Method and system for monitoring operation state of high-voltage cable

Abstract

The invention discloses a high-voltage cable operation state monitoring method and system, and relates to the field of power grid monitoring. The method is applied to a cable monitoring system, and comprises the following steps: identifying a change time point of monitoring data of an underground cable; taking a time period between the variation time point and the current time point as a fault prediction time window; calculating a high-frequency fractal dimension and a low-frequency fractal dimension of the monitoring data in the fault prediction time window; and if a difference value between the high-frequency fractal dimension and the low-frequency fractal dimension is greater than or equal to a preset fractal dimension threshold value, sending a fault prompt to remind a maintainer to check and maintain the underground cable. By implementing the technical scheme provided by the invention, the problem that the cable fault prediction period of the online monitoring device is short is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid monitoring, and particularly relates to a running state monitoring method and system of a high-voltage cable. BACKGROUND

[0002] With the popularization of power grid projects in China, the infrastructure construction in many regions has also developed rapidly; however, for some regions with harsh environments, cable failures occur frequently, and therefore, it is particularly important to monitor high-voltage cables in order to ensure stable power supply.

[0003] At present, the monitoring method for underground high-voltage cables mainly controls the running state of the cable body and the passage through patch type temperature measurement, tunnel environment monitoring, cable circulating current monitoring, partial discharge monitoring and other types of online monitoring devices; however, these traditional monitoring methods can only perceive the occurrence of cable failures to a certain extent, but for underground high-voltage cables, there are many and complex interference factors, especially in harsh environments, which makes the cable failure prediction period of the online monitoring device very short, thereby greatly increasing the difficulty of cable maintenance work. SUMMARY

[0004] In view of the short cable failure prediction period of the online monitoring device, the present application provides a running state monitoring method and system of a high-voltage cable.

[0005] In a first aspect, the present application provides a running state monitoring method of a high-voltage cable, applied to a cable monitoring system, and the method comprises:

[0006] identifying an abnormal time point of monitoring data of an underground cable;

[0007] taking a time period between the abnormal time point and a current time point as a fault prediction time window;

[0008] calculating a high-frequency fractal dimension and a low-frequency fractal dimension of the monitoring data in the fault prediction time window;

[0009] if a difference between the high-frequency fractal dimension and the low-frequency fractal dimension is greater than or equal to a preset fractal dimension threshold, issuing a fault prompt to remind maintenance personnel to investigate and maintain the underground cable.

[0010] Optionally, the identification of the abnormal time point of the monitoring data of the underground cable is specifically:

[0011] performing sliding window segmentation on the monitoring data of the underground cable to obtain a plurality of monitoring data sequences;

[0012] a plurality of statistical characteristic quantities corresponding to each of the plurality of monitoring data sequences, the plurality of statistical characteristic quantities comprising a plurality of time domain statistical characteristic quantities and a plurality of frequency domain statistical characteristic quantities;

[0013] a feature distance corresponding to each of the plurality of monitoring data sequences is calculated according to the plurality of statistical characteristic quantities corresponding to each of the plurality of monitoring data sequences, by using a feature distance function;

[0014] the feature distance corresponding to each of the plurality of monitoring data sequences is compared with a preset feature distance threshold in sequence according to the sliding window segmentation order;

[0015] if the feature distance of the currently compared monitoring data sequence is greater than or equal to the preset feature distance threshold, the comparison is stopped, and a time point corresponding to a starting data point of the currently compared monitoring data sequence is output as the anomaly time point.

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

[0017]

[0018] wherein, D t is a feature distance between the tth monitoring data sequence and the (t-1)th monitoring data sequence, W t is a timeliness weight of the tth monitoring data sequence, F t,i is an ith time domain statistical characteristic quantity of the tth monitoring data sequence, F t-1,i is an ith time domain statistical characteristic quantity of the (t-1)th monitoring data sequence, n is a total number of statistical characteristic quantities. S t,i is an ith frequency domain statistical characteristic quantity of the tth monitoring data sequence, S t-1,i is an ith frequency domain statistical characteristic quantity of the (t-1)th monitoring data sequence, is an importance weight of the time domain statistical characteristic in the feature distance, is an importance weight of the frequency domain statistical characteristic in the feature distance, n is a total number of time domain statistical characteristic quantities, and m is a total number of frequency domain statistical characteristic quantities.

