Cable insulation fault detection method and system

Abstract

The invention relates to a cable insulation fault detection method and system. The method comprises the following steps: under the condition that a sound wave generator injects a first sound wave with a preset frequency into a cable, acquiring a first sound wave signal when the first sound wave passes through each position of the cable based on a sensor array; controlling the sound wave generator to inject a second sound wave of a second frequency into the cable based on the fault type of the cable under the condition of determining that the cable has a fault based on the signal analysis result of each first sound wave signal; the second frequency is determined based on a mapping relation between a preset fault type and the sound wave frequency and the fault type; and determining a fault position corresponding to the fault type of the cable based on a second sound wave signal when a second sound wave collected by the sensor array passes through each position. By adopting the method, the cable can be monitored in real time, and the cable detection scene is expanded.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a cable insulation fault detection method and system. Background Art

[0002] In modern power systems and industrial applications, cables are not only the main medium for power transmission, but also an important tool for signal transmission. In order to improve the carrying capacity of cables, double-split cables (i.e., in-phase parallel cables) can be used to transmit power to increase the transmission capacity. When double-split cables are used for power transmission, there will be an imbalance in the current transmission of the two sub-cables running in parallel due to factors of the line itself, such as line differentiation caused by differences in path length and manufacturing process, and impedance asymmetry caused by electromagnetic coupling between multiple cables. When double-split cables work under current imbalance conditions, the probability of failure of the cable's insulation layer will increase. The cable's insulation layer plays a key role in preventing current leakage, protecting the cable conductor from external damage, and ensuring the safe operation of the power system. Therefore, it is very important to detect faults in the cable's insulation layer.

[0003] Generally, electrical testing methods can be used to detect insulation faults in cables, which is to evaluate the state of the insulation layer by measuring the electrical characteristics of the cable. However, this requires the power system to be powered off or disconnected before the cable faults can be detected, making it difficult to monitor the cable in real time and limiting the detection scenarios. Summary of the invention

[0004] Based on this, it is necessary to provide a cable insulation fault detection system, method, device, computer equipment, computer-readable storage medium and computer program product that can monitor cables in real time and expand cable detection scenarios to address the above technical problems.

[0005] In a first aspect, the present application provides a method for detecting cable insulation faults. The method includes: when an acoustic wave generator injects a first acoustic wave of a preset frequency into a cable, acquiring a first acoustic wave signal when the first acoustic wave passes through each position of the cable based on a sensor array; when it is determined that a fault exists in the cable based on the signal analysis results of each first acoustic wave signal, controlling the acoustic wave generator to inject a second acoustic wave of a second frequency into the cable based on the type of fault existing in the cable; the second frequency is determined based on a mapping relationship between a preset fault type and an acoustic wave frequency, and the fault type; and determining a fault position corresponding to the fault type of the cable based on the second acoustic wave signal acquired by the sensor array when the second acoustic wave passes through each position.

[0006] In one embodiment, based on the type of fault in the cable, controlling the sound wave generator to inject a second sound wave of a second frequency into the cable includes: obtaining humidity information and temperature information of the environment in which the cable is located; the humidity information includes a humidity mean and a humidity change rate, and the temperature information includes a temperature mean and a temperature change rate; performing frequency domain analysis on each first sound wave signal to determine each frequency domain feature of each first sound wave; each frequency domain feature includes high-frequency energy, high-frequency peak amplitude and spectrum entropy; based on the high-frequency energy, high-frequency peak amplitude and spectrum entropy of each first sound wave signal, constructing sound wave features respectively, and based on the humidity mean and humidity change rate, and the temperature mean and temperature change rate, constructing environmental features; when the feature correlation between the sound wave feature and the environmental feature is less than a correlation threshold, based on the type of fault in the cable, controlling the sound wave generator to inject a second sound wave of a second frequency into the cable.

[0007] In one embodiment, each frequency domain feature also includes: high-frequency energy ratio and spectrum main frequency; the method also includes: performing time-frequency analysis on each first sound wave signal to determine each time domain feature of each first sound wave signal; each time domain feature includes time domain energy, signal amplitude, maximum signal amplitude, minimum signal amplitude, rising time corresponding to the maximum signal amplitude, and falling time corresponding to the minimum signal amplitude; analyzing each time domain feature of each first sound wave signal to determine the time domain feature trend corresponding to each time domain feature; analyzing each frequency domain feature of each first sound wave signal to determine the frequency domain feature trend corresponding to each frequency domain feature; determining the fault type of the cable based on the matching results of each time domain feature trend and / or each frequency domain feature trend, and the preset fault feature trend corresponding to each preset fault type.

[0008] In one embodiment, at least one of the following items is also included: the first item: when the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, the frequency domain characteristic trend corresponding to the spectrum entropy shows an upward trend, and the frequency domain characteristic trend corresponding to the spectrum main frequency shows a shift trend, the fault type of the cable is determined to be a partial discharge fault; the second item: when the time domain characteristic trend corresponding to the maximum amplitude of the signal shows a downward trend, the time domain characteristic trend corresponding to the time domain energy shows a downward trend, and the time domain characteristic trend corresponding to the rising time or the falling time shows an upward trend, and the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, the fault type of the cable is determined to be an insulation moisture fault; the third item: when the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, the time domain characteristic trend corresponding to the signal amplitude shows a slow downward trend, and the frequency domain characteristic trend corresponding to the spectrum entropy shows an upward trend, the fault type of the cable is determined to be an insulation aging fault; the fourth item: when the time domain characteristic trend corresponding to the signal amplitude shows a fluctuating trend, the time domain characteristic trend corresponding to the high-frequency energy ratio shows an upward trend, and the time domain characteristic trend corresponding to the spectrum main frequency shows a shift trend, the fault type of the cable is determined to be a mechanical damage fault.

[0009] In one embodiment, the sensor array includes multiple sensors; based on the second sound wave signals collected by the sensor array when the second sound wave passes through each position, the fault position corresponding to the fault type of the cable is determined, including: with respect to the second sound wave signals collected by the sensor array when the second sound wave passes through each position, each second sound wave signal is analyzed to determine the sound wave propagation speed matched by the sensor corresponding to the second sound wave signal, and the sound wave propagation distance corresponding to the sound wave propagation speed; based on the sound wave propagation distance matched by each sensor, and the setting position of each sensor, the fault position corresponding to the fault type of the cable is determined.

[0010] In one embodiment, the method also includes: obtaining the first humidity and the first temperature at the location of each sensor; for each sensor, based on the sound wave velocity under standard conditions, the temperature correction coefficient, the humidity correction coefficient, the standard temperature, the standard humidity, and the first humidity and the first temperature, determining an adjusted sound wave propagation velocity that matches the sensor.

[0011] In one embodiment, the method also includes: obtaining a second humidity, a second temperature and a first vibration signal at the fault location; in the case where the fault type is an insulation moisture fault, if the second humidity is greater than the humidity at a position adjacent to the fault location, and the first vibration signal is in an abnormal state, then outputting a first verification pass result; in the case where the fault type is an insulation aging fault, if the second temperature is greater than the temperature at a position adjacent to the fault location, and the first vibration signal is in a normal state, then outputting a second verification pass result.

[0012] In one embodiment, the fault type of the cable is determined based on the matching results of each time domain characteristic trend and / or each frequency domain characteristic trend, and a preset fault characteristic trend corresponding to each preset fault type, including: determining multiple candidate fault types based on the matching results of each time domain characteristic trend and / or each frequency domain characteristic trend, and a preset fault characteristic trend corresponding to each preset fault type; determining the fault type of the cable based on each candidate fault type and a matching degree score corresponding to the candidate fault type.

[0013] In one embodiment, the method further includes: predicting a fault deterioration trend corresponding to the fault location based on the fault characteristics corresponding to the fault location while periodically injecting sound waves into the fault location; and outputting a fault maintenance suggestion matching the fault deterioration trend when a trend value characterizing the fault deterioration trend exceeds a threshold.

[0014] In a second aspect, the present application provides a cable insulation fault detection system, which is applied to the first aspect or any method in the first aspect, and the system includes: an acoustic wave generator, which is used to inject a first acoustic wave of a preset frequency into the cable; a sensor array, which is used to collect first acoustic wave signals when the first acoustic wave passes through various positions of the cable; a controller, which is used to control the acoustic wave generator to inject a second acoustic wave of a second frequency into the cable based on the type of fault in the cable when it is determined that a fault exists in the cable based on the signal analysis results of each first acoustic wave signal, and control the sensor array to collect second acoustic wave signals when the second acoustic wave passes through various positions; the second frequency is determined based on a mapping relationship between a preset fault type and the acoustic wave frequency, and the fault type; the controller is also used to determine a fault position corresponding to the fault type of the cable based on each second acoustic wave signal.

[0015] In the third aspect, the present application also provides a cable insulation fault detection device. The device includes: an acquisition module, which is used to obtain a first sound wave signal when the first sound wave passes through each position of the cable based on the sensor array when the sound wave generator injects a first sound wave of a preset frequency into the cable; a processing module, which is used to control the sound wave generator to inject a second sound wave of a second frequency into the cable based on the type of fault in the cable when it is determined that the cable has a fault based on the signal analysis results of each first sound wave signal; the second frequency is determined based on the mapping relationship between the preset fault type and the sound wave frequency, and the fault type; an analysis module, which is used to determine the fault position corresponding to the fault type of the cable based on the second sound wave signal collected by the sensor array when the second sound wave passes through each position.

[0016] In a fourth aspect, the present application also provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program: when the sound wave generator injects a first sound wave of a preset frequency into the cable, a first sound wave signal when the first sound wave passes through each position of the cable is acquired based on the sensor array; when it is determined that the cable has a fault based on the signal analysis results of each first sound wave signal, the sound wave generator is controlled to inject a second sound wave of a second frequency into the cable based on the type of fault in the cable; the second frequency is determined based on the mapping relationship between the preset fault type and the sound wave frequency, and the fault type; based on the second sound wave signal acquired by the sensor array when the second sound wave passes through each position, the fault position corresponding to the fault type of the cable is determined.

