Insulated cable operation supervision method and system

Abstract

The invention relates to the technical field of cable measurement, and particularly discloses an insulated cable operation supervision method and system, and the method comprises the following steps: obtaining the current sequence of each signal of each monitoring point; the signal comprises a vibration signal and a current signal of the insulated cable; calculating the abnormal score of the current sequence at each monitoring point, and taking the moment when the abnormal score is greater than the improved threshold value of the corresponding monitoring point as the abnormal moment of the corresponding monitoring point; and positioning a fault point by adopting a time difference positioning method according to the abnormal moment and position of each monitoring point. According to the scheme, the fault of the insulated cable can be found in time, and economic loss is prevented.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cable measurement, in particular to an insulating cable operation monitoring method and system. BACKGROUND

[0002] The insulating cable is a kind of cable using insulating material to protect the wire, which is usually composed of a conductor and an insulating layer. The conductor is the medium for transmitting current, and the insulating layer isolates the conductor from the outside world. After the laying of the insulating cable is completed, operation monitoring is still needed to ensure the safe and stable operation of the cable. The traditional operation monitoring of the insulating cable mainly relies on manual inspection and regular maintenance, or uses sensors to monitor the temperature, partial discharge and other physical parameters of the cable to determine whether the cable is running normally.

[0003] Using sensors to detect the operating parameters of the cable for fault detection not only saves manpower and financial resources, but also improves the accuracy of fault detection, so this method is widely used. Specifically, this method usually sets multiple sensors on the cable, compares the parameters collected by the sensors with fixed thresholds, and then determines whether the cable has failed.

[0004] In actual insulating cable laying, usually multiple insulating cables are laid together. After the insulating cable is powered on, an electric field will be generated around it. The electric fields generated by multiple insulating cables will superimpose, so that the stronger the electric field is in the place where the cables are distributed more densely, and the weaker the electric field is in the place where the cables are distributed more sparsely. There is a certain distance between the sensor and the fault point, and the fault signal generated at the fault point will be interfered by the electric field when it propagates to the sensor, resulting in attenuation of the fault signal, and the stronger the electric field is, the more serious the attenuation of the fault signal is.

[0005] Due to the attenuation of the fault signal, the signal collected by the sensor is smaller than the actual fault signal, so that the fault signal is smaller than the fixed threshold, and then the fault signal is determined as a normal signal, which cannot timely discover the fault condition, and may cause the insulating layer to be punctured, a fire accident, etc., causing economic losses. SUMMARY

[0006] The present application provides an insulating cable operation monitoring method and system, which aims to solve the technical problem of not timely discovering the fault of the insulating cable in the prior art.

[0007] An insulating cable operation monitoring method according to the present application, characterized in that it comprises the following steps: obtaining the current sequence of each signal at each monitoring point; the signal includes the vibration signal and the current signal of the insulating cable; calculating the abnormal score of the current sequence at each monitoring point, and taking the time point when the abnormal score is greater than the improved threshold of the corresponding monitoring point as the abnormal time point of the corresponding monitoring point; Among them, the anomaly score is obtained by weighting the degree of anomaly of each signal using its weight; the weight is 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 of the vertical distance between the corresponding monitoring point and each target insulated cable, and inversely correlated with the mean of the Euclidean distance between the corresponding monitoring point and the preset number of monitoring points closest to it; the target insulated cable is the remaining insulated cables excluding the insulated cable where the corresponding monitoring point is located; The time difference positioning method is used according to the abnormal time and position of each monitoring point to locate the fault point.

[0008] 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 signal propagation, thereby promptly detecting faults and avoiding economic losses.

[0009] Preferably, the monitoring point Signal propagation density for: ; Where, For monitoring points The nearest The Euclidean distance of the monitoring points, For monitoring points With the The vertical distance between the insulated cables, is the preset number, is the total number of target insulated cables, The natural constant The exponential function of base .

[0010] In the above scheme, by calculating the Euclidean distance between the corresponding monitoring point and the nearest monitoring points and the vertical distance to each target insulated cable, the signal propagation density of the monitoring point can be reflected, and the calculation result is relatively accurate.