[0019] Optionally, the calculation of the high-frequency fractal dimension and the low-frequency fractal dimension of the monitoring data in the fault prediction time window specifically comprises:

[0020] the monitoring data in the fault prediction time window is subjected to empirical mode decomposition to obtain an IMF component and a residual component;

[0021] a plurality of minimum box numbers of the IMF component are calculated by using a box counting method;

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

[0023] According to the gradient curve, the plurality of minimum box numbers are corrected to obtain a plurality of minimum corrected box numbers;

[0024] Based on the plurality of minimum corrected box numbers of the IMF component, a high-frequency fractal dimension of the monitoring data in the fault prediction time window is calculated.

[0025] Optionally, the calculation of the high-frequency fractal dimension and the low-frequency fractal dimension of the monitoring data in the fault prediction time window further comprises:

[0026] The extreme point distribution of the residual component is counted;

[0027] According to a plurality of preset time scales, the extreme point density of the residual component is calculated;

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

[0029] Optionally, according to the gradient curve, a weight sequence data table of the monitoring data in the fault prediction time window is calculated, and the weight sequence data table comprises weight values of a plurality of data points.

[0030] A plurality of box regions corresponding to a first minimum box number are obtained, each box region comprising an equal number of data points, the first minimum box number being consistent with the number of the plurality of box regions, and the first minimum box number being any one of the plurality of minimum box numbers.

[0031] The plurality of data points corresponding to a first box region are matched with the weight sequence data table to obtain a plurality of weight values corresponding to the first box region.

[0032] The maximum weight value of the plurality of 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 plurality of box regions are added to obtain a minimum corrected box number of the first minimum box number.

[0034] Optionally, the fault prompt comprises an abnormal position, an abnormal level, and a maintenance requirement.

[0035] In a second aspect, the application provides a running state monitoring system of a high-voltage cable, the system being a cable monitoring system, the system comprising an acquisition module, a processing module, and a warning module, wherein:

[0036] The acquisition module is configured to identify an abnormal time point of the monitoring data of the underground cable.

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

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

[0039] In a third aspect, the present application provides an electronic device, comprising a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the method according to any one of the first aspect.

[0040] In a fourth aspect, the present application provides a computer readable storage medium, which stores instructions, and when the instructions are executed, the method according to any one of the first aspect is performed.

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

[0042] The application identifies the abnormal time point of the monitoring data of the underground cable, so as to determine the starting time point of the abnormality of the running state of the underground cable; in order to further determine the reliability of the abnormality, the application takes the time period between the abnormal time point and the current time point as a fault prediction time period, and calculates the difference between the high-frequency fractal dimension and the low-frequency fractal dimension of the monitoring data in the time period, so as to determine whether the abnormality is reliable, wherein the high-frequency fractal dimension is the fractal dimension of the high-frequency signal, and the low-frequency fractal dimension is the fractal dimension of the low-frequency signal; it needs to be explained that the fractal dimension represents the complexity of the data, the high-frequency fractal dimension reflects the complexity of the cable monitoring data in the local, and the low-frequency fractal dimension reflects the complexity of the cable monitoring data in the whole, and the interference factor is irregular and sudden, so it is mainly reflected in the fractal dimension of the high-frequency signal, in the case of many interference factors, if the cable runs normally, the high-frequency fractal dimension will change but gradually stabilize, but the low-frequency fractal dimension will continue to increase, and the difference between the two will continue to decrease, if the running state of the cable is abnormal, the high-frequency signal will change instantaneously, and the fluctuation degree will become very violent, and the low-frequency signal is less affected, so that the difference between the two will become larger; 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 means that the probability of the current abnormality of the cable is high, at this time, a fault prompt is sent out, reminding the maintenance personnel to check and maintain the underground cable as soon as possible, the fault prediction period can be greatly prolonged, and the prediction is no longer limited to a short period, so as to provide sufficient prevention time for the maintenance work of the underground cable. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is a flowchart of a running state monitoring method of a high-voltage cable provided by the embodiment of the application.

[0044] Figure 2 is a schematic diagram of window sliding monitoring data provided by the embodiment of the application

[0045] Figure 3 is a structural schematic diagram of a running state monitoring system of a high-voltage cable provided by the embodiment of the application.

[0046] Figure 4 is a structural schematic diagram of an electronic device provided by the embodiment of the application.

[0047] Explanation of reference numerals: 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 DESCRIPTION

[0048] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0049] Due to the particularity of the installation environment of the underground cable, the operation state monitoring of the underground cable often depends on the online monitoring equipment. However, in the long-term operation process, problems such as high temperature, high humidity, accumulation of harmful gases, and water immersion may occur in the operation environment of the underground cable, thereby causing the monitoring data detected by the online monitoring equipment to be inaccurate. Although the cable fault prediction algorithm can realize noise reduction or denoising, due to the large amount of cable monitoring data, as the fault prediction period is lengthened, the data amount will become very large, resulting in a significant reduction in prediction efficiency, and therefore only short-term prediction of the cable fault can be realized, which greatly shortens the maintenance time left for the maintenance personnel, thereby increasing the difficulty of cable maintenance work.

