A method and system for monitoring the operating status of high-voltage cables

Abstract

A method and system for monitoring the operational status of high-voltage cables, relating to the field of power grid monitoring. This method, applied to a cable monitoring system, includes: identifying aberration time points in the monitoring data of underground cables; using the time interval between the aberration time point and the current time point as a fault prediction time window; calculating the high-frequency fractal dimension and low-frequency fractal dimension of the monitoring data within the fault prediction time window; and issuing a fault alert if the difference between the high-frequency fractal dimension and the low-frequency fractal dimension is greater than or equal to a preset fractal dimension threshold, thereby reminding maintenance personnel to inspect and maintain the underground cable. Implementing the technical solution provided in this application solves the problem of short cable fault prediction cycles in online monitoring devices.

Description

Technical Field

[0001] This application relates to the technical field of power grid monitoring, specifically to a method and system for monitoring the operating status of high-voltage cables. Background Technology

[0002] With the widespread application of power grid projects in my country, infrastructure construction in many regions has also developed rapidly; however, in some areas with harsh environments, cable faults occur frequently. Therefore, in order to ensure a stable power supply, monitoring of high-voltage cables is particularly important.

[0003] Currently, the main methods for monitoring underground high-voltage cables are to use various types of online monitoring devices, such as patch temperature measurement, tunnel environment monitoring, cable circulation current monitoring, and partial discharge monitoring, to control the operating status of the cable body and the channel. However, although these traditional monitoring methods can detect cable faults in advance to a certain extent, underground high-voltage cables are subject to numerous and complex interference factors, especially in harsh environments. This results in a very short cable fault prediction cycle for online monitoring devices, which greatly increases the difficulty of cable maintenance. Summary of the Invention

[0004] To address the issue of short cable fault prediction cycles in line monitoring devices, this application provides a method and system for monitoring the operating status of high-voltage cables.

[0005] In a first aspect, this application provides a method for monitoring the operating status of high-voltage cables, applied to a cable monitoring system, the method comprising:

[0006] Identify the time points of anomalous changes in monitoring data of underground cables;

[0007] The time period between the point of change and the current point of time is used as the fault prediction time window;

[0008] Calculate the high-frequency fractal dimension and low-frequency fractal dimension of the monitoring data within the fault prediction time window;

[0009] If the difference between the high-frequency fractal dimension and the low-frequency fractal dimension is greater than or equal to a preset fractal dimension threshold, a fault warning will be issued to remind maintenance personnel to inspect and maintain the underground cable.

[0010] Optionally, the time points at which the monitoring data for identifying underground cables changes are specifically:

[0011] The monitoring data of the underground cable is segmented by a sliding window to obtain multiple monitoring data sequences;

[0012] Calculate multiple statistical features corresponding to each of the multiple monitoring data sequences, wherein the multiple statistical features include multiple time-domain statistical features and multiple frequency-domain statistical features;

[0013] Based on the multiple statistical features corresponding to each of the multiple monitoring data sequences, the feature distance corresponding to each of the multiple monitoring data sequences is calculated using a feature distance function;

[0014] According to the sliding window segmentation order, the feature distances corresponding to each of the multiple monitoring data sequences are compared with the preset feature distance thresholds in turn;

[0015] If the feature distance of the current comparison monitoring data sequence is greater than or equal to the preset feature distance threshold, the comparison is stopped, and the time point corresponding to the starting data point of the current comparison monitoring data sequence is output as the mutation time point.

[0016] Optionally, the feature distance function is specifically:

[0017]

[0018] Among them, D t W represents the feature distance between the t-th monitoring data sequence and the (t-1)-th monitoring data sequence. t F is the timeliness weight for the t-th monitoring data sequence. t,i Let F be the i-th time-domain statistical feature of the t-th monitoring data sequence. t-1,i S is the i-th time-domain statistical feature of the (t-1)-th monitoring data sequence. t,i Let S be the i-th frequency domain statistical feature of the t-th monitoring data sequence. t-1,i Let i be the i-th frequency domain statistical feature of the (t-1)-th monitoring data sequence. The importance weights of time-domain statistical features in feature distance, represents the importance weight of frequency domain statistical features in feature distance, n is the total number of time domain statistical features, and m is the total number of frequency domain statistical features.