[0017] In a fifth aspect, the present application also provides a computer-readable storage medium. A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented: when the sound wave generator injects a first sound wave of a preset frequency into the cable, a first sound wave signal when the first sound wave passes through each position of the cable is acquired based on the sensor array; when it is determined that the cable has a fault based on the signal analysis results of each first sound wave signal, the sound wave generator is controlled to inject a second sound wave of a second frequency into the cable based on the type of fault in the cable; the second frequency is determined based on the mapping relationship between the preset fault type and the sound wave frequency, and the fault type; based on the second sound wave signal acquired by the sensor array when the second sound wave passes through each position, the fault position corresponding to the fault type of the cable is determined.

[0018] In a sixth aspect, the present application also provides a computer program product. The computer program product includes a computer program, which implements the following steps when executed by a processor: when an acoustic wave generator injects a first acoustic wave of a preset frequency into a cable, a first acoustic wave signal when the first acoustic wave passes through each position of the cable is acquired based on a sensor array; when it is determined that a fault exists in the cable based on the signal analysis results of each first acoustic wave signal, the acoustic wave generator is controlled to inject a second acoustic wave of a second frequency into the cable based on the type of fault existing in the cable; the second frequency is determined based on a mapping relationship between a preset fault type and an acoustic wave frequency, and the fault type; based on the second acoustic wave signal acquired by the sensor array when the second acoustic wave passes through each position, a fault position corresponding to the fault type of the cable is determined.

[0019] The above-mentioned cable insulation fault detection method, system, device, computer equipment, computer-readable storage medium and computer program product, when the sound wave generator injects a first sound wave of a preset frequency into the cable, can obtain the first sound wave signal when the first sound wave passes through each position of the cable based on the sensor array, and when it is determined that the cable has a fault based on the signal analysis results of each first sound wave signal, based on the type of fault in the cable, control the sound wave generator to inject a second sound wave of a second frequency into the cable, and then based on the second sound wave signal collected by the sensor array when the second sound wave passes through each position, the fault position corresponding to the fault type of the cable can be determined. Therefore, the present application does not need to shut down or disconnect the power supply of the power system, and can also realize the insulation fault detection of the cable, solves the problem of limited detection scenarios, and expands the detection scenarios. Moreover, the present application performs insulation fault detection on the cable from the perspective of the sound wave signal, and the sound wave signal has a high sensitivity to the faults existing in the cable, thereby improving the accuracy of cable insulation fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A diagram showing an application environment of a cable insulation fault detection method in one embodiment;

[0021] Figure 2 A schematic diagram of a process of detecting a cable insulation fault in one embodiment;

[0022] Figure 3 A schematic diagram of the structure of a cable insulation fault detection system in one embodiment;

[0023] Figure 4 A schematic diagram of a flow chart of a cable insulation fault detection method in another embodiment;

[0024] Figure 5 is a structural block diagram of a cable insulation fault detection device in one embodiment;

[0025] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0027] Cables are widely used in power systems and industrial fields. The health of their insulation layer plays a vital role in the stability, safety and long-term reliability of power transmission. With the increase in power transmission demand and the increase in system complexity, cable insulation failure has become more and more prominent.

[0028] Generally, the types of cable insulation failures include, but are not limited to: insulation aging, cracks and damage, moisture intrusion, and partial discharge. Insulation aging is used to reflect that as the cable works for a long time, the insulation material gradually loses its physical properties, resulting in a decrease in electrical performance; cracks and damage are used to reflect that external mechanical stress or excessive bending may cause cracks or partial damage to the insulation layer, thereby affecting its insulation capacity; moisture intrusion is used to reflect that the insulation layer may absorb moisture due to the influence of environmental humidity, resulting in uneven distribution of the electric field inside the cable, thereby causing insulation failure; partial discharge is used to reflect that tiny defects in the insulation material may cause partial discharge, thereby accelerating the aging and damage of the insulation layer.

[0029] As mentioned above, electrical testing methods can be used to detect insulation faults in cables, including insulation resistance testing, dielectric loss factor testing, etc. For electrical testing methods, on the one hand, this requires the power system to be powered off or disconnected before cable fault detection can be performed, which makes it difficult to achieve online real-time monitoring and limits the detection scenarios; on the other hand, electrical testing methods have low accuracy in locating fault points, especially in long-distance cable systems, and it is difficult to provide accurate fault location information.

[0030] In some cases, partial discharge detection can also be used to detect insulation faults in cables. Partial discharge detection is a more advanced insulation fault detection method that identifies potential insulation defects by monitoring tiny discharge phenomena in the insulation material. Although partial discharge detection can detect signs of insulation aging or defects at an early stage, this method is extremely sensitive to external electromagnetic interference and is easily affected by environmental noise. In addition, partial discharge detection requires the use of complex high-precision instruments, which is difficult to operate and is not suitable for long-term application in complex or high-noise environments.

[0031] In some cases, infrared thermal imaging technology can also be used to detect insulation faults in cables. Infrared thermal imaging technology indirectly evaluates the state of the internal insulation layer by detecting temperature anomalies on the cable surface. Although this method can quickly detect overheating caused by cable faults, it is essentially a detection of external heat in the cable and cannot deeply detect the specific conditions inside the insulation layer, making it difficult to provide accurate positioning information for the fault. At the same time, thermal imaging is only sensitive to major faults that have already occurred (such as heat caused by severe overload) and cannot detect minor faults in the early stages.

[0032] As can be seen from the above, the existing cable insulation detection methods still have obvious deficiencies in early detection of insulation faults, real-time monitoring and accuracy in complex operating environments. In order to meet these challenges, the detection method based on acoustic wave characteristics has gradually become an important development direction of cable fault detection technology with its high sensitivity, non-invasiveness and applicability in complex environments.

[0033] The detection method based on acoustic wave characteristics evaluates the health of the cable by analyzing the propagation characteristics of acoustic waves in the insulating material. The advantages of the acoustic wave detection method are its non-invasiveness and high sensitivity to subtle defects, especially in insulating materials, where acoustic waves can detect tiny defects inside the material and provide precise fault location information.

[0034] Acoustic emission technology and pulse echo technology are used in the detection method for acoustic wave characteristics. Acoustic emission technology is a non-destructive testing method commonly used to detect microcracks in materials. This method determines its internal defects by capturing the acoustic wave signal (i.e., acoustic emission) generated inside the material. Acoustic emission technology can detect cracks or local damage in the insulation layer, and is suitable for early detection of cable insulation faults. However, this technology relies on high-precision sensors and complex signal processing algorithms, has a low tolerance for noise environments, and has a high implementation cost. Pulse echo technology sends high-frequency sound wave pulses into the cable. When the sound wave encounters a defect, it is reflected. The sensor receives the reflected signal and calculates the location of the fault point by analyzing the time delay of the echo. Although pulse echo technology performs well under laboratory conditions, in actual applications, due to the complex cable environment and the variable sound wave propagation path, the echo signal is easily interfered by external noise and multiple reflections, thus affecting the accuracy of detection.

[0035] From the above, it can be seen that although the detection method based on acoustic wave characteristics is superior in theory, the existing acoustic wave detection technology still faces many challenges in practical applications, such as insufficient accuracy, real-time performance and anti-interference ability. In order to solve the deficiencies in the existing technology in terms of detection accuracy, real-time performance and anti-interference ability in complex environments, an acoustic wave signal of a specific frequency can be injected into the cable, and a distributed sensor array, time domain and frequency domain analysis and machine learning methods can be combined to achieve high-precision detection and accurate positioning of cable insulation faults, which can effectively improve the accuracy and real-time performance of fault detection, so that it can not only identify subtle defects in the cable insulation layer, but also provide early warnings in the early stages of faults, thereby helping operators to take maintenance measures in a timely manner and avoid major faults.

[0036] It should be understood that when the sound wave generator is controlled to inject sound waves into the cable, the sound waves can form propagation waves inside the cable. These waves will be reflected, transmitted and attenuated when encountering defects in the insulation layer. Therefore, they can provide a basic basis for cable insulation fault detection. For example, when the sound wave encounters a fault point in the insulation layer, part of the sound wave signal will be reflected. Therefore, by receiving and analyzing these reflected signals, the location of the fault can be inferred; after the sound wave passes through the fault location, its transmission characteristics (such as frequency, intensity, etc.) will also change. Therefore, by analyzing the transmission signal, the nature and severity of the fault can be further confirmed.

[0037] In view of this, in one embodiment, Figure 1As shown, a cable insulation fault detection system is provided. The cable insulation fault detection system 100 includes a sound wave generator 102, a sensor array 104 and a controller 106. Specifically, the sound wave generator 102 is used to inject a first sound wave of a preset frequency into the cable. The sensor array 104 is used to collect first sound wave signals when the first sound wave passes through various positions of the cable. The controller 106 is used to control the sound wave generator to inject a second sound wave of a second frequency into the cable based on the type of fault in the cable when it is determined that the cable has a fault based on the signal analysis results of each first sound wave signal, and control the sensor array to collect second sound wave signals when the second sound wave passes through various positions. The controller 106 is also used to determine the fault position corresponding to the fault type of the cable based on each second sound wave signal.

[0038] In one embodiment, Figure 2 As shown, a cable insulation fault detection method is provided, which is applied to Figure 1 The controller 106 in the embodiment is taken as an example to illustrate, and the following steps are included:

[0039] S202, when the sound wave generator injects a first sound wave of a preset frequency into the cable, acquiring a first sound wave signal when the first sound wave passes through each position of the cable based on the sensor array.

[0040] Among them, the frequency of the sound wave generator is adjustable, for example, the sound wave generator can generate a sound wave signal of 20 kHz to 500kHz. The setting of the sound wave generator is not limited, as long as the sensor array can fully cover the length of the cable to ensure the comprehensive monitoring of each possible fault location, for example, the sound wave generator is set relative to the length direction of the cable. It should be understood that the sound wave signal rate of 20 kHz to 500kHz can effectively stimulate various potential defects in the cable insulation layer and ensure the effective propagation of sound waves in the cable material. For example, high-frequency sound waves around 500kHz have a shorter wavelength and can penetrate the microstructure of the material, thereby revealing potential defects that are invisible to the naked eye, for example, they can effectively stimulate subtle defects such as tiny cracks or aging areas in the insulation layer; low-frequency sound waves in the range of 20kHz to 100kHz can penetrate thicker cable material layers and have a better effect on detecting defects deep in the insulation layer (such as severe aging or deep damage). Therefore, by combining acoustic signals of different frequencies, all kinds of fault conditions in the cable insulation layer can be fully covered, and defect detection of different depths and scales can be achieved.

