A method and system for monitoring the operation of insulated cables

Abstract

This invention relates to the field of cable measurement technology, specifically disclosing a method and system for monitoring the operation of insulated cables. The method includes the following steps: acquiring the current sequence of signals from each monitoring point; the signals include vibration signals and current signals of the insulated cable; calculating the anomaly score of the current sequence at each monitoring point, and identifying the moment when the anomaly score exceeds the corresponding monitoring point's improved threshold as the anomaly moment for that monitoring point; and locating the fault point using a time-difference positioning method based on the anomaly moment and location of each monitoring point. This invention enables timely detection of faults in insulated cables, preventing economic losses.

Description

Technical Field

[0001] This invention relates to the field of cable measurement technology, and specifically to a method and system for monitoring the operation of insulated cables. Background Technology

[0002] An insulated cable is a type of cable that uses insulating materials to protect the wires. It typically consists of a conductor and an insulation layer. The conductor is the medium for current transmission, and the insulation layer isolates the conductor from the external environment. After the insulated cable is laid, operational monitoring is required to ensure its safe and stable operation. Traditional insulated cable operation monitoring mainly relies on manual inspections and periodic maintenance, or using sensors to monitor physical parameters such as cable temperature and partial discharge to determine if there are any faults in the cable's operation.

[0003] Using sensors to detect cable operating parameters for fault detection not only saves manpower and financial resources but also improves the accuracy of fault detection, making this method widely used. Specifically, this method typically involves installing multiple sensors on the cable, comparing the parameters collected by the sensors with fixed thresholds, and then determining whether a cable fault has occurred.

[0004] In actual cable laying, multiple insulated cables are usually laid together. When energized, these cables generate an electric field around them. The electric fields generated by multiple insulated cables superimpose, resulting in a stronger electric field where the cables are denser and a weaker field where the cables are sparser. Since there is a certain distance between the sensor and the fault point, the fault signal generated at the fault point will be affected by the electric field when it reaches the sensor, causing the fault signal to attenuate. The stronger the electric field, the more severe the signal attenuation.

[0005] Because the fault signal is attenuated, the signal collected by the sensor is less than the actual fault signal, which makes the fault signal less than a fixed threshold. As a result, the fault signal is judged as a normal signal, and the fault cannot be detected in time. This may lead to serious accidents such as insulation layer breakdown and fire, resulting in economic losses. Summary of the Invention

[0006] This invention provides a method and system for monitoring the operation of insulated cables, aiming to solve the technical problem of untimely detection of faults in insulated cables in the prior art.

[0007] The present invention provides a method for monitoring the operation of insulated cables, characterized by comprising the following steps:

[0008] Obtain the current sequence of each signal at each monitoring point; the signals include vibration signals and current signals of the insulated cable;

[0009] Calculate the anomaly score of the current sequence at each monitoring point, and take the moment when the anomaly score is greater than the improvement threshold of the corresponding monitoring point as the anomaly moment of the corresponding monitoring point;

[0010] The anomaly score is obtained by weighting the anomaly degree of each signal using its weights; the weights are positively correlated with the signal quality of the current sequence of the corresponding signal; the signal quality is the product of the signal-to-noise ratio and accuracy of the corresponding signal; the improved threshold is positively correlated with the initial threshold and inversely correlated with the signal propagation density of the corresponding monitoring point; the signal propagation density is inversely correlated with the mean vertical distance between the corresponding monitoring point and each target insulated cable, and inversely correlated with the mean Euclidean distance between the corresponding monitoring point and the nearest preset number of monitoring points; the target insulated cable is the remaining insulated cable excluding the insulated cable where the corresponding monitoring point is located.

[0011] The fault location is determined by using the time difference positioning method based on the abnormal time and location of each monitoring point.

[0012] In the above scheme, the initial threshold is corrected according to the signal propagation density of the corresponding monitoring point to obtain an improved threshold, which can prevent the electric field generated by the insulated cable from affecting the signal propagation, thereby timely detecting faults and avoiding economic losses.