[0019] Optionally, calculating the high-frequency fractal dimension and low-frequency fractal dimension of the monitoring data within the fault prediction time window specifically includes:

[0020] Empirical mode decomposition is performed on the monitoring data within the fault prediction time window to obtain the IMF component and residual component;

[0021] The minimum number of boxes for the IMF component is calculated using the box counting method;

[0022] The monitoring data within the fault prediction time window is converted into a gradient curve;

[0023] Based on the gradient curve, the minimum number of boxes is corrected to obtain a minimum corrected number of boxes.

[0024] Based on the multiple minimum correction box numbers of the IMF components, the high-frequency fractal dimension of the monitoring data within the fault prediction time window is calculated.

[0025] Optionally, calculating the high-frequency fractal dimension and low-frequency fractal dimension of the monitoring data within the fault prediction time window further includes:

[0026] The distribution of extreme points of the residual components is statistically analyzed;

[0027] The density of extreme points of the residual components is calculated based on multiple preset time scales.

[0028] The low-frequency fractal dimension is calculated based on the preset distribution scale and the extreme point density.

[0029] Optionally, based on the gradient curve, a weighted sequence data table of monitoring data within the fault prediction time window is calculated, wherein the weighted sequence data table contains weight values ​​for multiple data points.

[0030] Obtain multiple box regions corresponding to the first minimum box number, each box region contains an equal number of data points, the first minimum box number is consistent with the number of the multiple box regions, and the first minimum box number is any one of the multiple minimum box numbers;

[0031] Multiple data points corresponding to the first box region are matched with the weight sequence data table to obtain multiple weight values ​​corresponding to the first box region.

[0032] The largest weight value among the multiple weight values ​​corresponding to the first box region is selected as the weight value of the first box region.

[0033] The weight values ​​of the multiple box regions are added together to obtain the minimum corrected box number of the first minimum box number.

[0034] Optionally, the fault indication includes the location of the fault, the level of the fault, and maintenance requirements.

[0035] Secondly, this application provides a high-voltage cable operation status monitoring system. The system is a cable monitoring system, comprising an acquisition module, a processing module, and an early warning module, wherein:

[0036] The acquisition module is used to identify the time points of anomalies in the monitoring data of underground cables;

[0037] The processing module is used to take the time period between the anomaly time point and the current time point as the fault prediction time window; and to calculate the high-frequency fractal dimension and low-frequency fractal dimension of the monitoring data within the fault prediction time window.

[0038] The early warning module is used to issue a fault prompt if the difference between the high-frequency fractal dimension and the low-frequency fractal dimension is greater than or equal to a preset fractal dimension threshold, so as to remind maintenance personnel to inspect and maintain the underground cable.

[0039] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0040] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0041] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0042] This application identifies the anomaly points in the monitoring data of underground cables to pinpoint the starting time of any abnormalities in the cable's operational status. To further determine the reliability of the anomaly, this application uses the time interval between the anomaly point and the current time as the fault prediction period. Within this period, the difference between the high-frequency fractal dimension and the low-frequency fractal dimension of the monitoring data is calculated to determine the reliability of the anomaly's occurrence. Here, the high-frequency fractal dimension refers to the fractal dimension of the high-frequency signal, and the low-frequency fractal dimension refers to the fractal dimension of the low-frequency signal. It should be explained that the fractal dimension characterizes the complexity of the data; the high-frequency fractal dimension reflects the local complexity of the cable monitoring data, while the low-frequency fractal dimension reflects the overall complexity. Interference factors, being irregular and sudden, primarily reflect... Currently, in the fractal dimension of high-frequency signals, under numerous interference factors, if the cable is operating normally, the high-frequency fractal dimension, although it will change, will gradually stabilize, while the low-frequency fractal dimension will continue to increase, and the difference between the two will continuously decrease. If the cable's operating state becomes abnormal, the high-frequency signal will experience transient changes, and its fluctuation will become very drastic, while the fluctuation of the low-frequency signal will be less affected, thus causing the difference between the two to increase. Therefore, when the difference between the high-frequency fractal dimension and the low-frequency fractal dimension is greater than or equal to the preset fractal dimension threshold, it indicates that the probability of the cable currently experiencing an anomaly is high. At this time, a fault warning is issued to remind maintenance personnel to inspect and maintain the underground cable as soon as possible. This process can greatly extend the fault prediction cycle, no longer limited to short-term prediction, thus providing sufficient preventive time for underground cable maintenance work. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a method for monitoring the operating status of a high-voltage cable according to an embodiment of this application.