[0011] Preferably, the degree of abnormality is the ratio of the product of the similarity between the current sequence and the historical fault sequence of the corresponding signal 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 in which the corresponding signal is in the historical fault period and the historical normal period respectively and has the same length as the current sequence.

[0012] In the above scheme, the abnormality of the signal is characterized by comparing the similarity of the current sequence of the signal with the historical fault sequence and the historical normal sequence in the time domain and frequency domain, which can comprehensively reflect the abnormality of the signal and make the calculation result more accurate.

[0013] 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 respectively; 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 time domain is for: ; is the number of the current sequence of the corresponding signal The component sequence and the matching component sequence in the target sequence distance, the matching component sequence is The component sequence with the smallest absolute value of the difference between the average frequencies of the component sequences is is the total number of component sequences in the current sequence, The natural constant The exponential function of base .

[0014] 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 result more accurate.

[0015] Preferably, the historical fault sequence and the historical normal sequence are set as the target sequence; then the similarity between the current sequence of any signal and the target sequence in the frequency domain is for: ; Where, is the total number of frequencies in the union of the target sequence and the current sequence of the corresponding signal, For the above-mentioned The amplitude of the frequency in the spectrum of the target sequence, For the above-mentioned The amplitude of the frequency in the spectrum diagram of the current sequence, The natural constant The exponential function of base .

[0016] In the above scheme, by comparing the amplitudes corresponding to 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 result more accurate.

[0017] Preferably, the weight of each signal is a value normalized to the corresponding signal quality, and the sum of the weights of all signals is 1.

[0018] Preferably, the monitoring points The improved threshold value is: ; In the formula, is the initial threshold value, is the signal propagation density of the monitoring point .

[0019] In the above scheme, the initial threshold value is corrected by the signal propagation density to obtain the improved threshold value, which is simple to calculate and easy to understand.

[0020] Preferably, a three-axis accelerometer and a high-frequency current transformer are arranged at each monitoring point, the three-axis accelerometer is used to collect vibration signals, and the high-frequency current transformer is used to collect current signals.

[0021] In the above scheme, the three-axis accelerometer has the advantages of high precision and good stability. The high-frequency current transformer has the advantages of high sensitivity, high precision, fast response speed, simple structure, small size, light weight, and easy installation and maintenance.

[0022] Preferably, the monitoring points are uniformly arranged along the length direction of the insulated cable.

[0023] The application also provides an insulated cable operation monitoring system, comprising a memory and a processor, wherein the processor executes a computer program stored in the memory to realize the insulated cable operation monitoring method of any one of the above.

[0024] The beneficial effects are: The scheme of the application obtains the improved threshold value by calculating the signal propagation density of the monitoring point, which can prevent signal attenuation during propagation, cannot timely discover fault conditions, and further causes economic losses. The abnormal score of the current sequence at each monitoring point is weighted according to the abnormal degree of each signal to obtain, which can comprehensively consider the abnormal degree of each signal, so that the calculation result is more accurate. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 is a step flow chart of the insulated cable operation monitoring method of the embodiment of the application; Figure 2 is a structure block diagram of the insulated cable operation monitoring system of the embodiment of the application. DETAILED DESCRIPTION

[0026] The embodiments described below with reference to the drawings are exemplary and are intended to explain the application, and cannot be understood as a limitation of the application.

[0027] As Figure 1As shown, according to the first aspect of the present application, there is provided an insulating cable operation monitoring method, comprising the following steps: S1, acquiring a current sequence of each signal of each monitoring point. The signals include vibration signals and current signals of the insulating cable.

[0028] The most common fault of the insulating cable is partial discharge. The partial discharge of the insulating cable refers to the discharge phenomenon occurring in a partial area of the cable insulation, which usually occurs at the defect position of the insulating layer, such as bubbles, cracks, impurities, etc. Partial discharge can cause oxidation and breakdown of the insulating layer, reduce the insulation performance of the insulating cable, and even cause short circuit or fire accidents of the insulating cable. Therefore, it is necessary to timely locate the partial discharge position, i.e. the fault point position, for maintenance to prevent significant economic losses.