[0013] Preferably, the monitoring point signal propagation density for:

[0014] ;

[0015] In the formula, For monitoring points With the nearest Euclidean distance between monitoring points For monitoring points With the The vertical distance between the target insulated cables, For the preset quantity, The total number of target insulated cables. For the natural constant An exponential function with base 1.

[0016] In the above scheme, by calculating the Euclidean distance between the corresponding monitoring point and each of the nearest monitoring points, as well as the perpendicular distance to each target insulated cable, the signal propagation density of the monitoring point can be reflected, and the calculation results are relatively accurate.

[0017] Preferably, the degree of abnormality is the ratio of the product of the similarity between the current sequence and the historical fault sequence in the time domain and the frequency domain to the product of the similarity between the current sequence and the historical normal sequence in the time domain and the frequency domain; wherein, the historical fault sequence and the historical normal sequence are sequences of the corresponding signal that are respectively in the historical fault period and the historical normal period and are of the same length as the current sequence.

[0018] In the above scheme, the degree of signal abnormality is characterized by comparing the similarity between the current sequence of the signal and the historical fault sequence and the historical normal sequence in the time domain and frequency domain. This can comprehensively reflect the degree of signal abnormality and make the calculation results more accurate.

[0019] Preferably, the EMD method is used to decompose the current sequence, historical fault sequence, and historical normal sequence of each signal into multiple component sequences; the historical fault sequence and historical normal sequence are set as target sequences; then the similarity between the current sequence and the target sequence of any signal in the time domain is... for:

[0020] ; For the current sequence of the corresponding signal, the first... Each component sequence matches a component sequence in the target sequence. Distance, the matching component sequence is with the first The component sequence with the smallest absolute value of the difference in the average frequencies of its component sequences. This represents the total number of component sequences in the current sequence. For the natural constant An exponential function with base 1.

[0021] In the above scheme, by comparing the component sequences of the current sequence and the target sequence, it is easier to capture the similar components of the current sequence and the target sequence, making the calculation results more accurate.

[0022] Preferably, the historical fault sequence and the historical normal sequence are set as the target sequence; then the similarity between the current sequence and the target sequence of any signal in the frequency domain is... for:

[0023] ;

[0024] In the formula, This represents the total number of frequencies in the union of the frequencies of the target sequence and the current sequence of the corresponding signal. For the stated and concentrated first The amplitude of each frequency in the spectrum of the target sequence. For the stated and concentrated first The amplitude of each frequency in the spectrum of the current sequence. For the natural constant An exponential function with base 1.

[0025] In the above scheme, by comparing the amplitudes of the current sequence and the target sequence at the same frequency, the similarity between the current sequence and the target sequence in the frequency domain can be reflected, making the calculation results more accurate.

[0026] Preferably, the weight of each signal is a value normalized to the quality of the corresponding signal, and the sum of the weights of all signals is 1.

[0027] Preferably, the monitoring point Improved threshold for:

[0028] ;

[0029] In the formula, As the initial threshold, For monitoring points The signal propagation density.

[0030] In the above scheme, the improved threshold is obtained by correcting the initial threshold through signal propagation density. The calculation is simple and easy to understand.

[0031] Preferably, each monitoring point is equipped with a triaxial accelerometer and a high-frequency current transformer. The triaxial accelerometer is used to collect vibration signals, and the high-frequency current transformer is used to collect current signals.

[0032] In the above scheme, the triaxial accelerometer has the advantages of high accuracy and good stability. The high-frequency current transformer has the advantages of high sensitivity, high accuracy, fast response speed, simple structure, small size, light weight, and easy installation and maintenance.

[0033] Preferably, the monitoring points are evenly arranged along the length of the insulated cable.

[0034] The present invention also provides an insulated cable operation monitoring system, including a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the insulated cable operation monitoring method described in any of the above claims.