[0041] The form in which the acoustic wave generator injects the first acoustic wave into the cable is not limited. For example, the first acoustic wave can be injected into the cable in the form of a pulse, that is, the propagation form of the first acoustic wave in the cable is a pulse signal. The pulse signal has better time resolution and can more accurately capture the propagation path and reflection point of the acoustic wave in the cable. The pulse signal is also easy to distinguish from other electromagnetic signals, thereby reducing the impact of interference. In some cases, the first acoustic wave can be injected into the conductor or insulation layer of the cable based on a coupler.

[0042] Among them, the sensor array is used to receive the reflection and transmission signals of the first sound wave in the cable and obtain the sound wave signal. The sensor array includes a plurality of sensors, so that the sound wave signal when the first sound wave passes through each position of the cable can be captured by the sensor array. It should be understood that for abnormal conditions in the cable insulation layer, the first sound wave received by each sensor has different characteristics such as intensity, frequency change and time delay, so as to accurately realize the fault detection process of the cable. In some cases, in order to ensure the synchronization of signal acquisition, all sensors are synchronized using a high-precision clock to ensure that the timing of the signal is accurate. In some embodiments, the sensor array includes a plurality of sensors, and the plurality of sensors can be evenly or unevenly arranged along the length direction of the cable. For example, the distance between adjacent sensors is less than or equal to a preset distance, and the preset distance can be 2 meters, 1 meter or other values. The type of sensor is not limited, for example, the sensor can be a sensor designed based on piezoelectric technology. The frequency of the signal that the sensor can detect is not limited, for example, the sensor can detect sound wave signals within 20kHz to 500kHz to ensure that various potential defects in the insulation layer can be detected.

[0043] For each sensor in the sensor array, the sensor is in close contact with the surface of the cable, so that the sound wave propagation information inside the cable can be captured. In order to avoid poor contact due to movement or vibration of the cable, in some embodiments, a mounting fixture or bracket can be used to fix the sensor to the surface of the cable to ensure that the sensor will not loosen due to vibration or cable movement. In other embodiments, in order to ensure that the sensor can repeatedly contact the surface of the cable, a coupling agent can be applied to the contact surface between the sensor and the cable. The type of coupling agent is not limited. For example, the coupling agent can be silicone grease to improve the efficiency of sound wave propagation. In order to ensure the quality of signal acquisition, in some embodiments, each sensor in the sensor array can be calibrated to ensure that the detection sensitivity and signal response of each sensor remain consistent. The calibration process is as follows:

[0044] Specifically, when each sensor is set on the sensor surface, the signal generator is controlled to inject a sound wave of a fixed frequency and a fixed amplitude into the cable, so that when the sound wave propagates along the cable, each sensor can collect a corresponding sound wave signal; the data acquisition system is controlled to synchronously collect the sound wave signals collected by each sensor based on the sampling frequency, and the sound wave signals collected by each sensor are obtained from the data acquisition system. Among them, the fixed frequency can be 100kHz, the fixed amplitude can be 5V (volts), and the sampling frequency can be 2MHz (megahertz), and the above values ​​can also be set in other ways.

[0045] Furthermore, for the acoustic wave signals collected by each sensor, the acoustic wave signals are analyzed in the time domain to extract the signal amplitude, signal peak value, rise time corresponding to the maximum signal amplitude, and signal duration of the acoustic wave signals; the acoustic wave signals are analyzed in the frequency domain to extract the amplitude and phase of the main frequency components. Furthermore, the signal amplitude, signal peak value, rise time corresponding to the maximum signal amplitude, signal duration, amplitude and phase of the main frequency components corresponding to the acoustic wave signals are compared with their respective corresponding standard values. Among the signal amplitude, signal peak value, rise time corresponding to the maximum signal amplitude, signal duration, amplitude and phase of the main frequency components corresponding to the acoustic wave signals, when the difference between at least one parameter value and the standard value is greater than a first preset value, it is determined that the sensor corresponding to the acoustic wave signal is faulty; and maintenance prompt information for the faulty sensor is output to remind the faulty sensor to be calibrated and maintained. Alternatively, a sensor adjacent to the sensor corresponding to the acoustic wave signal is determined, and the signal amplitude, signal peak value, rise time and signal duration corresponding to the maximum signal amplitude of the acoustic wave signal are compared with the signal amplitude, signal peak value and rise time and signal duration corresponding to the maximum signal amplitude of the acoustic wave signal collected by the adjacent sensor. Among the signal amplitude, signal peak value and rise time corresponding to the maximum signal amplitude, signal duration, amplitude and phase of the main frequency component of the acoustic wave signal, if the difference between at least one parameter value and the parameter value corresponding to the adjacent sensor is greater than the second preset value, and the adjacent sensor is not faulty, it is determined that the sensor corresponding to the acoustic wave signal is faulty, and maintenance prompt information for the sensor corresponding to the acoustic wave signal is output to remind the sensor corresponding to the acoustic wave signal and the adjacent sensor to be calibrated and maintained.

[0046] Furthermore, when the sensor has been calibrated and maintained, the control signal generator is used to re-inject sound waves of a fixed frequency and a fixed amplitude into the cable; based on the signal amplitude, signal peak, rise time corresponding to the maximum amplitude of the signal, signal duration, amplitude and phase of the main frequency components corresponding to the sound wave signal re-collected by each sensor, it is determined whether the sensor corresponding to the sound wave signal is faulty. For example, when the difference between all parameter values ​​and the standard value is less than or equal to the first preset value, it is determined that the sensor corresponding to the sound wave signal is not faulty, or when the difference between all parameter values ​​and the parameter values ​​corresponding to the adjacent sensors is less than or equal to the second preset value, and the adjacent sensors are not faulty, it is determined that the sensor corresponding to the sound wave signal is not faulty. In some embodiments, calibration and maintenance of each sensor in the sensor array can also be achieved at different times or in different environments, and there can be other settings, which are not limited in this embodiment.

[0047] S204, when it is determined that a fault exists in the cable based on the signal analysis results of each first sound wave signal, control the sound wave generator to inject a second sound wave of a second frequency into the cable based on the type of fault existing in the cable.

[0048] Among them, the second frequency is determined based on the mapping relationship between the preset fault type and the sound wave frequency, and the fault type, so that when a sound wave consistent with the sound wave frequency is injected into the cable, the position of the fault corresponding to the fault type in the cable can be detected in a targeted manner. Specifically, from the mapping relationship between the fault type and the sound wave frequency, the preset fault type consistent with the fault type existing in the cable is determined; the sound wave frequency corresponding to the matching preset fault type in the mapping relationship is determined as the second frequency. For example, when the preset fault type is insulation aging or delamination, any frequency between 20kHz and 50kHz can be determined as the second frequency; when the preset fault type is cracks or bubbles, any frequency between 300kHz and 500kHz can be determined as the second frequency.

[0049] Among them, based on the signal analysis results of each first acoustic wave signal, the implementation method of determining that the cable has a fault is not limited, and the following is an example of possible implementation methods. In one embodiment, when the preset frequency is in the first frequency range, for each first acoustic wave signal, the first acoustic wave signal is subjected to frequency domain analysis to obtain a preset frequency domain feature; the preset frequency domain feature includes a high-frequency component; the high-frequency component corresponding to each first acoustic wave signal is subjected to statistical analysis to obtain a high-frequency component trend result. If the high-frequency component trend result shows a downward trend, or the high-frequency component trend result indicates that the high-frequency component gradually disappears, it is determined that the type of fault in the cable is a tiny crack or bubble.

[0050] Based on S204, it can be known that the frequency of the sound wave injected into the cable can be adjusted based on the mapping relationship between the preset fault type and the sound wave frequency. The second frequency can also be determined in the following manner, specifically:

[0051] In some embodiments, based on each first sound wave signal, if it is determined that the peak amplitude of the high-frequency region of the corresponding spectrum shows a downward trend, and the energy of the high-frequency component shows a downward trend, the first frequency can be reduced, that is, the second frequency is the frequency after the first frequency is reduced, for example, it can be adjusted from 500kHz to 100kHz or 50kHz. Therefore, the low-frequency signal has a stronger penetration ability, is more suitable for detecting larger defects (such as insulation aging, delamination, etc.), and is not easily affected by moisture.

[0052] In some embodiments, based on each first acoustic wave signal, if it is determined that the corresponding signal time delay shows an upward trend, and the waveform of the acoustic wave signal shows a fuzzy trend or the amplitude change trend shows a downward trend, the first frequency can be increased, that is, the second frequency is the frequency after the first frequency is increased, for example, it can be adjusted from 50kHz to 200kHz or 300kHz. Therefore, high-frequency signals are more suitable for detecting small defects (such as cracks and bubbles), can provide higher resolution, and help locate the cause of the fuzzy reflection signal.

[0053] In some embodiments, based on each first sound wave signal, if it is determined that the peak position of the corresponding high-frequency spectrum is offset and the spectrum entropy increases, the frequency is dynamically adjusted according to the spectrum change trend. For example, if the high-frequency signal is offset, the second frequency can be determined in the frequency adjacent to the offset frequency. For example, if the first frequency is 300kHz, the frequency can be adjusted from 300kHz to 250kHz or 350kHz.

[0054] In one embodiment, based on each first sound wave signal, if it is determined that the corresponding signal noise presents a downward trend, the first frequency can be increased, and the energy of the sound wave can be increased to determine the second frequency. For example, the frequency can be reduced to 50kHz or 100kHz, and the energy of the injected sound wave can be increased. Thus, the low-frequency signal is not easily interfered by noise, and increasing the energy can enhance the signal penetration ability and improve the signal-to-noise ratio.