[0029] Since the partial discharge is continuous, it will produce abnormal current. Moreover, according to the prior art, the partial discharge will also cause abnormal vibration of the insulating cable. Therefore, the present application detects the vibration signals and current signals generated due to the partial discharge of the insulating cable to determine whether the insulating cable has a partial discharge fault and locate the fault point, thereby realizing operation monitoring of the insulating cable.

[0030] The present application uniformly arranges a plurality of monitoring points along the length direction of the insulating cable, and each monitoring point is provided with a sensor for collecting vibration signals and current signals. For example, the vibration signals are collected by a three-axis accelerometer, and the current signals are collected by a high-frequency current transformer. The three-axis accelerometer has the advantages of high precision and good stability. The high-frequency current transformer has the advantages of high sensitivity, high precision, fast response speed, simple structure, small size, light weight, and easy installation and maintenance.

[0031] S2, calculating an abnormal score of the current sequence at each monitoring point, and taking a time point at which the abnormal score is greater than an improved threshold value of the corresponding monitoring point as an abnormal time point of the corresponding monitoring point.

[0032] The skilled in the art can know that the insulated cables are usually laid together when laying. After the insulated cables are powered on, an electric field is generated around the insulated cables. The electric fields generated by the multiple insulated cables are also superimposed. Due to the terrain or environmental influence, the density of the insulated cables at different positions is also different. When the insulated cables are densely distributed, the superimposed electric field strength is relatively high, and when the insulated cables are sparsely distributed, the superimposed electric field strength is relatively low. When the sensor collects the signal, the signal emitted by the fault point is hindered by the electric field when propagating to the sensor, resulting in signal attenuation. Since each fault point in the prior art uses a fixed threshold, the attenuated signal may be less than the fixed threshold, and thus the fault signal is determined to be normal, and the fault cannot be found in time. Therefore, the present application improves the initial threshold value to obtain an improved threshold value. Therefore, step S2 further includes the following steps: S21, obtaining the improved threshold value of each monitoring point.

[0033] The improved threshold value is positively correlated with the initial threshold value and inversely correlated with the signal propagation density of the corresponding monitoring point. This is because the influence of the monitoring point on signal propagation is different, and thus the threshold value of the monitoring point should also be different. The greater the influence on signal propagation, the smaller the threshold value of the corresponding monitoring point should be. The present application uses the signal propagation density to represent the influence of the corresponding monitoring point on signal propagation. Therefore, the improved threshold value is inversely correlated with the signal propagation density of the corresponding monitoring point. Step S21 further includes the following steps: S211, calculating the signal propagation density of each monitoring point.

[0034] The signal propagation density is inversely correlated with the mean value of the vertical distance between the corresponding monitoring point and each target insulated cable and the mean value of the 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. This is because the greater the mean value of the vertical distance between the corresponding monitoring point and each target insulated cable, the farther the corresponding monitoring point is from each target insulated cable, and thus the more sparse the insulated cable at the corresponding monitoring point, and thus the lower the superimposed electric field strength, and the smaller the signal propagation density. The smaller the mean value of the Euclidean distance between the corresponding monitoring point and the nearest preset number of monitoring points, the closer the corresponding monitoring point is to the remaining monitoring points, and there may be a redundant monitoring point, which reduces the accuracy of using the mean value of the vertical distance between the corresponding monitoring point and each target insulated cable to represent the signal propagation density. Therefore, the present application also uses the mean value of the Euclidean distance between the corresponding monitoring point and the nearest preset number of monitoring points to correct the signal propagation density, thereby improving the accuracy of the calculation result.

[0035] In one embodiment, the signal propagation density of the monitoring point is: ​ ; In the formula, is the monitoring point and the Euclidean distance of the nearest monitoring point, is the monitoring point and the vertical distance of the target insulated cable, is the preset number, is the total number of target insulated cables, is the exponential function with the natural constant as the base.

[0036] In step S211, the signal propagation density is represented by the average of the vertical distances of the corresponding monitoring point and each target insulated cable, and the signal propagation density is corrected by the average of the Euclidean distances of the corresponding monitoring point and the nearest preset number of monitoring points, so that the calculation result is more accurate.

[0037] S212, obtaining the improved threshold value of each monitoring point.