[0035] The beneficial effects are:

[0036] The present invention obtains an improved threshold by calculating the signal propagation density at monitoring points, which can prevent signal attenuation during propagation, thus preventing the failure to detect faults in a timely manner and resulting in economic losses. Furthermore, the anomaly score of the current sequence at each monitoring point is obtained by weighting the anomaly degree of each signal using their respective weights, which can comprehensively consider the anomaly degree of each signal, making the calculation results more accurate. Attached Figure Description

[0037] Figure 1This is a flowchart illustrating the steps of the insulated cable operation monitoring method according to an embodiment of the present invention;

[0038] Figure 2 This is a structural block diagram of the insulated cable operation monitoring system according to an embodiment of the present invention. Detailed Implementation

[0039] The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the invention, and should not be construed as limiting the invention.

[0040] like Figure 1 As shown, according to a first aspect of the present invention, a method for monitoring the operation of an insulated cable is provided, comprising the following steps:

[0041] S1. Obtain the current sequence of each signal at each monitoring point. The signals include vibration signals and current signals from the insulated cable.

[0042] The most common fault in insulated cables is partial discharge. Partial discharge refers to a discharge phenomenon occurring in a localized area within the cable insulation, typically at defects in the insulation layer, such as bubbles, cracks, or impurities. Partial discharge can lead to oxidation, decomposition, and breakdown of the insulation layer, reducing the insulation performance of the cable and even causing accidents such as short circuits or fires. Therefore, it is crucial to promptly locate the partial discharge point, i.e., the fault location, for repair to prevent significant economic losses.

[0043] Since partial discharge is a continuous process, it generates abnormal current. Furthermore, according to existing technology, partial discharge can also cause abnormal vibrations in insulated cables. Therefore, this invention detects vibration and current signals generated by partial discharge in insulated cables to determine whether a partial discharge fault exists in the cable, locates the fault point, and achieves operational monitoring of the insulated cable.

[0044] This invention features multiple monitoring points evenly arranged along the length of the insulated cable, each equipped with a sensor to collect vibration and current signals. For example, vibration signals are collected using a triaxial accelerometer, and current signals are collected using a high-frequency current transformer. The triaxial accelerometer offers advantages such as high accuracy and good stability. The high-frequency current transformer offers advantages such as high sensitivity, high accuracy, fast response, simple structure, small size, light weight, and ease of installation and maintenance.

[0045] S2. Calculate the anomaly score of the current sequence at each monitoring point, and take the moment when the anomaly score is greater than the improvement threshold of the corresponding monitoring point as the anomaly moment of the corresponding monitoring point.

[0046] Those skilled in the art will know that insulated cables are typically laid in multiple strands together. When an insulated cable is energized, it generates an electric field around it. The electric fields generated by multiple insulated cables also superimpose. Due to terrain or environmental influences, the density of insulated cables varies at different locations. When the insulated cables are densely distributed, the superimposed electric field strength is higher; when the insulated cables are sparsely distributed, the superimposed electric field strength is lower. When a sensor collects a signal, the signal emitted from a fault point is hindered by the electric field as it propagates to the sensor, causing signal attenuation. Because existing technologies use a fixed threshold for each fault point, the attenuated signal may be lower than the fixed threshold, leading to faulty signals being mistaken for normal signals and preventing timely fault detection. Therefore, this invention addresses the problems in existing technologies by improving the initial threshold to obtain an improved threshold. Therefore, step S2 further includes the following steps:

[0047] S21. Obtain the improved threshold for each monitoring point.

[0048] The improved threshold is positively correlated with the initial threshold and inversely correlated with the signal propagation density of the corresponding monitoring point. This is because the degree of influence on signal propagation varies at different monitoring points, therefore the threshold for each monitoring point should also be different. Furthermore, the greater the degree of influence on signal propagation, the smaller the threshold should be at the corresponding monitoring point. This invention uses signal propagation density to characterize the degree of influence of the corresponding monitoring point on signal propagation. Therefore, the improved threshold is inversely correlated with the signal propagation density of the corresponding monitoring point. Step S21 then further includes the following steps:

[0049] S211. Calculate the signal propagation density at each monitoring point.