[0055] S206: Determine a fault position corresponding to the fault type of the cable based on the second sound wave signals collected by the sensor array when the second sound wave passes through each position.

[0056] Among them, the fault location refers to the location where the fault exists in the cable, and the fault types corresponding to different fault locations may be the same or different. The implementation method of determining the fault location corresponding to the fault type of the cable based on each second acoustic wave signal is not limited. For example, a fault detection model can be obtained based on multiple historical acoustic wave signals and the fault location corresponding to each historical acoustic wave signal. Thus, by inputting each second acoustic wave signal into a fault detection model, the fault location corresponding to the fault type of the cable can be determined. Alternatively, the fault location can also be determined based on the similarity between each second acoustic wave signal.

[0057] In one embodiment, when the severity corresponding to the fault type is greater than a preset degree, an early warning mechanism is triggered to remind the operator to take maintenance measures in time, and a corresponding fault detection report is generated so that the operator can quickly understand the health status of the cable. Thus, by avoiding the occurrence of major faults, the continuous and stable operation of the power system is ensured. Therefore, the method of the present application can provide real-time online monitoring and fault warning, thereby significantly reducing the risk of system downtime caused by cable faults. Among them, fault detection includes information such as fault type, fault location corresponding to the fault type, severity, and maintenance recommendations; the fault severity has a corresponding relationship with the fault type, for example, the severity corresponding to a partial discharge fault is less than the severity of an insulation aging fault.

[0058] In some embodiments, the fault detection report can be sent to the monitoring center or archived in the form of an electronic document for subsequent analysis and tracking. In some cases, the fault detection model can also be updated based on the fault location corresponding to each second acoustic wave signal, so that the fault detection model can be continuously optimized as data accumulates to ensure the generalization ability and adaptability of the model.

[0059] based on Figure 2 The method shown, when the sound wave generator injects a first sound wave of a preset frequency into the cable, the first sound wave signal when the first sound wave passes through each position of the cable can be obtained based on the sensor array, and when it is determined that the cable has a fault based on the signal analysis results of each first sound wave signal, based on the type of fault in the cable, the sound wave generator is controlled to inject a second sound wave of a second frequency into the cable, and then based on the second sound wave signal collected by the sensor array when the second sound wave passes through each position, the fault position corresponding to the fault type of the cable can be determined. Therefore, the present application does not need to shut down or disconnect the power supply of the power system, and can also realize the insulation fault detection of the cable, which solves the problem of limited detection scenarios and expands the detection scenarios. Moreover, the present application performs insulation fault detection on the cable from the perspective of the sound wave signal, and the sound wave signal has a high sensitivity to the faults existing in the cable, thereby improving the accuracy of the cable insulation fault detection.

[0060] based on Figure 2 It can be seen from the content shown that the fault type of the cable and the position corresponding to the fault type can be determined by inputting sound waves into the cable for the second time. In one embodiment, the fault type and fault position can also be determined based on each first sound wave signal. Specifically, each first sound wave signal is analyzed in the time domain to obtain the target time domain feature, and each first sound wave signal is analyzed in the frequency domain to obtain the target frequency domain feature; the target time domain feature and the target frequency domain feature are input into the fault diagnosis model to determine the fault type, fault location and the confidence score corresponding to the fault type. Therefore, through intelligent detection methods, human intervention can be reduced and the accuracy and efficiency of fault detection can be improved.

[0061] In one embodiment, the cable insulation fault detection system may further include a detection device, the detection device is used to detect humidity information and temperature information of the environment in which the cable is located; the humidity information includes the humidity mean and the humidity change rate, and the temperature information includes the temperature mean and the temperature change rate. Specifically, based on the type of fault in the cable, controlling the sound wave generator to inject a second sound wave of a second frequency into the cable includes the following steps:

[0062] S31, performing frequency domain analysis on each first sound wave signal to determine each frequency domain feature of each first sound wave; each frequency domain feature includes high-frequency energy, high-frequency peak amplitude and spectrum entropy; wherein, high-frequency peak amplitude refers to high-frequency peak amplitude, and high-frequency energy refers to energy within a high-frequency range (such as 100kHz to 500kHz).

[0063] S32, constructing an acoustic wave feature based on the high-frequency energy, high-frequency peak amplitude and spectrum entropy of each first acoustic wave signal, and constructing an environmental feature based on the humidity mean and humidity change rate, and the temperature mean and temperature change rate.

[0064] For example, the sensor array includes N sensors, and the acoustic wave feature S satisfies:

[0065]

[0066] in, represents the high-frequency energy of the first acoustic wave signal collected by the i-th sensor in the sensor array, ; represents the high-frequency peak amplitude of the first acoustic wave signal collected by the i-th sensor, Represents the spectral entropy of the first sound wave signal collected by the i-th sensor.

[0067] Environmental characteristics E meet: , where T represents the mean temperature and H represents the mean humidity. represents the rate of temperature change, Indicates the rate of change of humidity.

[0068] S33, when the feature correlation between the acoustic wave feature and the environmental feature is less than a correlation threshold, based on the type of fault existing in the cable, controlling the acoustic wave generator to inject a second acoustic wave of a second frequency into the cable.

[0069] Among them, the method of determining the characteristic correlation between the sound wave feature and the environmental feature is not limited. For example, the Pearson correlation coefficient between the sound wave feature and the environmental feature can be used as the characteristic correlation; or the Pearson correlation coefficient between the high-frequency energy corresponding to each sensor and the environmental feature can be determined as the first sub-correlation; the Pearson correlation coefficient between the high-frequency peak amplitude corresponding to each sensor and the environmental feature is determined as the second sub-correlation; the Pearson correlation coefficient between the spectrum entropy corresponding to each sensor and the environmental feature is determined as the third sub-correlation; based on the first sub-correlation, the second sub-correlation and the third sub-correlation, the characteristic correlation between the sound wave feature and the environmental feature is determined, that is, the characteristic correlation includes the first sub-correlation, the second sub-correlation and the third sub-correlation.

[0070] In some embodiments, the correlation threshold includes a first sub-threshold, a second sub-threshold, and a third sub-threshold. When the first sub-correlation is less than the first sub-threshold, the second sub-correlation is less than the second sub-threshold, and the third sub-correlation is less than the third sub-threshold, it is determined that the feature correlation between the acoustic wave feature and the environmental feature is less than the correlation threshold. The specific values ​​of the first sub-threshold, the second sub-threshold, and the third sub-threshold can be set according to actual conditions. For example, the first sub-threshold can be 0.3.

[0071] Based on the contents of S31-S33, it can be known that, when it is determined that the cable has a fault based on the signal analysis results of each first acoustic wave signal, if the characteristic correlation between the acoustic wave characteristics and the environmental characteristics is detected to be less than the correlation threshold, it can be explained that the cable is not faulty due to environmental factors, which can further improve the accuracy of cable fault detection. Furthermore, based on the type of fault in the cable, the acoustic wave generator is controlled to inject a second acoustic wave of a second frequency into the cable, thereby realizing the process of detecting the cable fault location.

[0072] Combining the contents of S31-S33, it can be known that from the perspective of the characteristic correlation between the acoustic wave characteristics and the environmental characteristics, it can be further determined whether the detection result of the cable fault is caused by environmental factors. It can also be determined in the following ways, specifically:

[0073] In one embodiment, the humidity information includes multiple humidities, and the temperature information includes multiple temperatures. It is also possible to determine whether environmental factors affect the detection result of the cable fault based on the high-frequency signal change trend of multiple first sound wave signals, the comparison results of the humidity change trends corresponding to multiple humidities, or the comparison results of the high-frequency signal change trend with the temperature change trends corresponding to multiple temperatures. For example, in the humidity increase stage or the temperature rapid heating stage, the high-frequency signal attenuates significantly, or in the temperature rapid heating stage, it can be determined that the environmental factors cause the detection result of the cable to be faulty.

[0074] In one embodiment, a clustering algorithm (such as K-means or other methods) can also be used to classify multiple first sound wave signals into different categories, and for signals of the same category, it is determined whether the signal changes meet the characteristics of electrical faults. If so, it can be determined that environmental factors have not caused the detection result of the cable to be faulty, that is, the cable itself has a fault. Otherwise, the cable fault is caused by environmental factors.

[0075] In one embodiment, for the first sound wave signal collected by each sensor, when the first sound wave signal is synchronously analyzed in the time domain and the frequency domain, if high-frequency energy attenuation or abnormality is detected at the same time point, but the temperature and humidity at the same time point do not change, it can be determined that environmental factors have not caused the detection result of the cable to be faulty.

[0076] In some embodiments, if it is determined that environmental factors cause the detection result of the cable to be faulty, maintenance information is output to remind maintenance personnel to maintain the cable. For example, if the humidity rises from 40% to 80% and the high-frequency energy is attenuated by 30%, it can be determined that moisture intrusion causes high-frequency attenuation. Therefore, it can be explained that the detection result of the cable caused by moisture is faulty, and maintenance information such as checking the cable sealing, performing drying treatment and sealing repair can be output. For example, if the high-frequency energy attenuation is detected to be 50% at the same time point, but there is no obvious change in humidity and temperature, it can be explained that the environmental factors did not cause the detection result of the cable to be faulty, that is, the cable itself is faulty, and the fault type may be a crack in the insulation layer, and maintenance suggestions such as local insulation repair or replacement of the damaged cable can be output.

[0077] In one embodiment, each frequency domain feature further includes: high frequency energy ratio and spectrum main frequency, and the high frequency energy ratio refers to the ratio of energy in the high frequency band (such as 100kHz to 500kHz) to the total energy. Specifically, the following steps may also be included:

[0078] S41, performing time-frequency analysis on each first sound wave signal to determine each time domain feature of each first sound wave signal; each time domain feature includes time domain energy, signal amplitude, maximum signal amplitude, minimum signal amplitude, rising time corresponding to the maximum signal amplitude, and falling time corresponding to the minimum signal amplitude; wherein, the rising time corresponding to the maximum signal amplitude refers to the time taken to rise to the maximum signal amplitude, and the falling time corresponding to the minimum signal amplitude refers to the time taken to fall to the minimum signal amplitude.