[0044] Figure 2 This is a schematic diagram of window sliding monitoring data provided in an embodiment of this application.

[0045] Figure 3 This is a schematic diagram of the structure of a high-voltage cable operation status monitoring system provided in an embodiment of this application.

[0046] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0047] Explanation of reference numerals in the attached drawings: 1. Acquisition module; 2. Processing module; 3. Early warning module; 400. Electronic device; 401. Processor; 402. Communication bus; 403. User interface; 404. Network interface; 405. Memory. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0049] Due to the unique installation environment of underground cables, their operational status monitoring often relies heavily on online monitoring equipment. However, during long-term operation, underground cables may encounter problems such as high temperature, high humidity, accumulation of harmful gases, and water immersion, leading to inaccurate monitoring data from online monitoring equipment. While current cable fault prediction algorithms can reduce or eliminate noise, the sheer volume of cable monitoring data becomes enormous as the fault prediction cycle lengthens, significantly reducing prediction efficiency. Consequently, they can only provide short-term cable fault predictions, greatly shortening the maintenance time available to maintenance personnel and increasing the difficulty of cable maintenance work.

[0050] Therefore, to solve this problem, this application provides a method for monitoring the operating status of high-voltage cables. This method is applied to a cable monitoring system, such as... Figure 1 As shown, the method includes steps S101 to S104, which are as follows:

[0051] S101. Identify the time points of change in monitoring data of underground cables.

[0052] In the above steps, the monitoring data of underground cables is a continuous time-series data, which includes various data types, such as vibration, cable circulating current, cable current, cable voltage, and resistance. When the cable itself malfunctions or the environment changes, the monitoring data of underground cables will fluctuate. In this case, this application identifies the time point at which the fluctuation begins as the anomaly time point, thereby capturing the complete development trend of the fault. When identifying the anomaly time point, since the stability of different types of monitoring data is different, this application identifies the anomaly time points of monitoring data of multiple monitoring types, and then selects the anomaly time point closest to the current time point from multiple anomaly time points as the anomaly time point for subsequent analysis, thus ensuring the timeliness of the monitoring data.

[0053] The specific method for identifying the time points of anomalies is as follows: First, the monitoring data of underground cables is divided into multiple monitoring data sequences by a sliding window. The window size is determined by the degree of fluctuation of the monitoring data. That is, by calculating the coefficient of variation of the monitoring data, and then matching the coefficient of variation with a preset sliding window range table, the window size of the current monitoring data is obtained. It should be noted that if the degree of fluctuation is more severe, the window range is larger to cover the complete feature evolution process. If the degree of fluctuation is more gentle, the window range is smaller to capture more detailed stable features.

[0054] Then, multiple statistical features corresponding to each of the multiple monitoring data sequences are calculated. These statistical features include both time-domain and frequency-domain characteristics of the monitoring data. Time-domain features include, but are not limited to, variance, mean, and skewness; frequency-domain features include, but are not limited to, dominant frequency and secondary frequency. This comprehensively covers both explicit and implicit characteristics of the monitoring data. Next, a feature distance function is used to calculate the feature distances corresponding to each of the multiple monitoring data sequences. It should be noted that for any given monitoring data sequence, its feature distance is calculated with the next adjacent monitoring data sequence. For example, ... Figure 2 As shown, Figure 2 This is a schematic diagram of window sliding monitoring data provided in an embodiment of this application. In this time period, t1 is the starting time point, t2 is the current time point, and the time period contains 5 data (a1, a2, a3, a4, a5). S1 and S2 are both sliding windows. At this time, the feature distance of the monitoring data sequence in S2 is the feature distance between the monitoring data sequence in S1 and the monitoring data sequence in S2.