[0050] The signal propagation density is inversely correlated with the mean vertical distance between the corresponding monitoring point and each target insulated cable, and also inversely correlated with the mean Euclidean distance between the corresponding monitoring point and the nearest preset number of monitoring points. The target insulated cables are all insulated cables excluding the one containing the corresponding monitoring point. This is because a larger mean vertical distance between the corresponding monitoring point and each target insulated cable indicates a greater distance between the monitoring point and the target insulated cables, resulting in sparser insulated cables at the monitoring point and thus a lower superimposed electric field strength, leading to a smaller signal propagation density. Conversely, a smaller mean Euclidean distance between the corresponding monitoring point and the nearest preset number of monitoring points indicates a closer proximity to the other monitoring points, potentially leading to redundant monitoring points and reducing the accuracy of using the mean vertical distance between the monitoring point and each target insulated cable to characterize the signal propagation density. Therefore, this invention also uses the mean Euclidean distance between the corresponding monitoring point and the nearest preset number of monitoring points to correct the signal propagation density and improve the accuracy of the calculation results.

[0051] In one embodiment, monitoring point signal propagation density for:

[0052] ;

[0053] In the formula, For monitoring points With the nearest Euclidean distance between monitoring points For monitoring points With the The vertical distance between the target insulated cables, For the preset quantity, The total number of target insulated cables. For the natural constant An exponential function with base 1.

[0054] In step S211, the signal propagation density is characterized by the average vertical distance between the corresponding monitoring point and each target insulated cable, and the signal propagation density is corrected by the average Euclidean distance between the corresponding monitoring point and the nearest preset number of monitoring points, so that the calculation results are more accurate.

[0055] S212, Obtain the improved threshold for each monitoring point.

[0056] In one embodiment, the monitoring point Improved threshold for:

[0057] ;

[0058] In the formula, As the initial threshold, For monitoring points The signal propagation density. The initial threshold is an empirically set threshold.

[0059] In step S212, the initial threshold is corrected based on the signal propagation density of the monitoring point to obtain an improved threshold, which is not only simple to calculate but also easy to interpret and understand.

[0060] S22. Obtain the anomaly score of the current sequence at each monitoring point.

[0061] The anomaly score is obtained by weighting the anomaly degree of each signal according to its weight. This is because the signals include vibration signals and current signals, and the anomaly degree of the two signals is different, as is the weight given to each signal. Therefore, step S2 also includes the following steps:

[0062] S221. Calculate the weight of each signal.

[0063] Signal weights characterize the importance of a given signal; signals of higher importance should be assigned higher weights. The weights are positively correlated with the signal quality of the current sequence of the corresponding signal. Signal quality is the product of the signal-to-noise ratio (SNR) and accuracy. The SNR is the ratio of the useful signal strength to the noise strength, used to measure the quality of signal transmission or processing. A higher SNR indicates better signal quality, and therefore, a higher weight should be assigned to that signal. Accuracy characterizes the accuracy with which the anomaly time of a monitoring point is obtained when using only vibration or current signals. Higher accuracy also warrants a higher weight.

[0064] The weight of each signal is the value after normalizing the quality of the corresponding signal, and the sum of the weights of all signals is 1.

[0065] In one embodiment, the first Weight of each signal for:

[0066] ;

[0067] In the formula, For the first The signal-to-noise ratio of the current sequence of a signal. For the first The accuracy of the current sequence of a signal.

[0068] In S221, the signal weights are characterized by the signal-to-noise ratio and accuracy of the current sequence of the signal, so that the importance of each signal can be truly reflected, thereby improving the accuracy of anomaly score calculation.

[0069] S222. Calculate the degree of abnormality of each signal.

[0070] The degree of anomaly is the ratio of the product of the similarity between the current sequence and the historical fault sequence in the time and frequency domains to the product of the similarity between the current sequence and the historical normal sequence in the time and frequency domains. Here, the historical fault sequence and the historical normal sequence of each signal are sequences of the same length as the current sequence. The current sequence is a sequence of a preset duration prior to the current time of the corresponding signal; the historical fault sequence is a sequence of a preset duration extracted from the history of the corresponding signal when a fault occurred; and the historical normal sequence is a sequence of a preset duration extracted from the history of the corresponding signal when there was no fault.