[0079] S42, analyzing each time domain feature of each first sound wave signal, and determining a time domain feature trend corresponding to each time domain feature.

[0080] S43, analyzing each frequency domain feature of each first sound wave signal, and determining a frequency domain feature trend corresponding to each frequency domain feature.

[0081] S44, determining the fault type of the cable based on the matching results of each time domain characteristic trend and / or each frequency domain characteristic trend and the preset fault characteristic trends corresponding to each preset fault type.

[0082] Among them, based on the matching results of each time domain characteristic trend and / or each frequency domain characteristic trend, and the preset fault characteristic trend corresponding to each preset fault type, there is no limitation on the implementation method of determining the fault type of the cable, and several of the methods are illustrated below.

[0083] In one embodiment, when the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, the frequency domain characteristic trend corresponding to the spectrum entropy shows an upward trend, and the frequency domain characteristic trend corresponding to the spectrum main frequency shows a shift trend, the fault type of the cable is determined to be a partial discharge fault. That is, when the high-frequency energy decreases significantly, the spectrum entropy increases, and the main frequency shifts, the fault type of the cable is determined to be a partial discharge fault.

[0084] In one embodiment, when the time domain characteristic trend corresponding to the maximum amplitude of the signal shows a downward trend, the time domain characteristic trend corresponding to the time domain energy shows a downward trend, and the time domain characteristic trend corresponding to the rising time or the falling time shows an upward trend, and the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, the fault type of the cable is determined to be an insulation moisture fault. That is, when the maximum amplitude of the signal gradually decays, the time domain energy decreases, the rising / falling time becomes longer, and the high-frequency energy ratio decreases, the fault type of the cable is determined to be an insulation moisture fault.

[0085] In one embodiment, when the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, the time domain characteristic trend corresponding to the signal amplitude shows a slow downward trend, and the frequency domain characteristic trend corresponding to the spectrum entropy shows an upward trend, the fault type of the cable is determined to be an insulation aging fault. That is, when the high-frequency energy ratio continues to decrease, the overall signal amplitude slowly decays, and the spectrum entropy increases, the fault type of the cable is determined to be an insulation aging fault.

[0086] In one embodiment, when the time domain characteristic trend corresponding to the signal amplitude shows a fluctuating trend, the time domain characteristic trend corresponding to the high-frequency energy ratio shows an upward trend, and the time domain characteristic trend corresponding to the main frequency of the spectrum shows a shifting trend, the fault type of the cable is determined to be a mechanical damage fault. That is, when the signal amplitude fluctuates violently, the high-frequency energy is abnormally enhanced, and the main frequency of the spectrum is unstable, the fault type of the cable is determined to be a mechanical damage fault.

[0087] In one embodiment, the acoustic wave signal includes a reflected signal. If the signal time delay of the sensor matching the acoustic wave signal is less than a delay threshold, and the amplitude of the reflected signal is greater than a preset amplitude threshold, the fault type of the cable is determined to be a short circuit fault; if the signal time delay of the sensor matching the acoustic wave signal is greater than or equal to the delay threshold, and the amplitude of the reflected signal is greater than the preset amplitude threshold, the fault type of the cable is determined to be an open circuit fault.

[0088] In one embodiment, an envelope curve corresponding to each first acoustic wave signal is obtained. If the curve amplitude of each envelope curve is less than a set amplitude threshold, the fault type of the cable is determined to be an insulation moisture fault; based on the envelope curve corresponding to each first acoustic wave signal, the amplitude change trend of the envelope curve is obtained; if the amplitude change trend of the envelope curve shows a downward trend, the fault type of the cable is determined to be an insulation aging fault; if there are regular peaks in the amplitude change trend of the envelope curve, the fault type of the cable is determined to be a partial discharge fault.

[0089] In one embodiment, if the attenuation degree of the high-frequency signal corresponding to each first sound wave signal is less than the attenuation threshold, the fault type of the cable is determined to be an insulation crack fault; if the attenuation degree of the high-frequency signal corresponding to each first sound wave signal is greater than or equal to the attenuation threshold, the fault type of the cable is determined to be an insulation moisture fault; if the high-frequency signal corresponding to each first sound wave signal is attenuated in a specific frequency band, the fault type of the cable is determined to be an insulation layer bubble fault, that is, bubbles appear in the insulation layer.

[0090] In one embodiment, if the low-frequency component in the frequency spectrum corresponding to each first sound wave signal is greater than a set threshold, the fault type of the cable is determined to be an open circuit fault; if the high-frequency component in the frequency spectrum corresponding to each first sound wave signal is concentrated and has strong energy, the fault type of the cable is determined to be a short circuit fault; if the frequency spectrum energy distribution corresponding to each first sound wave signal shows a trend of shifting toward the low-frequency direction, the fault type of the cable is determined to be an insulation degradation fault.

[0091] In some embodiments, if the high-frequency signal is attenuated in a high-humidity environment, the cable fault type can also be determined to be an insulation moisture fault; if the insulation performance is degraded in a high-temperature environment, resulting in a decrease in the amplitude of the reflected signal, the cable fault type can be determined to be a thermal aging fault.

[0092] Based on the above content, it can be known that the fault type of the cable can be determined based on the matching results of each time domain characteristic trend and / or each frequency domain characteristic trend and the preset fault characteristic trend corresponding to each preset fault type. The fault type can also be determined by the following method, specifically:

[0093] In one embodiment, each first acoustic wave signal can be analyzed based on a fault type classification model to determine the fault type of the cable. In some embodiments, multiple historical acoustic wave signals can be classified based on a classification algorithm; the classified historical acoustic wave signals are processed to determine the insulation fault characteristics; and a machine learning model is trained based on the insulation fault characteristics to obtain a fault classification model. In other words, the number of fault type classification models can be multiple, and each fault type classification model corresponds to a fault type, that is, each first acoustic wave signal is input into each fault type classification model respectively, and the fault type can be obtained. For example, the fault type includes partial discharge, insulation aging, mechanical damage, etc. Therefore, by training and learning a large amount of fault data, not only the accuracy of fault identification can be improved, but also different cable types and use environments can be adapted. Moreover, through automated fault identification, manual intervention can be reduced, and the efficiency and accuracy of fault detection can be improved.

[0094] It should be understood that cable interference and other noise sources in the environment where the cable is located may affect the accuracy of signal acquisition. In some cases, each first acoustic wave signal can be preprocessed, and the fault type of the cable can be determined based on the predicted first acoustic wave signals. Preprocessing can include denoising, normalization, time alignment, gain adjustment, etc. Similarly, each second acoustic wave signal can also be preprocessed, and the fault position corresponding to the fault type of the cable can be determined based on each preprocessed second acoustic wave signal. Wavelet transform or bandpass filter can be used to remove low-frequency noise and environmental noise in the acoustic wave signal, the signal amplitude of all acoustic wave signals can be normalized to eliminate the gain difference between different sensors, and the signal that characterizes the fault characteristics of the acoustic wave signal can be increased by gain adjustment processing to ensure that the key fault characteristics are not masked by noise.

[0095] In one embodiment, the fault type of the cable can also be determined based on each time domain feature trend and / or each frequency domain feature trend, a preset time domain feature trend corresponding to the fault type, and a preset frequency domain feature area. The preset time domain feature trend includes a sub-time domain feature trend corresponding to each time domain feature, and the preset frequency domain feature trend includes a sub-frequency domain feature trend corresponding to each frequency domain feature. Specifically, when the similarity between each time domain feature trend and the sub-time domain feature trend corresponding to each time domain feature is greater than a first similarity threshold, and / or the similarity between each frequency domain feature trend and the sub-frequency domain feature trend corresponding to each frequency domain feature is greater than a second similarity threshold, the corresponding fault type is determined as the fault type of the cable. The similarity can be calculated based on methods such as Euclidean distance, dynamic time warping (DTW), or cosine similarity.

[0096] In one embodiment, based on the matching results of each time domain characteristic trend and / or each frequency domain characteristic trend and the preset fault characteristic trend corresponding to each preset fault type, determining the fault type of the cable includes the following steps:

[0097] S51, based on the matching results of each time domain characteristic trend and / or each frequency domain characteristic trend and the preset fault characteristic trend corresponding to each preset fault type, determine multiple candidate fault types. The multiple candidate fault types include at least two of partial discharge fault, insulation moisture fault, insulation aging fault, and mechanical damage fault.

[0098] S52: Determine the fault type of the cable based on the candidate fault types and the matching scores corresponding to the candidate fault types.

[0099] There is no limitation on the method of determining the fault type of the cable based on each candidate fault type and the matching score corresponding to the candidate fault type. For example, the candidate fault type with the highest matching score can be determined as the fault type of the cable, or the candidate fault types can be sorted according to the matching score, and the top M candidate fault types can be determined as the fault type of the cable.

[0100] Based on the contents of S51-S52, when multiple candidate fault types are determined, the fault type of the cable is determined by combining the matching scores corresponding to the candidate fault types, which can improve the accuracy of determination.

[0101] In one embodiment, the sensor array includes a plurality of sensors, and the implementation method of determining the fault position corresponding to the fault type of the cable based on the second sound wave signal collected by the sensor array when the second sound wave passes through each position includes the following steps:

[0102] S61, analyzing each second sound wave signal collected by the sensor array when the second sound wave passes through each position, and determining a sound wave propagation velocity matched with the sensor corresponding to the second sound wave signal, and a sound wave propagation distance corresponding to the sound wave propagation velocity.

[0103] The acoustic wave sensing speed refers to the propagation speed of the second acoustic wave in the cable, and there is no limit to the implementation method of determining the acoustic wave sensing speed matching the sensor corresponding to the second acoustic wave signal. For example, for the i-th sensor, the ratio of the cable length L to the signal time delay corresponding to the i-th sensor is determined as the acoustic wave sensing speed matching the i-th sensor, that is, .