[0055] The feature distance function is shown below:

[0056]

[0057] Among them, D t W represents the feature distance between the t-th monitoring data sequence and the (t-1)-th monitoring data sequence. t F is the timeliness weight for the t-th monitoring data sequence. t,i Let F be the i-th time-domain statistical feature of the t-th monitoring data sequence. t-1,i S is the i-th time-domain statistical feature of the (t-1)-th monitoring data sequence. t,i Let S be the i-th frequency domain statistical feature of the t-th monitoring data sequence. t-1,i Let i be the i-th frequency domain statistical feature of the (t-1)-th monitoring data sequence. The importance weights of time-domain statistical features in feature distance, represents the importance weight of frequency domain statistical features in feature distance, n is the total number of time domain statistical features, and m is the total number of frequency domain statistical features.

[0058] In the above formula, the Euclidean distance is used to calculate the feature distance between two adjacent monitoring data sequences. The multiple statistical features include both time-domain and frequency-domain statistical features. Since frequency-domain statistical features can express latent features, to more accurately calculate the feature distance between two adjacent monitoring data sequences, a weighted average of the importance of time-domain statistical features in the feature distance is also assigned. The importance weight of frequency domain statistical features in feature distance ,and > , + =1; In addition, since the monitoring data is time-sensitive, the feature distance of each monitoring data sequence in the above formula needs to be multiplied by the time-sensitivity weight for adaptive adjustment, so as to conform to the actual situation of the monitoring data. Specifically, the further away the monitoring data sequence is from the current time point, the lower its time-sensitivity weight.

[0059] After obtaining the feature distances of multiple monitoring data sequences, the feature distances of each of the multiple monitoring data sequences are compared with the preset feature distance thresholds in the order of sliding window segmentation. If the feature distance of a certain monitoring data sequence is greater than or equal to the preset feature distance threshold during the comparison process, it indicates that the monitoring data sequence is significantly different from the adjacent monitoring data sequences, thus indicating that an anomaly has occurred in the monitoring data starting from that monitoring data sequence. At this point, the comparison is stopped, and the time point corresponding to the starting data point of the monitoring data sequence can be taken as the time point of the anomaly.

[0060] S102. Use the time period between the time of the anomaly and the current time as the fault prediction time window.

[0061] S103. Calculate the high-frequency fractal dimension and low-frequency fractal dimension of the monitoring data within the fault prediction time window.

[0062] S104. If the difference between the high-frequency fractal dimension and the low-frequency fractal dimension is greater than or equal to the preset fractal dimension threshold, a fault prompt will be issued to remind maintenance personnel to inspect and maintain the underground cable.

[0063] In steps S102 to S104 above, after determining the time point of the anomaly, the time period of the anomaly is confirmed. At this time, the time period between the time point of the anomaly and the current time point is used as the fault prediction time window, thereby adaptively adjusting the fault prediction time range. This not only extends the fault prediction cycle, but also provides more comprehensive and effective analysis data for fault prediction.

[0064] Furthermore, fluctuations in underground cable monitoring data do not necessarily indicate an anomaly in the cable itself; they could also be caused by changes in the underground environment. Therefore, relying solely on analyzing the intensity of data fluctuations within the fault prediction time window is insufficient to effectively determine whether the cable itself is abnormal, leading to false alarms. To improve the reliability of anomaly detection, this application calculates the high-frequency and low-frequency fractal dimensions of the monitoring data within the fault prediction time window. The high-frequency fractal dimension represents the fractal dimension of the high-frequency signal, and the low-frequency fractal dimension represents the fractal dimension of the low-frequency signal. It should be noted that fractal dimension characterizes the complexity of the data; the high-frequency fractal dimension reflects the local complexity of the cable monitoring data, while the low-frequency fractal dimension reflects the overall complexity. Interference factors caused by environmental changes are irregular and sudden, and therefore primarily manifest in the fractal dimension of the high-frequency signal.