[0071] The higher the similarity between the current sequence and historical fault sequences, the more abnormal the current sequence is, i.e., the higher the degree of abnormality. Conversely, the higher the similarity between the current sequence and historical normal sequences, the more normal the current sequence is, i.e., the lower the degree of abnormality. Therefore, step S222 also includes the following steps:

[0072] First, calculate the similarity in the time domain between the current sequence and historical fault sequences and historical normal sequences.

[0073] In one embodiment, the EMD method is used to decompose the current sequence, historical fault sequence, and historical normal sequence of each signal into multiple component sequences. The EMD method is an empirical mode decomposition method, a fully adaptive signal processing method that can decompose a time-series sequence into multiple intrinsic mode functions, i.e., component sequences. The similarity between the current sequence and the historical fault and historical normal sequences in the time domain is reflected by comparing the similarity of the component sequences in the time domain.

[0074] Specifically, historical fault sequences and historical normal sequences are set as target sequences; then the similarity between the current sequence and the target sequence of any signal in the time domain is... for:

[0075] ;

[0076] In the formula, For the current sequence of the corresponding signal, the first... Each component sequence matches a component sequence in the target sequence. Distance, the matching component sequence is with the first The component sequence with the smallest absolute value of the difference in the average frequencies of its component sequences. This represents the total number of component sequences in the current sequence. For the natural constant An exponential function with base 1.

[0077] The instantaneous frequencies of the component sequences can be obtained through Hilbert transform, and the average frequency of each component sequence can be obtained by the ratio of each instantaneous frequency to the total number of instantaneous frequencies.

[0078] In this step, the current sequence, historical fault sequence, and historical normal sequence of each signal are decomposed into multiple component sequences. The similarity of each component sequence in the time domain is compared to characterize the similarity between the current sequence and the historical fault sequence and the historical normal sequence. This makes it easier to capture the similar components of the current sequence, historical fault sequence, and historical normal sequence, and makes the calculation results more accurate.

[0079] In another embodiment, the MEMD method is used to decompose the current sequence, historical fault sequence, and historical normal sequence of each signal into multiple component sequences. The MEMD method is a multivariate empirical mode decomposition method, an extension of the EMD method, capable of decomposing a time series sequence into a specified number of intrinsic mode functions, i.e., component sequences. This means that the current sequence, historical fault sequence, and historical normal sequence are all decomposed into the same number of component sequences, allowing for comparison of the corresponding component sequences of the current sequence and the historical fault and normal sequences. The calculation formula is similar to that using the EMD method and will not be repeated here.

[0080] In other embodiments, the similarity between the current sequence and historical fault sequences and historical normal sequences can be directly compared. For example, the similarity can be calculated using Pearson correlation coefficient and cosine similarity.

[0081] Secondly, the similarity between the current sequence and historical fault sequences and historical normal sequences in the frequency domain is calculated respectively.

[0082] In one embodiment, historical fault sequences and historical normal sequences are set as target sequences. Then, the similarity between the current sequence and the target sequence of any signal in the frequency domain is... for:

[0083] ;

[0084] In the formula, This represents the total number of frequencies in the union of the frequencies of the target sequence and the current sequence of the corresponding signal. For the stated and concentrated first The amplitude of each frequency in the spectrum of the target sequence. For the stated and concentrated first The amplitude of each frequency in the spectrum of the current sequence. For the natural constant An exponential function with base 1.

[0085] In this step, the similarity between the current sequence and the target sequence in the frequency domain is obtained by comparing the amplitudes corresponding to the current sequence and the target sequence at the same frequency, which simplifies the calculation and makes the calculation results more accurate.

[0086] Finally, the degree of abnormality of each signal is obtained.