[0104] Among them, the signal time delay refers to the time delay from the time when the sound wave generator emits the first sound wave to the time when the sensor receives the reflected signal. The implementation method of determining the signal time delay of the sensor corresponding to the second sound wave signal is not limited. For example, usually, the signal collected by the sensor is a reflected signal, then the second sound wave signal includes the reflected signal, and the cross-correlation function between the reflected signal and the transmitted signal is determined; the time point corresponding to the peak of the cross-correlation function is determined as the signal time delay of the sensor corresponding to the second sound wave signal. Among them, the transmitted signal refers to the original signal emitted by the sound wave generator.

[0105] for example, Indicates the transmission signal. represents the reflected signal corresponding to the i-th sensor, then the cross-correlation function between the reflected signal and the transmitted signal is satisfy:

[0106]

[0107] The cross-correlation function The time point corresponding to the peak value of is determined as the signal time delay matching the i-th sensor.

[0108] In this embodiment, the acoustic wave propagation distance refers to the distance between the fault location and the sensor. The method for determining the acoustic wave propagation distance of the sensor corresponding to the second acoustic wave signal is not limited. For example, the ratio of the product of the acoustic wave sensing speed matched with the sensor and the signal time delay to 2 can be determined as the acoustic wave propagation distance. That is, the acoustic wave propagation distance matched with the i-th sensor is .

[0109] S62: Determine a fault position corresponding to the fault type of the cable based on the sound wave propagation distance matched with each sensor and the installation position of each sensor.

[0110] Specifically, each acoustic wave propagation distance, each setting position, and the fault position satisfy a nonlinear equation group, which is expressed as:

[0111]

[0112] in, Indicates the fault location. represents the setting position of the i-th sensor, represents the sound wave propagation distance of the ith sensor. Based on the triangulation positioning method or the least squares method, the fault location corresponding to the fault type of the cable can be determined by solving the nonlinear equations.

[0113] For example, the sensor array includes a first sensor and a second sensor, and the setting position of the first sensor is represented as , the setting position of the second sensor is expressed as , the sound wave propagation distance of the first sensor is expressed as , the sound wave propagation distance of the second sensor is expressed as , determine the fault location by triangulation satisfy:

[0114]

[0115]

[0116] When the distance between the first sensor and the second sensor is Q, the fault location satisfy:

[0117]

[0118] It should be understood that the temperature and humidity at the location of the sensor will affect the propagation speed of the sound wave, and thus affect the propagation distance of the sound wave matched with the sensor. In one embodiment, the following steps may also be included:

[0119] S71, obtaining a first humidity and a first temperature at a location of each sensor.

[0120] Among them, the cable insulation fault detection system can also include a detection device, the detection device includes multiple sub-temperature detection devices and multiple sub-humidity detection devices, each sub-temperature detection device is used to detect a first temperature at the sensor location, and each sub-humidity detection device is used to detect a first humidity at the sensor location.

[0121] S72, for each sensor, based on the sound wave velocity under standard conditions, the temperature correction coefficient, the humidity correction coefficient, the standard temperature, the standard humidity, and the first humidity and the first temperature, determine an adjusted sound wave propagation velocity that matches the sensor.

[0122] Specifically, the adjusted sound wave propagation velocity, sound wave velocity, temperature correction coefficient, humidity correction coefficient, standard temperature, standard humidity, and the relationship between the first humidity and the first temperature satisfies:

[0123]

[0124] in, represents the adjusted sound wave propagation speed, represents the speed of sound waves under standard conditions, represents the temperature correction factor, represents the humidity correction factor, represents the characteristic temperature, Indicates humidity. represents the first temperature, Indicates the first humidity.

[0125] In one embodiment, when determining the adjusted sound wave propagation speed that matches the sensor, the sound wave propagation distance can be updated based on the adjusted sound wave propagation speed. Thus, when determining the fault location based on the updated sound wave propagation distance, the influence of environmental factors on the fault location determination process can be avoided, and the accuracy of fault location determination can be improved.

[0126] In some embodiments, the fault type can also be determined based on the first humidity and the first temperature at the location of each sensor, and each first sound wave signal. Specifically, the frequency domain analysis is performed on each first sound wave signal to determine the high-frequency component amplitude and low-frequency energy corresponding to each first sound wave signal; the second vibration signal at the location of the sensor corresponding to each first sound wave signal and the signal time delay matched with each sensor are obtained; based on the high-frequency component amplitude, low-frequency energy, signal time delay, first temperature, first humidity and vibration amplitude of the second vibration signal corresponding to each first sound wave signal, a comprehensive feature vector is constructed; the comprehensive feature vector is classified and identified based on a machine learning model (such as a decision tree, etc.), and the fault type can be determined.

[0127] In one embodiment, when determining the fault location corresponding to the fault type of the cable, the following steps may also be included:

[0128] S81, obtaining a second humidity, a second temperature and a first vibration signal at the fault location.

[0129] Wherein, if a first sub-humidity detection device adjacent to the fault location is determined, then based on the adjacent first sub-humidity detection device, the second humidity at the fault location is collected. If a first sub-temperature detection device adjacent to the fault location is determined, then based on the adjacent first sub-temperature detection device, the second temperature at the fault location is collected. The detection device may also include multiple vibration sensors, then based on the vibration sensor adjacent to the fault location among the multiple vibration sensors, the first vibration signal at the fault location is collected.

[0130] S82, when the fault type is an insulation moisture fault, if the second humidity is greater than the humidity at a position adjacent to the fault position, and the first vibration signal is in an abnormal state, a first verification pass result is output.

[0131] S83, when the fault type is an insulation aging fault, if the second temperature is greater than the temperature at a position adjacent to the fault position, and the first vibration signal is in a normal state, a second verification pass result is output.

[0132] The first verification result is used to indicate the accuracy of the determined insulation moisture fault and the fault location where the insulation moisture fault is located. The second verification result is used to indicate the accuracy of the determined insulation aging fault and the fault location where the insulation aging fault is located.

[0133] In some embodiments, when the fault type is an insulation crack fault or a mechanical damage fault, if the second temperature is greater than the temperature at a position adjacent to the fault position and the first vibration signal is in an abnormal state, a third verification pass result is output.

[0134] In some embodiments, when the fault type is an insulation moisture fault, if the second humidity is greater than the humidity at a position adjacent to the fault position, and the difference between the second temperature and the temperature at the adjacent position is within a set range, a fourth verification pass result is output.

[0135] In some embodiments, each frequency domain feature may also include a low-frequency energy ratio. When the fault type is an insulation aging fault, if the frequency domain feature trend corresponding to the low-frequency energy ratio shows an upward trend, the first vibration signal is in a normal state, and the second temperature is greater than the temperature at the position adjacent to the fault location, the fifth verification pass result is output.

[0136] In some embodiments, when the fault type is a short circuit fault, if the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, and the time delay mean represented by the signal time delay trend is less than the mean threshold, the sixth verification pass result is output.

[0137] In some embodiments, when the fault type is an open circuit fault, if the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, and the time delay mean represented by the signal time delay trend is greater than or equal to the mean threshold, the seventh verification pass result is output.

[0138] Based on the contents of S81-S83, when the fault location of the cable is determined, the determined insulation moisture fault and insulation aging fault are further verified by combining the temperature, humidity and first vibration signal at the fault location, thereby improving the reliability of the fault detection process.

[0139] In one embodiment, when the fault location corresponding to the fault type of the cable is determined, the following steps are also included:

[0140] S91, predicting a fault deterioration trend corresponding to the fault location based on a fault feature corresponding to the fault location while periodically injecting sound waves into the fault location.

[0141] There is no limitation on the method of predicting the fault deterioration trend corresponding to the fault location based on the fault feature corresponding to the fault location, and examples are given below in combination with possible implementation methods.

[0142] In one embodiment, a sensor adjacent to the fault location is determined, and frequency domain analysis and / or time domain analysis are performed on the acoustic wave signals collected by the adjacent sensors to obtain fault features corresponding to the fault location; a time series analysis method is used to capture the fault change trend in the fault features; and the fault change trend is analyzed based on a trend prediction model to predict the fault deterioration trend corresponding to the fault location.

[0143] Among them, the time series analysis method may include but is not limited to: sliding window method, exponentially weighted moving average (EWMA), etc. The trend prediction model can be obtained by training a machine learning model based on the historical fault change trend. The machine learning model includes but is not limited to: linear regression, support vector machine (SVM), long short-term memory network (LSTM), etc.

[0144] S92: When the trend value of the fault deterioration trend representation exceeds a threshold, output a fault maintenance suggestion matching the fault deterioration trend.

[0145] In some embodiments, the trend value of the fault deterioration trend can be represented by the reflection amplitude attenuation value in the fault deterioration trend, or the trend value of the fault deterioration trend can be represented by the high-frequency energy attenuation value in the fault deterioration trend. For example, when the reflection amplitude attenuation value exceeds 50%, or the high-frequency energy attenuation value exceeds 30%, a fault maintenance suggestion matching the fault deterioration trend is output.

[0146] In some cases, the degree of fault deterioration can be determined based on the difference between the trend value and the threshold value, and the degree of fault deterioration includes mild, moderate and severe. The larger the difference, the closer the degree of fault deterioration is to severe. For example, when the fault deterioration trend indicates crack expansion, the corresponding fault maintenance suggestions may include: local insulation repair or replacement; increase the inspection frequency to weekly monitoring; install a protective layer near the fault location to prevent further cracking. When the fault deterioration trend indicates increased moisture intrusion, the corresponding fault maintenance suggestions may include: check the cable sealing and perform waterproof repairs; install a dehumidification device to reduce the ambient humidity; dry the cable to delay moisture erosion. When the fault deterioration trend indicates local insulation aging, the corresponding fault maintenance suggestions may include: local replacement or strengthening of the cable; increase load monitoring to avoid large current shocks; and perform insulation resistance tests regularly.

[0147] Based on the contents of S91-S92, when the fault location corresponding to the fault type of the cable is determined, the fault location can be continuously monitored by periodically injecting sound waves into the fault location. When the fault deterioration trend corresponding to the fault location is predicted based on the trend prediction model, the fault maintenance suggestion matching the fault deterioration trend is output, so that the cable can be maintained in time to improve the operation reliability of the cable. Moreover, the prediction efficiency and prediction ability can be further improved based on the trend prediction model, making the fault detection process more intelligent and automated.