[0050] Therefore, in order to solve this problem, the present application provides an operation state monitoring method of a high-voltage cable, which is applied to a cable monitoring system, as shown in Figure 1 The method comprises steps S101 to S104, and the steps are as follows:

[0051] S101, identifying an abnormal time point of monitoring data of the underground cable.

[0052] In the above steps, the monitoring data of the underground cable is a kind of continuous time series data, which contains multiple data types, such as vibration, cable circulating current, cable current, cable voltage, resistance, etc. When the cable itself is abnormal or the environment changes, the monitoring data of the underground cable will fluctuate, at this time, the present application identifies the time point when the fluctuation starts as the abnormal time point, so as to capture the complete development trend of the fault. When identifying the abnormal time point, since the stability of different types of monitoring data is different, the present application identifies the abnormal time points of the monitoring data of multiple monitoring types, and then selects the abnormal time point closest to the current time point from the multiple abnormal time points as the abnormal time point used for subsequent analysis, thereby ensuring the timeliness of the monitoring data.

[0053] The manner of identifying the abnormal time point is specifically: first, the monitoring data of the underground cable is segmented by a sliding window to obtain a plurality of monitoring data sequences, wherein the window size is determined by the fluctuation degree of the monitoring data, that is, the coefficient of variation of the monitoring data is calculated, then the coefficient of variation is matched with a preset sliding window range table to obtain the window size of the current monitoring data. It needs to be noted that the more intense the fluctuation degree is, the larger the window range is, so as to cover the complete feature evolution process, and the more gentle the fluctuation degree is, the smaller the window range is, so as to capture more detailed stable features.

[0054] Then a plurality of statistical characteristic quantities corresponding to the plurality of monitoring data sequences are calculated, wherein the statistical characteristic quantities include time domain features and frequency domain features of the monitoring data, wherein the time domain features include but are not limited to variance, mean, skewness, and the frequency domain features include but are not limited to main frequency and secondary frequency, so as to comprehensively cover the explicit features and implicit features of the monitoring data. Then, a feature distance function is used to calculate the feature distance corresponding to each of the plurality of monitoring data sequences. It needs to be noted that for any one monitoring data sequence, the feature distance is calculated with the adjacent next monitoring data sequence, for example, as shown in the following formula (1): Figure 2 Figure 2 A schematic diagram of window sliding monitoring data provided by the embodiment of the application is shown in the following formula (2):

[0055] The feature distance function is shown in the following formula (3):

[0056]

[0057] Wherein, D t is the feature distance between the tth monitoring data sequence and the (t-1)th monitoring data sequence, W t is the timeliness weight of the tth monitoring data sequence, F t,i is the ith time domain statistical characteristic quantity of the tth monitoring data sequence, F t-1,i is the ith time domain statistical characteristic quantity of the (t-1)th monitoring data sequence, n is the total number of statistical characteristic quantities. S t,i is the ith frequency domain statistical characteristic quantity of the tth monitoring data sequence, S t-1,i is the ith frequency domain statistical characteristic quantity of the (t-1)th monitoring data sequence, is the importance weight of the time domain statistical characteristic in the feature distance, is the importance weight of the frequency domain statistical characteristic in the feature distance, n is the total number of time domain statistical characteristics, and m is the total number of frequency domain statistical characteristics.​

[0058] In the above formula, for two adjacent monitoring data sequences, the Euclidean distance is used to calculate the feature distance of the two, and the plurality of statistical characteristic quantities include both time domain statistical characteristic quantities and frequency domain statistical characteristic quantities, and the frequency domain statistical characteristic quantities can express implicit characteristics, so in order to more accurately calculate the feature distance between two adjacent monitoring data sequences, the importance weight of the time domain statistical characteristic quantity in the feature distance , the importance weight of the frequency domain statistical characteristic quantity in the feature distance , and =1; in addition, since the monitoring data has timeliness, the feature distance of each monitoring data sequence in the above formula also needs to be multiplied by a timeliness weight for adaptive adjustment, so as to conform to the actual situation of the monitoring data. Specifically, the farther the monitoring data sequence is from the current time point, the lower the timeliness weight.