[0087] In one embodiment, the degree of abnormality of any signal for:

[0088] ;

[0089] In the formula, This represents the similarity in the time domain between the current sequence and the historical fault sequences of the corresponding signals. This represents the similarity in the frequency domain between the current sequence and the historical fault sequences of the corresponding signal. This represents the similarity in the time domain between the current sequence and historical normal sequences of the corresponding signal. This represents the similarity in the frequency domain between the current sequence and the historical normal sequence of the corresponding signal.

[0090] In this embodiment, the degree of abnormality of the corresponding signal is characterized by the similarity between the current sequence and the historical fault sequence of the corresponding signal in the time domain and frequency domain, and the similarity between the current sequence and the historical normal sequence of the corresponding signal in the time domain and frequency domain. Since the comparison is made in both the time domain and the frequency domain, the degree of abnormality of the corresponding signal can be reflected comprehensively and accurately.

[0091] In step S222, the degree of signal anomaly is characterized by the similarity between the current sequence and the target sequence of the corresponding signal in both the time and frequency domains. This comprehensive approach reflects the degree of signal anomaly and makes the calculation results more accurate. Furthermore, when calculating the similarity between the current sequence and the target sequence in the time domain, the similarity of component sequences is used, which makes it easier to capture the similar components between the current sequence and the target sequence, thus resulting in more accurate calculation results.

[0092] In an alternative embodiment, the degree of anomaly of the signal can also be characterized by the variance of the current sequence. If the variance of the current sequence is greater than a set value, it indicates that the current sequence is more discrete and more likely to be abnormal.

[0093] S223. Obtain the anomaly score for each monitoring point.

[0094] In one embodiment, monitoring point Abnormal scores for:

[0095] ;

[0096] In the formula, For the first The weight of each signal, No. The degree of abnormality of each signal.

[0097] In S223, the abnormality score of the monitoring point is obtained by weighting the abnormality of the corresponding signal. Since the abnormality of multiple signals and their weights are combined, the calculation result of the abnormality score is more accurate.

[0098] S23. The moment when the abnormal score of each monitoring point is greater than the improvement threshold of the corresponding monitoring point is taken as the abnormal moment of the corresponding monitoring point.

[0099] In this step, because an improved threshold is used and the anomaly score integrates the degree of anomaly and the importance of multiple signals, the time of anomaly at each monitoring point can be accurately obtained.

[0100] S3. Based on the abnormal time and location of each monitoring point, the time difference positioning method is used to locate the fault point.

[0101] Time-of-flight (TOF) positioning is an existing technology that uses the time difference between the arrival times of sound or electromagnetic waves at two points to determine the location of a point. TOF positioning is typically based on the hyperbolic positioning principle. Specifically, the location of the monitoring point is fixed. The fault point transmits signals to two monitoring points. By calculating the difference in the time of the anomaly between the two monitoring points and the speed of signal propagation, the distance difference between the fault point and the two monitoring points is obtained. This distance difference defines a hyperbola, and the fault point lies on this hyperbola. By increasing the number of monitoring points, multiple hyperbolas can be obtained, and the intersection of the hyperbolas is the fault point. TOF positioning has advantages such as high-precision positioning and strong anti-interference capability.

[0102] In the insulated cable operation monitoring method of the present invention, the initial threshold is corrected by the signal propagation density of the corresponding monitoring point to obtain an improved threshold. This can prevent the influence of the electric field generated by the insulated cable on the signal during propagation, thereby enabling timely detection of faults and avoiding economic losses. Furthermore, the anomaly score of each monitoring point is obtained by weighting the degree of anomaly of each signal, which can comprehensively consider the anomalies of each signal, making the calculation results more accurate.

[0103] like Figure 2 As shown, according to a second aspect of the present invention, an insulated cable operation monitoring system is also provided, the system including a memory and a processor, the processor executing a computer program stored in the memory to implement the insulated cable operation monitoring method described in the first aspect of the present invention.

[0104] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and therefore will not be described in detail here.

[0105] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented by computer-readable / executable instructions stored or otherwise maintained on such a computer-readable medium.

