates Dimension The data value of the data point; Indicates The mean of all data points in the dimension; Indicates The standard deviation of the data values ​​of all data points in the dimension, Indicates This embodiment provides a simple and direct method for calculating periodic intensity, which is suitable for application scenarios requiring small amount of calculation.

[0055] S2: In response to the abnormality level being greater than a preset threshold, a warning signal is generated.

[0056] If the abnormality of the transmission line monitoring data is greater than the preset threshold , an early warning signal is generated to give a prompt, otherwise, monitoring continues without alarming. Preset threshold The implementation personnel can limit the value of according to the specific implementation situation. For example, the preset threshold Take 0.7. The early warning signal can be a text message such as "abnormal warning" sent to the monitoring platform.

[0057] An embodiment of the present application also discloses an online intelligent monitoring system for a power transmission line, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an online intelligent monitoring method for a power transmission line according to the present application is implemented.

[0058] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0059] In the present application, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high bandwidth memory HBM (High Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0060] Although this specification has shown and described a plurality of embodiments of the present application, it is obvious to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, modifications and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein may be adopted.

[0061] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for online intelligent monitoring of power transmission lines, characterized in that: Includes steps: Cluster the multi-dimensional data points to obtain multiple clusters, and calculate the abnormality degree of the clusters; In response to the abnormality being greater than a preset threshold, an early warning signal is generated; the multi-dimensional data points include: temperature, humidity and wind speed of the transmission line; Among them, for any cluster, the weighted Euclidean distance between each data point contained in the cluster and the cluster center of the cluster is calculated and normalized to obtain the normalized result, and the average of the normalized results of all clusters is taken as the abnormality degree; the calculation formula of the weighted Euclidean distance is: , Indicates The first Data points and The weighted Euclidean distance between the cluster centers of the clusters, Indicates Data points and The cluster center of the cluster is in The difference in dimensions, Indicates The weighting coefficients of the dimensions, Indicates the total number of dimensions.

2. The method for online intelligent monitoring of power transmission lines according to claim 1, characterized in that: include: Weighting coefficient The calculation formula is: ,in, represents the total number of clusters, Indicates The first cluster The total number of data points in each dimension, Indicates The total number of data points involved in clustering of the dimension, Indicates The first The periodic strength of the dimension, Represents the standard normalization function.

3. The method for online intelligent monitoring of power transmission lines according to claim 1, characterized in that: include: Weighting coefficient The calculation formula is: ,in, represents the total number of clusters, Indicates The first The periodic strength of the dimension, Represents the standard normalization function.

4. The method for online intelligent monitoring of power transmission lines according to claim 2 or 3, characterized in that: The calculation formula of periodic intensity is: ,in, Indicates The periodic intensity of the dimension, Indicates The total number of data points in the dimensions, Indicates Dimension The data value of the data point; Indicates The mean of all data points in the dimension; Indicates The standard deviation of the data values ​​of all data points in the dimension, Indicates An exponential function with base .

5. The method for online intelligent monitoring of power transmission lines according to claim 2 or 3, characterized in that: The calculation formula of periodic intensity is: ,in, Indicates The periodic intensity of the dimension, Indicates The total number of data points in the dimensions, Indicates Dimension The data value of the data point; Indicates The mean of all data points in the dimension; Indicates The standard deviation of the data values ​​of all data points in the dimension, Indicates The standard deviation of all data points at the time of difference in the dimension, Indicates An exponential function with base .

6. The method for online intelligent monitoring of power transmission lines according to claim 5, characterized in that: In the clustering process, for any cluster in any iteration round, the data points are sorted in ascending order, and the difference values ​​of adjacent data points at corresponding moments are calculated to obtain the first-order difference sequence.

7. The method for online intelligent monitoring of power transmission lines according to claim 1, characterized in that: Clustering multi-dimensional data points to obtain multiple clusters includes the following steps: In each clustering round, for any data point, the data point is divided into the cluster whose cluster center has the smallest initial weighted Euclidean distance to the data point. All data points are divided, and then the cluster center of each cluster is updated according to the data values ​​of all data points in different dimensions in each cluster until the preset clustering end condition is reached and clustering is stopped, resulting in multiple clusters.

8. An online intelligent monitoring system for power transmission lines, 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, the method for online intelligent monitoring of a power transmission line according to any one of claims 1 to 7 is implemented.

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