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

[0060] S102, taking the time period between the anomaly time point and the current time point as a fault prediction time window.

[0061] S103, calculating the high-frequency fractal dimension and the low-frequency fractal dimension of the monitoring data in 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 a preset fractal dimension threshold, a fault prompt is issued to remind maintenance personnel to check and maintain the underground cable.

[0063] In the above steps S102 to S104, after the anomaly time point is determined, the time period of the anomaly occurrence is confirmed. At this time, the time period between the anomaly time point and the current time point is taken as a fault prediction time window, so as to adaptively adjust the fault prediction time range. At this time, not only is the fault prediction period extended, but also more comprehensive and effective analysis data is provided for fault prediction.

[0064] ​​​Then, since the fluctuation of the monitoring data of the underground cable may not be caused by the cable itself, but also may be caused by the change of the underground environment, only relying on analyzing the fluctuation degree of the data in the fault prediction time window cannot effectively determine whether the cable itself is abnormal, thereby causing false alarm. At this time, in order to improve the reliability of the cable itself abnormality judgment, the high-frequency fractal dimension and the low-frequency fractal dimension of the monitoring data in the fault prediction time window are calculated, wherein the high-frequency fractal dimension is the fractal dimension of the high-frequency signal, and the low-frequency fractal dimension is the fractal dimension of the low-frequency signal. It needs to be explained that the fractal dimension represents the complexity of the data, the high-frequency fractal dimension reflects the complexity of the cable monitoring data in the local, and the low-frequency fractal dimension reflects the complexity of the cable monitoring data in the whole. The interference factors caused by the change of the environment are irregular and sudden, and therefore mainly reflect the fractal dimension of the high-frequency signal.

[0065] However, since the cable will be aged in the long-term use process, the aging of the cable in the local meets the use standard, but the influence of the aging on the current will be continuously added, thereby causing large fluctuation on the certain power transmission line. At this time, only the threshold value judgment of the high-frequency fractal dimension will cause the possibility of misjudgment. In addition, for the low-frequency fractal dimension, the continuous small interference will cause the continuous rise of the low-frequency fractal dimension, but the influence on the cable is not large, and also easily causes the possibility of misjudgment. Therefore, the present application considers the problem, and calculates the difference between the high-frequency fractal dimension and the low-frequency fractal dimension, so as to adapt to the continuous change of the high-frequency fractal dimension and the low-frequency fractal dimension. It needs to be explained that in the case of many interference factors, if the cable is in normal operation, the high-frequency fractal dimension will change but gradually stabilize, but the low-frequency fractal dimension will continuously increase, and the difference between the two will continuously decrease. If the running state of the cable is abnormal, the high-frequency signal will have transient change, and the fluctuation degree will become very violent, and the influence of the fluctuation of the low-frequency signal is small, thereby causing the difference between the two to become large. 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 value, it means that the probability of the current abnormality of the cable is high, and at this time, the fault prompt is sent to remind the maintenance personnel to quickly check and maintain the underground cable.

[0066] In a possible implementation, due to the influence of factors such as aging, environment, load fluctuation and the like, the monitoring data of the underground cable presents nonlinear and non-stationary characteristics, so that the distribution law of high-frequency signals and low-frequency signals is very chaotic. Therefore, the monitoring data in the fault prediction time window is decomposed by the present application to directly extract IFM components and residual components of different time scales from the signals, wherein the IFM components represent different oscillation modes of the signals from high frequency to low frequency, and the residual components represent the overall trend of the signals; since the early signals of the cable fault are high-frequency fluctuations, the first IFM component after the empirical mode decomposition is selected as the high-frequency signal used for subsequent fault analysis to improve the accuracy of the cable fault judgment; then, the box counting method is used to calculate a plurality of minimum box numbers of the IFM component, wherein the box counting method is a routine technical means for those skilled in the art, and will not be described in detail here. At this time, it needs to be explained that in the case of more environmental interference factors, if the cable is in an abnormal state at this time, the fault characteristics of the cable are not obvious compared with the environmental noise. Therefore, in order to highlight the fault characteristics of the cable, the plurality of minimum box numbers of the IFM component are modified before calculating the high-frequency fractal dimension, the key change points are highlighted, the significance of the fault characteristics is enhanced, the high-frequency fractal dimension focuses more accurately on the high-value fault information, and the interference of irrelevant fluctuations is reduced. Specifically, the monitoring data in the fault prediction time window is subjected to a derivative operation to obtain a gradient curve, wherein the gradient curve reflects the change rate of the monitoring data at each time point; then, according to the gradient curve, the weight of each data point in the monitoring data is calculated, and the calculation method is as follows:

[0067]

[0068] wherein, is the weight of the i th data point, is the gradient value of the i th data point.