[0148] In combination with the above, in one embodiment, Figure 3As shown, a structural schematic diagram of a cable insulation fault detection system is provided, and the cable insulation fault detection system includes an acoustic wave generator 102, a sensor array 104, and a data analysis and processing system 302. Among them, the sensor array 104 includes N sensors, namely, the first sensor to the Nth sensor; the data analysis and processing system 302 includes a controller 106, a pre-processing device 3021, and a display interface 3022. The pre-processing device is used to process the acoustic wave signal and send the processed acoustic wave signal to the controller 106, so that the controller 106 executes the method of the above embodiment. The controller 106 is also used to control the display interface 3022 to display the verification pass result, the fault detection report, etc.

[0149] exist Figure 3 Based on the above, in one embodiment, Figure 4 As shown, a cable insulation fault detection method is provided, which is applied to Figure 1 The controller 106 in the embodiment is taken as an example to illustrate, and the following steps are included:

[0150] S402, when the sound wave generator injects a first sound wave of a preset frequency into the cable, acquiring a first sound wave signal when the first sound wave passes through each position of the cable based on the sensor array.

[0151] S404, performing time-frequency analysis on each first sound wave signal to determine each time domain feature of each first sound wave signal; each time domain feature includes time domain energy, signal amplitude, maximum signal amplitude, minimum signal amplitude, rising time corresponding to the maximum signal amplitude, and falling time corresponding to the minimum signal amplitude.

[0152] S406, performing frequency domain analysis on each first sound wave signal to determine each frequency domain feature of each first sound wave; each frequency domain feature includes high frequency energy, high frequency peak amplitude, spectrum entropy, high frequency energy ratio and spectrum dominant frequency.

[0153] S408, analyzing each time domain feature of each first sound wave signal to determine a time domain feature trend corresponding to each time domain feature, and analyzing each frequency domain feature of each first sound wave signal to determine a frequency domain feature trend corresponding to each frequency domain feature.

[0154] S410, determining a fault type of the cable based on the matching results of each time domain characteristic trend and / or each frequency domain characteristic trend and a preset fault characteristic trend corresponding to each preset fault type.

[0155] S412, obtaining humidity information and temperature information of the environment in which the cable is located, wherein the humidity information includes a humidity mean value and a humidity change rate, and the temperature information includes a temperature mean value and a temperature change rate.

[0156] S414, constructing acoustic wave features based on the high-frequency energy, high-frequency peak amplitude and spectrum entropy of each first acoustic wave signal, and constructing environmental features based on the humidity mean and humidity change rate, and the temperature mean and temperature change rate.

[0157] S416, when the feature correlation between the acoustic wave feature and the environmental feature is less than a correlation threshold, based on the type of fault existing in the cable, control the acoustic wave generator to inject a second acoustic wave of a second frequency into the cable.

[0158] S418, analyzing each second sound wave signal collected by the sensor array when the second sound wave passes through each position, and determining a sound wave propagation velocity matched with the sensor corresponding to the second sound wave signal, and a sound wave propagation distance corresponding to the sound wave propagation velocity.

[0159] S420: Determine a fault position corresponding to the fault type of the cable based on the sound wave propagation distance matched with each sensor and the installation position of each sensor.

[0160] Among them, the specific content of S402-S420 can be adapted to the description of the above content and will not be repeated here.

[0161] Combined with the above content, it can be seen that in the power transmission system, the insulation layer of the cable plays a vital role in the safety and reliability of the system. However, with the aging of the cable, the influence of environmental factors or other external damage, the insulation layer is prone to failure, resulting in unstable operation of the power system or even serious safety accidents. Existing insulation fault detection technologies mostly rely on the monitoring of electrical parameters, making it difficult to accurately locate minor damage and lack the ability to monitor in real time in complex environments. The cable insulation fault detection method provided by the present application has broad application prospects in power transmission systems, industrial electrical systems, and communication networks. For example, in online fault monitoring of high-voltage power transmission, distribution networks, industrial electrical systems, long-distance transmission lines, and cable insulation damage monitoring in complex environments, it can solve the problem of difficulty in achieving high-precision and real-time detection in existing cable insulation fault detection, which can greatly reduce the maintenance cost and fault risk of the cable, reduce fault downtime, and improve the operation efficiency and safety of the power system. Moreover, the method provided by the present application also has important application value in insulation monitoring of data centers and communication networks, which can effectively extend the service life of the cable and ensure the stability and reliability of the system.

[0162] In this application, by injecting sound waves into the cable, when the sound waves propagate inside the cable, they will change when encountering material media (such as conductors, insulation layers, sheaths, etc.), and when encountering defects (such as cracks, aging or moisture intrusion), reflection, transmission or attenuation will occur. The reflected sound wave signal provides an important basis for the fault location and characteristics. The propagation characteristics of the sound wave signal (such as reflection time, amplitude change, etc.) provide a basic basis for the detection and positioning of the fault. Therefore, further combining sensor array detection and machine learning methods can achieve high-precision, real-time detection and positioning of cable insulation faults, which has the advantages of non-invasiveness, high sensitivity, and strong real-time performance, significantly improving the safety and reliability of cable operation, and providing a new technical solution for the operation and maintenance of power and communication systems. Unlike traditional electrical detection methods, sound wave signals can propagate in the insulation layer of the cable and show high sensitivity to tiny defects in the insulating material. Therefore, combining the sensor array to receive and analyze the reflection and transmission characteristics of the sound wave in the cable can effectively identify subtle faults in the cable insulation layer and provide accurate fault location and fault type.

[0163] Among them, by performing time domain analysis on the acoustic wave signal, the time characteristics of the acoustic wave signal can be determined, such as the arrival time, amplitude, phase change, etc. of the signal. The abnormal changes in these parameters can directly reflect the location and nature of the defects encountered by the acoustic wave when propagating inside the cable. For example, by analyzing the arrival time of the acoustic wave signal, the system can calculate the propagation speed of the acoustic wave in the cable (that is, the propagation distance of the acoustic wave), thereby inferring the fault location. For example, the amplitude change can reflect the attenuation degree of the acoustic wave when encountering a defect, and the waveform distortion reveals the impact on the acoustic wave when passing through the defect area. These time domain characteristics are crucial to accurately locate the fault and assess the severity of the fault. By performing frequency domain analysis on the acoustic wave signal based on Fourier transform, the frequency components of the analysis signal can be determined. For example, changes in high-frequency components can often reveal tiny defects or aging areas in the insulation layer. High-frequency components are most susceptible to insulation defects during propagation. By identifying the attenuation and distortion of high-frequency signals, the system can detect tiny defects in the insulation layer, such as cracks, voids, or damp areas. For example, cracks usually lead to the enhancement of high-frequency components, while defects such as bubbles may cause the spectrum to be broadened. Through detailed analysis in the frequency domain, the system can identify different types of faults. Therefore, by adopting the time-frequency joint analysis method, the accuracy of fault identification can be improved.

[0164] Moreover, during the cable insulation fault detection process, the present application can dynamically adjust the frequency of the sound waves injected into the cable based on the mapping relationship between the preset fault type and the sound wave frequency, so as to accurately locate the fault position corresponding to the fault type, thereby improving the fault detection accuracy.

[0165] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0166] Based on the same inventive concept, the embodiment of the present application also provides a cable insulation fault detection device for implementing the cable insulation fault detection method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more cable insulation fault detection device embodiments provided below can refer to the limitations of the cable insulation fault detection method above, and will not be repeated here.

[0167] In one embodiment, Figure 5 As shown, a cable insulation fault detection device is provided, including: an acquisition module 502, a processing module 504 and an analysis module 506, wherein: the acquisition module 502 is used to obtain a first sound wave signal when the first sound wave passes through each position of the cable based on a sensor array when the sound wave generator injects a first sound wave of a preset frequency into the cable; the processing module 504 is used to control the sound wave generator to inject a second sound wave of a second frequency into the cable based on the type of fault in the cable when it is determined that the cable has a fault based on the signal analysis results of each first sound wave signal; the second frequency is determined based on a mapping relationship between a preset fault type and the sound wave frequency, and the fault type; the analysis module 506 is used to determine the fault position corresponding to the fault type of the cable based on the second sound wave signal collected by the sensor array when the second sound wave passes through each position.

[0168] In one embodiment, the processing module 504 is also used to: obtain humidity information and temperature information of the environment in which the cable is located; the humidity information includes the humidity mean and the humidity change rate, and the temperature information includes the temperature mean and the temperature change rate; perform frequency domain analysis on each first sound wave signal to determine each frequency domain feature of each first sound wave; each frequency domain feature includes high-frequency energy, high-frequency peak amplitude and spectrum entropy; based on the high-frequency energy, high-frequency peak amplitude and spectrum entropy of each first sound wave signal, respectively construct sound wave features, and based on the humidity mean and the humidity change rate, as well as the temperature mean and the temperature change rate, construct environmental features; when the feature correlation between the sound wave feature and the environmental feature is less than the correlation threshold, based on the type of fault existing in the cable, control the sound wave generator to inject a second sound wave of a second frequency into the cable.

[0169] In one embodiment, each frequency domain feature also includes: high-frequency energy ratio and spectrum main frequency; processing module 504 is also used to: perform time-frequency analysis on each first sound wave signal to determine each time domain feature of each first sound wave signal; each time domain feature includes time domain energy, signal amplitude, maximum signal amplitude, minimum signal amplitude, rising time corresponding to the maximum signal amplitude, and falling time corresponding to the minimum signal amplitude; analyze each time domain feature of each first sound wave signal to determine the time domain feature trend corresponding to each time domain feature; analyze each frequency domain feature of each first sound wave signal to determine the frequency domain feature trend corresponding to each frequency domain feature; determine the fault type of the cable based on the matching results of each time domain feature trend and / or each frequency domain feature trend, and the preset fault feature trend corresponding to each preset fault type.