[0065] However, due to the aging process of cables over a long period of use, while the aging may meet the usage standards locally, the overall impact of this aging on the current will accumulate, causing significant fluctuations in a certain section of the transmission line. In this case, relying solely on threshold judgment of the high-frequency fractal dimension may lead to misjudgment. In addition, for the low-frequency fractal dimension, continuous small disturbances will cause the low-frequency fractal dimension to rise continuously, but the impact on the cable is not significant, which may also easily lead to misjudgment. Therefore, this application takes this problem into consideration and calculates the difference between the high-frequency fractal dimension and the low-frequency fractal dimension to adapt to the continuous changes in the high-frequency and low-frequency fractal dimensions. It should be explained that, under conditions with many interference factors, if the cable is operating normally, the high-frequency fractal dimension will change but gradually stabilize, while the low-frequency fractal dimension will continue to increase, and the difference between the two will continuously decrease. If the cable's operating state is abnormal, the high-frequency signal will undergo transient changes, and its fluctuation will become very violent, while the fluctuation of the low-frequency signal will be less affected, thus causing the difference between the two to increase. Therefore, when the difference between the high-frequency fractal dimension and the low-frequency fractal dimension is greater than or equal to the preset fractal dimension threshold, it indicates that the probability of the cable currently experiencing an anomaly is high. At this time, a fault warning is issued to remind maintenance personnel to check and maintain the underground cable as soon as possible.

[0066] In one possible implementation, due to the influence of factors such as aging, environment, and load fluctuations, the monitoring data of underground cables exhibits nonlinear and non-stationary characteristics, resulting in highly chaotic distribution patterns of high-frequency and low-frequency signals. Therefore, this application selects to perform empirical mode decomposition (EMD) on the monitoring data within the fault prediction time window, directly extracting IMF components and residual components at different time scales from the signal. The IMF components represent different oscillation modes of the signal from high to low frequency, while the residual components represent the overall trend of the signal. Since the early signals of cable faults exhibit high-frequency fluctuations, the first IMF component after EMD is selected as the high-frequency signal for subsequent fault analysis to improve the accuracy of cable fault judgment. Then, the box counting method is used to calculate multiple minimum box counts of the IMF components. The box counting method is a conventional technique for those skilled in the art and will not be elaborated further here. It should be noted that... In environments with numerous interference factors, if the cable is in an abnormal state, its fault characteristics are not as obvious as environmental noise. Therefore, to highlight the cable's fault characteristics, this application corrects the number of multiple minimum boxes of the IMF components before calculating the high-frequency fractal dimension, emphasizing key change points and enhancing the saliency of fault characteristics. This allows the high-frequency fractal dimension to more accurately focus on high-value fault information and reduce interference from irrelevant fluctuations. Specifically, the differentiation of the monitoring data within the fault prediction time window is performed to obtain the gradient curve, which reflects the rate of change of the monitoring data at each time point. Then, based on the gradient curve, the weight of each data point in the monitoring data is calculated as follows:

[0067]

[0068] in, Let i be the weight of the i-th data point. Let be the gradient value of the i-th data point.

[0069] In the above formula, the exponential function is used to give greater weight to data points that change drastically (with large gradient values), thereby highlighting key points of change.

[0070] After determining the weights of the data points in the monitoring data, the minimum number of boxes for the IMF component is corrected to obtain multiple minimum corrected box numbers. For example, if there is a minimum number of boxes with 3 boxes among the multiple minimum box numbers of the IMF component, the maximum weight value of multiple data points in each box is selected as the weight value of that box. Then, the weight values ​​of the three boxes are added together to obtain the corrected minimum box number.

[0071] Finally, take the logarithm of the multiple minimum correction box numbers and the fractal scales corresponding to each of the multiple minimum correction box numbers, and then perform curve fitting. At this time, the slope of the fitted curve is the high-frequency fractal dimension.