[0069] In the above formula, the form of the exponential function makes the data points with large gradient values have larger weights, so as to highlight the key change points.

[0070] After determining the weight of the data points of the monitoring data, the plurality of minimum box numbers of the IFM component are modified to obtain a plurality of minimum modified box numbers. For example, if there is a minimum box number with a box number of 3 in the plurality of minimum box numbers of the IFM component, the maximum weight value of a plurality of data points in each box is selected as the weight value of the box, and then the weight values of the three boxes are added to obtain the modified minimum box number.

[0071] Finally, the plurality of minimum modified box numbers and the fractal scales corresponding to the plurality of minimum modified box numbers are taken logarithm, and curve fitting is performed, at this time, the slope of the fitting curve is the high-frequency fractal dimension.

[0072] In a possible implementation, since the residual component represents the overall trend of the signal, the low-frequency fractal dimension of the monitoring data can be determined according to the residual component, and the residual component has less local geometric details, the waveform presents a slowly varying trend or periodic oscillation, and the energy is distributed in a long time scale, so that the box counting method is difficult to capture the change; therefore, the present application is directed to the data characteristics of the residual component, a plurality of time scales are preset, then the extreme points in the plurality of time scales are counted, and the extreme point density is calculated, finally the plurality of time scales and the extreme point density corresponding to the plurality of time scales are respectively taken logarithm, and curve fitting is performed, at this time the slope of the fitting curve is the low-frequency fractal dimension, the method focuses on the change law of the fluctuation frequency of data with time, so as to quantify the intensive degree of the fluctuation trend of the low-frequency signal, thereby highlighting the long-term change trend of the data.

[0073] In a possible implementation, in order to improve the maintenance efficiency of the maintenance personnel, the fault prompt includes the abnormal position, the abnormal level and the maintenance requirement.

[0074] With reference to Figure 3 , the present application also provides a running state monitoring system of a high-voltage cable, the system is a cable monitoring system, and the system comprises an acquisition module 1, a processing module 2 and a warning module 3, wherein:

[0075] The acquisition module 1 is configured to identify an abnormal time point of monitoring data of an underground cable.

[0076] The processing module 2 is configured to take a time period between the abnormal time point and a current time point as a fault prediction time window, and calculate a high-frequency fractal dimension and a low-frequency fractal dimension of the monitoring data in the fault prediction time window.

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

[0078] It should be noted that, when the device provided in the above embodiments implements its functions, the above-described division of each functional module is used as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above-described functions. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be described here.

[0079] The present application also discloses an electronic device. With reference to Figure 4 , Figure 4is a structural schematic diagram of an electronic device disclosed by an embodiment of the present application. The electronic device 400 can 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 configured to realize connection and communication between the components.

[0081] The user interface 403 can include a display and a camera. Optionally, the user interface 403 can further include a standard wired interface and a wireless interface.

[0082] The network interface 404 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0083] The processor 401 can include one or more processing cores. The processor 401 is connected to various parts of the server through various interfaces and lines, and performs various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 401 can be integrated with a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU is mainly used to process an operating system, a user interface, and an application program. The GPU is used to render and draw the content to be displayed on the display. The modem is used to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 401, but can be implemented by a separate chip.

[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 several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments is merely illustrative, and the division of units can be changed according to actual conditions, such as a combination or integration of some units, or a distribution of some features to other systems, or some features can be ignored, or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0088] The units described as separate components may or can not be physically separate, and the components shown as units may or can not be physical units, i.e. may be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0089] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0090] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable memory. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: U disk, mobile hard disk, magnetic disk or optical disk, and various media that can store program codes.

[0091] The above is only exemplary embodiments of the present disclosure, which cannot limit the scope of the present disclosure. Any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will easily think of other embodiments of the present disclosure after considering the specification and the true disclosure.

[0092] The present application is intended to cover any variations, uses or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present 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; Calculate the high-frequency fractal dimension and low-frequency fractal dimension of the monitoring data within the fault prediction time window; 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 Let S be the i-th time-domain statistical feature of the (t-1)-th monitoring data sequence, where n is the total number of statistical features. 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 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.

5. The method according to claim 4, 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.

6. The method according to claim 4, 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.

7. 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.

8. A high-voltage cable operation status monitoring system, characterized in that, The system is a cable monitoring system, which 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.

9. 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 7.

10. 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 7.

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