[0038] In one embodiment, the improved threshold value of the monitoring point is: ; In the formula, is the initial threshold value, is the signal propagation density of the monitoring point . Wherein, the initial threshold value is an empirical threshold value set by man.

[0039] In step S212, the initial threshold value is corrected according to the signal propagation density of the monitoring point to obtain the improved threshold value, which is not only simple to calculate, but also easy to explain and understand.

[0040] S22, obtaining the abnormal score of the current sequence at each monitoring point.

[0041] The abnormal score is obtained by weighting the abnormal degree of each signal by the weight of the signal. This is because the signal includes vibration signal and current signal, and the abnormal degree of the two signals is not the same, and the importance of the two signals, that is, the weight of the corresponding signal, is also not the same. Therefore, step S2 further includes the following steps: S221, calculating the weight of each signal.

[0042] ​The signal weight represents the importance of the corresponding signal. Signals with higher importance should be assigned higher weights. The weight is 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 and accuracy of the corresponding signal. The signal-to-noise ratio refers to the ratio of the useful signal strength to the noise strength and is used to measure the quality of the signal transmission or processing process. The higher the signal-to-noise ratio, the better the signal quality, and the higher the weight should be assigned to the signal. Accuracy represents the accuracy of the abnormal moment of the monitoring point obtained when using the vibration signal or current signal alone. The higher the accuracy, the higher the weight should be assigned to the signal.

[0043] The weight of each signal is the value normalized by the corresponding signal quality, and the sum of the weights of all signals is 1.

[0044] In one embodiment, The weight of the signal for: ; Where, For the The signal-to-noise ratio of the current sequence of signals, For the The accuracy of the current sequence of signals.

[0045] In S221 , the weight of the signal is characterized according to 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 the calculation of the abnormality score.

[0046] S222. Calculate the abnormality level of each signal.

[0047] 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 and frequency domains of the corresponding signal to the product of the similarity between the current sequence and the historical normal sequence in the time and frequency domains. The historical fault sequence and 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 before the current moment of the corresponding signal. The historical fault sequence is a sequence of a preset duration captured from the history of the corresponding signal when a fault occurred. The historical normal sequence is a sequence of a preset duration captured from the history of the corresponding signal when there were no faults.

[0048] The higher the similarity between the current sequence and the historical fault sequence, the more abnormal the current sequence is, that is, the higher the degree of abnormality is. The higher the similarity between the current sequence and the historical normal sequence, the more normal the current sequence is, that is, the lower the degree of abnormality is. Therefore, step S222 also includes the following steps: First, the similarity between the current sequence and the historical fault sequence and the historical normal sequence in the time domain is calculated respectively.

[0049] In one embodiment, the EMD method is used to decompose the current sequence, historical fault sequences, and historical normal sequences of each signal into multiple component sequences. The EMD method, known as empirical mode decomposition (EMD), is a fully adaptive signal processing method that can decompose a time series into multiple intrinsic mode functions (IMFs), i.e., component sequences. The similarity between the current sequence and the historical fault sequences and historical normal sequences in the time domain is reflected by comparing the similarity of the component sequences in the time domain.

[0050] Specifically, the historical fault sequence and the historical normal sequence are set as the target sequence; then the similarity between the current sequence of any signal and the target sequence in the time domain is for: ; Where, is the number of the current sequence of the corresponding signal The component sequence and the matching component sequence in the target sequence distance, the matching component sequence is The component sequence with the smallest absolute value of the difference between the average frequencies of the component sequences is is the total number of component sequences in the current sequence, The natural constant The exponential function of base .

[0051] The instantaneous frequency of the component sequence can be obtained by 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.

[0052] In this step, the current sequence, historical fault sequence, and historical normal sequence of each signal are decomposed into multiple component sequences, and 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, making the calculation results more accurate.

[0053] In another embodiment, the MEMD method is used to decompose the current sequence, historical fault sequences, and historical normal sequences of each signal into multiple component sequences. The MEMD method is a multivariate empirical mode decomposition method, an extension of the EMD method, and can decompose a time series into a specified number of intrinsic mode functions, i.e., component sequences. This means that the current sequence, historical fault sequences, and historical normal sequences can all be decomposed into the same number of component sequences, allowing comparison of the corresponding component sequences of the current sequence, historical fault sequences, and historical normal sequences. The calculation formula is similar to that of the EMD method and will not be repeated here.