[0072] In one possible implementation, since the residual component characterizes the overall trend of the signal, the low-frequency fractal dimension of the monitoring data can be determined based on the residual component. However, the residual component has few local geometric details, its waveform exhibits a slow change trend or periodic oscillation, and its energy is distributed over a long time scale, making it difficult for the box counting method to capture its changes. Therefore, this application targets the data characteristics of the residual component, presets multiple time scales, then counts the extreme points within multiple time scales, calculates the extreme point density, and finally takes the logarithm of the multiple time scales and the extreme point density corresponding to the multiple time scales, and performs curve fitting. The slope of the fitted curve is then the low-frequency fractal dimension. This method focuses on the changing pattern of the number of data fluctuations over time, thereby quantifying the density of the fluctuation trend of the low-frequency signal and highlighting the long-term trend of the data.

[0073] In one possible implementation, in order to improve the maintenance efficiency of maintenance personnel, the fault prompt includes the location of the abnormality, the level of the abnormality, and the maintenance requirements.

[0074] Reference Figure 3 This application also provides a high-voltage cable operation status monitoring system, which is a cable monitoring system. The system includes an acquisition module 1, a processing module 2, and an early warning module 3, wherein:

[0075] The acquisition module 1 is used to identify the time points of change in the monitoring data of underground cables;

[0076] The processing module 2 is used to take the time period between the anomaly time point and the current time point as the fault prediction time window; and to calculate the high-frequency fractal dimension and low-frequency fractal dimension of the monitoring data within the fault prediction time window.

[0077] The early warning module 3 is used to issue a fault prompt if the difference between the high-frequency fractal dimension and the low-frequency fractal dimension is greater than or equal to a preset fractal dimension threshold, so as to remind maintenance personnel to inspect and maintain the underground cable.

[0078] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0079] This application also discloses an electronic device. (See reference...) Figure 4 , Figure 4This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 400 may include: at least one processor 401, at least one network interface 404, a user interface 403, a memory 405, and at least one communication bus 402.

[0080] The communication bus 402 is used to enable communication between these components.

[0081] The user interface 403 may include a display screen and a camera. Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0082] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0083] The processor 401 may include one or more processing cores. The processor 401 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 405, and by calling data stored in memory 405. Optionally, the processor 401 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 401 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 401.

[0084] The memory 405 may include random access memory (RAM) or read-only memory. Optionally, the memory 405 may include a non-transitory computer-readable storage medium. The memory 405 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 405 may also be at least one storage device located remotely from the aforementioned processor 401. (Refer to...) Figure 4 The memory 405, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for monitoring the operating status of a high-voltage cable.

[0085] exist Figure 4 In the illustrated electronic device 400, the user interface 403 is mainly used to provide an input interface for the user and acquire user input data; while the processor 401 can be used to call an application program stored in the memory 405 for monitoring the operating status of a high-voltage cable. When executed by one or more processors 401, the electronic device 400 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0086] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0087] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0088] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0090] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0091] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0092] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for monitoring the operating status of a high-voltage cable, characterized in that, The method, applied to a cable monitoring system, includes: Identify the time points of anomalous changes in monitoring data of underground cables; The time period between the point of change and the current point of time is used as the fault prediction time window; The calculation of the high-frequency fractal dimension and low-frequency fractal dimension of the monitoring data within the fault prediction time window specifically includes: Empirical mode decomposition is performed on the monitoring data within the fault prediction time window to obtain the IMF component and residual component; The minimum number of boxes for the IMF component is calculated using the box counting method; The monitoring data within the fault prediction time window is converted into a gradient curve; Based on the gradient curve, the minimum number of boxes is corrected to obtain a minimum corrected number of boxes. Based on the multiple minimum correction box numbers of the IMF components, the high-frequency fractal dimension of the monitoring data within the fault prediction time window is calculated. If the difference between the high-frequency fractal dimension and the low-frequency fractal dimension is greater than or equal to a preset fractal dimension threshold, a fault warning will be issued to remind maintenance personnel to inspect and maintain the underground cable.