[0106] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A method for monitoring the operation of insulated cables, characterized in that, Includes the following steps: Obtain the current sequence of each signal at each monitoring point; the signals include vibration signals and current signals of the insulated cable; Calculate the anomaly score of the current sequence at each monitoring point, and take the moment when the anomaly score is greater than the improvement threshold of the corresponding monitoring point as the anomaly moment of the corresponding monitoring point; The anomaly score is obtained by weighting the anomaly degree of each signal using its weights; the weights are positively correlated with the signal quality of the current sequence of the corresponding signal; the signal quality is the product of the signal-to-noise ratio and accuracy of the corresponding signal; the improved threshold is positively correlated with the initial threshold and inversely correlated with the signal propagation density of the corresponding monitoring point; the signal propagation density is inversely correlated with the mean vertical distance between the corresponding monitoring point and each target insulated cable, and inversely correlated with the mean Euclidean distance between the corresponding monitoring point and the nearest preset number of monitoring points; the target insulated cable is the remaining insulated cable excluding the insulated cable where the corresponding monitoring point is located. The fault location is determined by using the time difference positioning method based on the abnormal time and location of each monitoring point.

2. The method for monitoring the operation of insulated cables according to claim 1, characterized in that, The monitoring point signal propagation density for: ； In the formula, For monitoring points With the nearest Euclidean distance between monitoring points For monitoring points With the The vertical distance between the target insulated cables, For the preset quantity, The total number of target insulated cables. For the natural constant An exponential function with base 1.

3. The method for monitoring the operation of insulated cables according to claim 1, characterized in that, The degree of abnormality is the ratio of the product of the similarity between the current sequence and the historical fault sequence in the time domain and the frequency domain to the product of the similarity between the current sequence and the historical normal sequence in the time domain and the frequency domain; wherein, the historical fault sequence and the historical normal sequence are sequences of the corresponding signal that are respectively in the historical fault period and the historical normal period and are of the same length as the current sequence.

4. The method for monitoring the operation of insulated cables according to claim 3, characterized in that, The EMD method is used to decompose the current sequence, historical fault sequence, and historical normal sequence of each signal into multiple component sequences; the historical fault sequence and historical normal sequence are set as the target sequence. Then the similarity between the current sequence and the target sequence of any signal in the time domain for: ; The current sequence of the corresponding signal Each component sequence matches a component sequence in the target sequence. Distance, the matching component sequence is with the first The component sequence with the smallest absolute value of the difference in the average frequencies of its component sequences. This represents the total number of component sequences in the current sequence. For the natural constant An exponential function with base 1.

5. The method for monitoring the operation of insulated cables according to claim 3, characterized in that, Set the historical failure sequence and the historical normal sequence as the target sequence; Then the similarity between the current sequence and the target sequence of any signal in the frequency domain for: ； In the formula, This represents the total number of frequencies in the union of the frequencies of the target sequence and the current sequence of the corresponding signal. For the stated and concentrated first The amplitude of each frequency in the spectrum of the target sequence. For the stated and concentrated first The amplitude of each frequency in the spectrum of the current sequence. For the natural constant An exponential function with base 1.

6. The method for monitoring the operation of insulated cables according to claim 1, characterized in that, The weight of each signal is the value after normalizing the quality of the corresponding signal, and the sum of the weights of all signals is 1.

7. The method for monitoring the operation of insulated cables according to claim 1, characterized in that, The monitoring point Improved threshold for: ； In the formula, As the initial threshold, For monitoring points The signal propagation density.

8. The method for monitoring the operation of insulated cables according to claim 1, characterized in that, Each monitoring point is equipped with a triaxial accelerometer and a high-frequency current transformer. The triaxial accelerometer is used to collect vibration signals, and the high-frequency current transformer is used to collect current signals.

9. The method for monitoring the operation of insulated cables according to claim 1, characterized in that, Each monitoring point is evenly distributed along the length of the insulated cable.

10. An insulated cable operation monitoring system, comprising a memory and a processor, characterized in that, The processor executes the computer program stored in the memory to implement the insulated cable operation monitoring method as described in any one of claims 1 to 9.

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