[0054] In other embodiments, the similarity between the current sequence and the historical failure sequence, the historical normal sequence can also be directly compared. For example, the Pearson correlation coefficient and the cosine similarity can be used to calculate the similarity.

[0055] Secondly, the similarity between the current sequence and the historical failure sequence, the historical normal sequence in the frequency domain is calculated respectively.

[0056] In one embodiment, the historical failure sequence and the historical normal sequence are set as the target sequence, and the similarity between the current sequence of any signal and the target sequence in the frequency domain is : ; In the formula, is the total number of frequencies in the union of the frequencies of the target sequence and the current sequence of the corresponding signal, is the amplitude of the th frequency in the frequency spectrum of the target sequence in the union, is the amplitude of the th frequency in the frequency spectrum of the current sequence in the union, is the exponential function with the natural constant as the base number.

[0057] In this step, 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 is obtained, so that the calculation is simple and the calculation result is more accurate.

[0058] Finally, the abnormality degree of each signal is obtained.

[0059] In one embodiment, the abnormality degree of any signal is : ; In the formula, is the similarity between the current sequence and the historical failure sequence of the corresponding signal in the time domain, is the similarity between the current sequence and the historical failure sequence of the corresponding signal in the frequency domain, is the similarity between the current sequence and the historical normal sequence of the corresponding signal in the time domain, is the similarity between the current sequence and the historical normal sequence of the corresponding signal in the frequency domain.

[0060] In this embodiment, the abnormality degree of the corresponding signal is represented by the similarity between the current sequence and the historical failure sequence of the corresponding signal in the time domain and the frequency domain, and the similarity between the current sequence and the historical normal sequence of the corresponding signal in the time domain and the frequency domain. Since comparison is made in the time domain and the frequency domain, the abnormality degree of the corresponding signal can be comprehensively and accurately reflected.

[0061] In step S222, the degree of signal anomaly is characterized by the similarity between the current and target sequences in both the time and frequency domains. This comprehensively reflects the degree of signal anomaly, resulting in more accurate calculation results. Furthermore, when calculating the time-domain similarity between the current and target sequences, the component sequence similarity representation is used, making it easier to capture the similarities between the current and target sequences, resulting in more accurate calculation results.

[0062] In an alternative embodiment, the abnormality of the signal may also be represented by the variance of the current sequence. If the variance of the current sequence is greater than a set value, it means that the current sequence has a greater degree of dispersion and is more likely to be abnormal.

[0063] S223. Obtain anomaly scores for each monitoring point.

[0064] In one embodiment, the monitoring point The abnormal score of for: ; Where, For the The weight of the signal, No. The abnormality of a signal.

[0065] In S223, the abnormality degree of the corresponding signal is weighted by its weight to obtain the abnormality score of the monitoring point. Since the abnormality degrees and weights of multiple signals are combined, the calculation result of the abnormality score is more accurate.

[0066] S23. The moment when the abnormal score of each monitoring point is greater than the improved threshold of the corresponding monitoring point is regarded as the abnormal moment of the corresponding monitoring point.

[0067] In this step, since an improved threshold is adopted and the abnormality score integrates the abnormality degree and importance degree of multiple signals, the abnormal time of each monitoring point can be accurately obtained.

[0068] S3. Use the time difference positioning method according to the abnormal time and position of each monitoring point to locate the fault point.

[0069] Time-of-day positioning (TDOT) is a state-of-the-art method that uses the time difference between the arrival of sound or electromagnetic waves at two points to determine the location of a point. TDOT is typically based on the principle of hyperbolic positioning. Specifically, the location of a monitoring point is determined. The fault point transmits a signal to two monitoring points. The difference in the time of the abnormality at the two monitoring points is calculated along with the speed of signal propagation to determine the distance difference between the fault point and the two monitoring points. 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 generated, and the intersection of the hyperbolas is the fault point. TDOT offers advantages such as high-precision positioning and strong anti-interference capabilities.