2. The method according to claim 1, characterized in that, The specific time points at which the monitoring data for identifying underground cables changes are: The monitoring data of the underground cable is segmented by a sliding window to obtain multiple monitoring data sequences; Calculate multiple statistical features corresponding to each of the multiple monitoring data sequences, wherein the multiple statistical features include multiple time-domain statistical features and multiple frequency-domain statistical features; Based on the multiple statistical features corresponding to each of the multiple monitoring data sequences, the feature distance corresponding to each of the multiple monitoring data sequences is calculated using a feature distance function; According to the sliding window segmentation order, the feature distances corresponding to each of the multiple monitoring data sequences are compared with the preset feature distance thresholds in turn; If the feature distance of the current comparison monitoring data sequence is greater than or equal to the preset feature distance threshold, the comparison is stopped, and the time point corresponding to the starting data point of the current comparison monitoring data sequence is output as the mutation time point.

3. The method according to claim 2, characterized in that, The specific feature distance function is as follows: Among them, D t W represents the feature distance between the t-th monitoring data sequence and the (t-1)-th monitoring data sequence. t F is the timeliness weight for the t-th monitoring data sequence. t,i Let F be the i-th time-domain statistical feature of the t-th monitoring data sequence. t-1,i S is the i-th time-domain statistical feature of the (t-1)-th monitoring data sequence. t,i Let S be the i-th frequency domain statistical feature of the t-th monitoring data sequence. t-1,i Let i be the i-th frequency domain statistical feature of the (t-1)-th monitoring data sequence. The importance weights of time-domain statistical features in feature distance, represents the importance weight of frequency domain statistical features in feature distance, n is the total number of time domain statistical features, and m is the total number of frequency domain statistical features.

4. The method according to claim 1, characterized in that, The calculation of the high-frequency fractal dimension and low-frequency fractal dimension of the monitoring data within the fault prediction time window also includes: The distribution of extreme points of the residual components is statistically analyzed; The density of extreme points of the residual components is calculated based on multiple preset time scales. The low-frequency fractal dimension is calculated based on the preset multiple time scales and the extreme point density.

5. The method according to claim 1, characterized in that, The step of correcting the multiple minimum box numbers according to the gradient curve to obtain multiple minimum corrected box numbers specifically includes: Based on the gradient curve, a weighted sequence data table of monitoring data within the fault prediction time window is calculated, and the weighted sequence data table contains weight values ​​for multiple data points. Obtain multiple box regions corresponding to the first minimum box number, each box region contains an equal number of data points, the first minimum box number is consistent with the number of the multiple box regions, and the first minimum box number is any one of the multiple minimum box numbers; Multiple data points corresponding to the first box region are matched with the weight sequence data table to obtain multiple weight values ​​corresponding to the first box region. The largest weight value among the multiple weight values ​​corresponding to the first box region is selected as the weight value of the first box region. The weight values ​​of the multiple box regions are added together to obtain the minimum corrected box number of the first minimum box number.

6. The method according to claim 1, characterized in that, The fault message includes the location of the fault, the level of the fault, and the maintenance requirements.

7. A high-voltage cable operation status monitoring system, characterized in that, The system is used to execute a high-voltage cable operation status monitoring method as described in any one of claims 1-6. The system is a cable monitoring system, and the system includes an acquisition module (1), a processing module (2), and an early warning module (3), wherein: The acquisition module (1) is used to identify the time points of change in the monitoring data of underground cables; The processing module (2) is used to take the time period between the anomaly time point and the current time point as the fault prediction time window; and to calculate the high-frequency fractal dimension and low-frequency fractal dimension of the monitoring data within the fault prediction time window. The early warning module (3) is used to issue a fault prompt if the difference between the high-frequency fractal dimension and the low-frequency fractal dimension is greater than or equal to a preset fractal dimension threshold, so as to remind maintenance personnel to inspect and maintain the underground cable.

8. An electronic device, characterized in that, The device includes a processor (401), a memory (405), a user interface (403), and a network interface (404). The memory (405) is used to store instructions. The user interface (403) and the network interface (404) are used to communicate with other devices. The processor (401) is used to execute the instructions stored in the memory (405) to cause the electronic device (400) to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 6.

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