[0170] In one embodiment, the processing module 504 is further used to execute at least one of the following: the first item: when the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, the frequency domain characteristic trend corresponding to the spectrum entropy shows an upward trend, and the frequency domain characteristic trend corresponding to the spectrum main frequency shows a shift trend, the fault type of the cable is determined to be a partial discharge fault; the second item: when the time domain characteristic trend corresponding to the maximum amplitude of the signal shows a downward trend, the time domain characteristic trend corresponding to the time domain energy shows a downward trend, and the time domain characteristic trend corresponding to the rising time or the falling time shows an upward trend, and the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, the fault type of the cable is determined to be an insulation moisture fault; the third item: when the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, the time domain characteristic trend corresponding to the signal amplitude shows a slow downward trend, and the frequency domain characteristic trend corresponding to the spectrum entropy shows an upward trend, the fault type of the cable is determined to be an insulation aging fault; the fourth item: when the time domain characteristic trend corresponding to the signal amplitude shows a fluctuating trend, the time domain characteristic trend corresponding to the high-frequency energy ratio shows an upward trend, and the time domain characteristic trend corresponding to the spectrum main frequency shows a shift trend, the fault type of the cable is determined to be a mechanical damage fault.

[0171] In one embodiment, the sensor array includes multiple sensors; the analysis module 506 is also used to: analyze each second sound wave signal collected by the sensor array when the second sound wave passes through each position, determine the sound wave propagation speed of the sensor matching the second sound wave signal, and the sound wave propagation distance corresponding to the sound wave propagation speed; based on the sound wave propagation distance matching each sensor and the setting position of each sensor, determine the fault position corresponding to the fault type of the cable.

[0172] In one embodiment, the analysis module 506 is also used to: obtain the first humidity and the first temperature of the location of each sensor; for each sensor, based on the sound wave velocity under standard conditions, the temperature correction coefficient, the humidity correction coefficient, the standard temperature, the standard humidity, and the first humidity and the first temperature, determine the adjusted sound wave propagation velocity matching the sensor.

[0173] In one embodiment, the analysis module 506 is also used to obtain a second humidity, a second temperature and a first vibration signal at the fault location; when the fault type is an insulation moisture fault, if the second humidity is greater than the humidity at a position adjacent to the fault location, and the first vibration signal is in an abnormal state, then output a first verification pass result; when the fault type is an insulation aging fault, if the second temperature is greater than the temperature at a position adjacent to the fault location, and the first vibration signal is in a normal state, then output a second verification pass result.

[0174] In one embodiment, the analysis module 506 is also used to: determine multiple candidate fault types based on the matching results of each time domain characteristic trend and / or each frequency domain characteristic trend, and the preset fault characteristic trends corresponding to each preset fault type; determine the fault type of the cable based on each candidate fault type and the matching score corresponding to the candidate fault type.

[0175] In one embodiment, the analysis module 506 is also used to: predict the fault deterioration trend corresponding to the fault location based on the fault characteristics corresponding to the fault location when periodically injecting sound waves into the fault location; and output a fault maintenance suggestion matching the fault deterioration trend when the trend value represented by the fault deterioration trend exceeds a threshold.

[0176] Each module in the above cable insulation fault detection device can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0177] In one embodiment, a computer device is provided. The computer device may be a cable insulation fault detection system, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as each first acoustic wave signal. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a cable insulation fault detection method is implemented.

[0178] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0179] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0180] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0181] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0182] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0183] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0184] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0185] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A cable insulation fault detection method, characterized in that: The method comprises: When the sound wave generator injects a first sound wave of a preset frequency into the cable, a first sound wave signal is acquired based on the sensor array when the first sound wave passes through each position of the cable; When it is determined that the cable has a fault based on the signal analysis results of each of the first sound wave signals, the sound wave generator is controlled to inject a second sound wave of a second frequency into the cable based on the type of fault in the cable; the second frequency is determined based on a mapping relationship between a preset fault type and a sound wave frequency, and the fault type; Based on the second sound wave signals collected by the sensor array when the second sound wave passes through each of the positions, a fault position corresponding to the fault type of the cable is determined.

2. The method according to claim 1, characterized in that The step of controlling the sound wave generator to inject a second sound wave of a second frequency into the cable based on the type of fault present in the cable comprises: Acquire humidity information and temperature information of the environment in which the cable is located; the humidity information includes a humidity mean value and a humidity change rate, and the temperature information includes a temperature mean value and a temperature change rate; Performing frequency domain analysis on each of the first sound wave signals to determine each of the frequency domain features of each of the first sound waves; each of the frequency domain features includes high-frequency energy, high-frequency peak amplitude, and spectrum entropy; Based on the high-frequency energy, high-frequency peak amplitude and spectrum entropy of each of the first sound wave signals, respectively construct sound wave features, and based on the humidity mean value and the humidity change rate, and the temperature mean value and the temperature change rate, construct environmental features; When the feature correlation between the acoustic wave feature and the environmental feature is less than a correlation threshold, based on the type of fault existing in the cable, the acoustic wave generator is controlled to inject a second acoustic wave of a second frequency into the cable.

3. The method according to claim 2, characterized in that Each of the frequency domain features further includes: high frequency energy ratio and spectrum main frequency; the method further includes: Performing time-frequency analysis on each of the first sound wave signals to determine each of the time domain features of each of the first sound wave signals; each of the time domain features includes time domain energy, signal amplitude, maximum signal amplitude, minimum signal amplitude, rising time corresponding to the maximum signal amplitude, and falling time corresponding to the minimum signal amplitude; Analyze each time domain feature of each of the first sound wave signals to determine a time domain feature trend corresponding to each of the time domain features; Analyze each frequency domain feature of each of the first sound wave signals to determine a frequency domain feature trend corresponding to each of the frequency domain features; The fault type of the cable is determined based on the matching results of each of the time domain characteristic trends and / or each of the frequency domain characteristic trends and the preset fault characteristic trends corresponding to each preset fault type.

4. The method according to claim 3, characterized in that Also includes at least one of the following: Item 1: When the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, the frequency domain characteristic trend corresponding to the spectrum entropy shows an upward trend, and the frequency domain characteristic trend corresponding to the main frequency of the spectrum shows a shift trend, it is determined that the fault type of the cable is a partial discharge fault; Item 2: When the time domain characteristic trend corresponding to the maximum amplitude of the signal shows a downward trend, the time domain characteristic trend corresponding to the time domain energy shows a downward trend, and the time domain characteristic trend corresponding to the rising time or the falling time shows an upward trend, and the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, it is determined that the fault type of the cable is an insulation moisture fault; Item 3: When the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, the time domain characteristic trend corresponding to the signal amplitude shows a slowly downward trend, and the frequency domain characteristic trend corresponding to the spectrum entropy shows an upward trend, it is determined that the fault type of the cable is an insulation aging fault; Item 4: When the time domain characteristic trend corresponding to the signal amplitude shows a fluctuating trend, the time domain characteristic trend corresponding to the high-frequency energy ratio shows an upward trend, and the time domain characteristic trend corresponding to the main frequency of the spectrum shows a shifting trend, it is determined that the fault type of the cable is a mechanical damage fault.

5. The method according to claim 1, characterized in that The sensor array includes a plurality of sensors; the second acoustic wave signals collected by the sensor array when the second acoustic wave passes through each of the positions, determining the fault position corresponding to the fault type of the cable, comprises: Analyze each of the second sound wave signals collected by the sensor array when the second sound wave passes through each of the positions to determine a sound wave propagation velocity matched by the sensor corresponding to the second sound wave signal and a sound wave propagation distance corresponding to the sound wave propagation velocity; Based on the sound wave propagation distance matched with each of the sensors and the installation position of each of the sensors, a fault position corresponding to the fault type of the cable is determined.

6. The method according to claim 5, characterized in that The method further comprises: Acquire a first humidity and a first temperature at a location where each of the sensors is located; For each of the sensors, an adjusted sound wave propagation velocity matching the sensor is determined based on the sound wave velocity under standard conditions, the temperature correction coefficient, the humidity correction coefficient, the standard temperature, the standard humidity, and the first humidity and the first temperature.

7. The method according to claim 1, characterized in that The method further comprises: Acquire a second humidity, a second temperature and a first vibration signal at the fault location; In the case where the fault type is an insulation moisture fault, if the second humidity is greater than the humidity at a position adjacent to the fault position, and the first vibration signal is in an abnormal state, outputting a first verification pass result; In the case where the fault type is an insulation aging fault, if the second temperature is greater than the temperature at a position adjacent to the fault position, and the first vibration signal is in a normal state, a second verification pass result is output.

8. The method according to claim 3, characterized in that The determining the fault type of the cable based on the matching results of the time domain characteristic trends and / or the frequency domain characteristic trends and the preset fault characteristic trends corresponding to the preset fault types includes: Determine a plurality of candidate fault types based on the matching results of the time domain characteristic trends and / or the frequency domain characteristic trends and the preset fault characteristic trends corresponding to the preset fault types; The fault type of the cable is determined based on each of the candidate fault types and the matching scores corresponding to the candidate fault types.

9. The method according to claim 1, characterized in that: The method further comprises: In the case of periodically injecting sound waves into the fault location, predicting the fault deterioration trend corresponding to the fault location based on the fault characteristics corresponding to the fault location; When the trend value of the fault deterioration trend representation exceeds a threshold, a fault maintenance suggestion matching the fault deterioration trend is output.

10. A cable insulation fault detection system, characterized in that: The method applied to any one of claims 1 to 9, wherein the system comprises: an acoustic wave generator, used for injecting a first acoustic wave of a preset frequency into the cable; A sensor array, used for collecting first sound wave signals when the first sound wave passes through various positions of the cable; A controller, for, when it is determined that the cable has a fault based on the signal analysis results of each of the first sound wave signals, controlling the sound wave generator to inject a second sound wave of a second frequency into the cable based on the type of fault in the cable, and controlling the sensor array to collect the second sound wave signal when the second sound wave passes through each of the positions; the second frequency is determined based on a mapping relationship between a preset fault type and a sound wave frequency, and the fault type; The controller is further configured to determine a fault position corresponding to a fault type of the cable based on each of the second sound wave signals.

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