[0070] In the insulation cable operation supervision method, the initial threshold is corrected by the signal propagation density of the corresponding monitoring point to obtain an improved threshold, which can prevent the influence of the electric field generated by the insulation cable on the signal during signal propagation, and further can discover the fault condition in time to avoid economic losses. The abnormal score of each monitoring point is obtained by weighting the abnormal degree of each signal using the weight of each signal, which can comprehensively consider the abnormal conditions of each signal, so that the calculation result is more accurate.

[0071] As shown in Figure 2 According to the second aspect of the present application, an insulation cable operation supervision system is also provided, which comprises a memory and a processor. The processor executes the computer program stored in the memory to realize the insulation cable operation supervision method of the first aspect of the present application.

[0072] The system also comprises a communication bus and a communication interface and other components well known to those skilled in the art, the settings and functions of which are known in the art, and thus will not be described here.

[0073] In the present application, the aforementioned memory can be any tangible medium containing or storing a program, which can be used by or in conjunction with an instruction execution system, device or apparatus. For example, the computer readable storage medium can be any suitable magnetic storage medium 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) and the like, or any other medium that can be used to store the required information and can be accessed by an application, a module or both. Any such computer storage medium can be part of the device or accessible or connectable to the device. Any application or module described in the present application can be implemented by computer readable / executable instructions stored or otherwise held by such computer readable medium.

[0074] While the specification has illustrated and described various embodiments of the application, it will be clear to those of ordinary skill in the art that various changes, modifications, and substitutions can be made thereto without departing from the spirit and scope of the application. It is understood that in the process of practicing the application, various alternatives, modifications, and equivalents can be employed.

Claims

1. A method for supervising the operation of an insulated cable, characterized in that: The steps include: Acquire a current sequence of each signal at each monitoring point; the signals include a vibration signal and a current signal 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 improved threshold of the corresponding monitoring point as the anomaly moment of the corresponding monitoring point; Among them, the anomaly score is obtained by weighting the degree of anomaly of each signal using its weight; the weight is 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 of the vertical distance between the corresponding monitoring point and each target insulated cable, and inversely correlated with the mean of the Euclidean distance between the corresponding monitoring point and the preset number of monitoring points closest to it; the target insulated cable is the remaining insulated cables excluding the insulated cable where the corresponding monitoring point is located; The time difference positioning method is used according to the abnormal time and position of each monitoring point to locate the fault point.

2. The insulated cable operation supervision method according to claim 1, characterized in that: The monitoring point Signal propagation density for: ； Where, For monitoring points The nearest The Euclidean distance of the monitoring points, For monitoring points With the The vertical distance between the insulated cables, is the preset number, is the total number of target insulated cables, The natural constant The exponential function of base .

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

4. The insulated cable operation supervision method 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 respectively; the historical fault sequence and historical normal sequence are set as the target sequence; The similarity between the current sequence and the target sequence of any signal in the time domain is for: ; is the first signal in the current sequence The component sequence and the matching component sequence in the target sequence distance, the matching component sequence is The component sequence with the smallest absolute value of the difference between the average frequencies of the component sequences is is the total number of component sequences in the current sequence, The natural constant The exponential function of base .

5. The insulated cable operation supervision method according to claim 3, characterized in that: Set the historical fault sequence and the historical normal sequence as the target sequence; The similarity between the current sequence and the target sequence of any signal in the frequency domain is for: ； Where, is the total number of frequencies in the union of the target sequence and the current sequence of the corresponding signal, For the above mentioned The amplitude of the frequency in the spectrum of the target sequence, For the above mentioned The amplitude of the frequency in the spectrum diagram of the current sequence, The natural constant The exponential function of base .

6. The insulated cable operation supervision method according to claim 1, characterized in that: The weight of each signal is the value normalized by the corresponding signal quality, and the sum of the weights of all signals is 1.

7. The insulated cable operation supervision method according to claim 1, characterized in that: The monitoring point Improved threshold for: ； Where, is the initial threshold, For monitoring points signal propagation density.

8. The insulated cable operation supervision method according to claim 1, characterized in that: Each monitoring point is provided 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 insulated cable operation supervision method according to claim 1, characterized in that: The monitoring points are evenly arranged 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 supervision method according to any one of claims 1 to 9.

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