Cable insulation fault detection method and system

Abstract

The application relates to a cable insulation fault detection method and system. The method comprises the following steps: in the case that an acoustic wave generator injects a first acoustic wave with 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; in the case that a signal analysis result based on each first acoustic wave signal shows that the cable has a fault, controlling the acoustic wave generator to inject a second acoustic wave with a second frequency into the cable based on a type of the fault existing in the cable; the second frequency is determined based on a mapping relationship between preset fault types and acoustic wave frequencies and the fault type; and determining a fault position corresponding to the fault type of the cable based on a second acoustic wave signal acquired by the sensor array when the second acoustic wave passes through each position. The method can be used for monitoring the cable in real time and expanding the cable detection scene.

Description

TECHNICAL FIELD

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

[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 the cable, double split cables (i.e. parallel cables) can be used to transmit power to increase the transmission capacity. When double split cables are used for power transmission, the two sub-cables in parallel operation may not be balanced due to factors such as line differences caused by path length, manufacturing process differences, impedance asymmetry caused by electromagnetic coupling between multiple cables, etc. When double split cables work under current imbalance conditions, the probability of failure of the cable insulation layer increases. The insulation layer of the cable 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 insulation layer of the cable.

[0003] Generally, electrical test methods can be used to detect insulation faults in cables. This method assesses the state of the cable insulation layer by measuring its electrical properties. However, this requires the power system to be powered off or disconnected before the cable can be tested for faults, making it difficult to monitor the cable in real time and limiting the detection scenarios. SUMMARY

[0004] Therefore, 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 the cable in real time and expand the cable detection scenarios.

[0005] In a first aspect, the present application provides a cable insulation fault detection method. The method comprises: in the case that a sound wave generator injects a first sound wave of a preset frequency into a cable, acquiring a first sound wave signal of the first sound wave when passing through each position of the cable based on a sensor array; in the case that a signal analysis result based on each first sound wave signal determines that the cable has a fault, controlling the sound wave generator to inject a second sound wave of a second frequency into the cable based on a fault type of the cable; the second frequency is determined based on a mapping relationship between the 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 of the second sound wave when passing through each position collected by the sensor array.

[0006] In one embodiment, based on the type of cable existing fault, the method of controlling the sound wave generator to inject a second sound wave of a second frequency into the cable comprises: obtaining humidity information and temperature information of the environment where the cable is located; the humidity information comprises humidity average and humidity change rate, and the temperature information comprises temperature average and 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 comprises high frequency energy, high frequency peak amplitude and spectral entropy; based on the high frequency energy, high frequency peak amplitude and spectral entropy of each first sound wave signal, sound wave features are constructed respectively, and based on the humidity average and humidity change rate, and the temperature average and temperature change rate, environment features are constructed; in the case that the correlation degree between the sound wave features and the environment features is less than a correlation degree threshold, based on the type of cable existing fault, the sound wave generator is controlled to inject a second sound wave of a second frequency into the cable.

[0007] In one embodiment, each frequency domain feature further comprises: high frequency energy ratio and spectral main frequency; the method further comprises: 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 comprises time domain energy, signal amplitude, signal maximum amplitude, signal minimum amplitude, rising time corresponding to signal maximum amplitude, and falling time corresponding to signal minimum amplitude; analyzing each time domain feature of each first sound wave signal to determine a time domain feature trend corresponding to each time domain feature; analyzing each frequency domain feature of each first sound wave signal to determine a frequency domain feature trend corresponding to each frequency domain feature; based on the matching results of each time domain feature trend and / or each frequency domain feature trend, and the pre-set fault feature trend corresponding to each pre-set fault type, the type of cable fault is determined.

[0008] In one embodiment, the method further comprises at least one of the following: the first item: in the case that the frequency domain feature trend corresponding to the high frequency energy ratio presents a downward trend, the frequency domain feature trend corresponding to the spectral entropy presents an upward trend, and the frequency domain feature trend corresponding to the spectral main frequency presents a deviation trend, the type of cable fault is determined as a partial discharge fault; the second item: in the case that the time domain feature trend corresponding to the signal maximum amplitude presents a downward trend, the time domain feature trend corresponding to the time domain energy presents a downward trend, and the time domain feature trend corresponding to the rising time or the falling time presents an upward trend, and the frequency domain feature trend corresponding to the high frequency energy ratio presents a downward trend, the type of cable fault is determined as an insulation damp fault; the third item: in the case that the frequency domain feature trend corresponding to the high frequency energy ratio presents a downward trend, and the time domain feature trend corresponding to the signal amplitude presents a slow downward trend, and the frequency domain feature trend corresponding to the spectral entropy presents an upward trend, the type of cable fault is determined as an insulation aging fault; the fourth item: in the case that the time domain feature trend corresponding to the signal amplitude presents a fluctuation trend, the time domain feature trend corresponding to the high frequency energy ratio presents an upward trend, and the time domain feature trend corresponding to the spectral main frequency presents a deviation trend, the type of cable fault is determined as a mechanical damage fault.

[0009] In an embodiment, the sensor array includes a plurality of sensors; the fault location corresponding to the fault type of the cable is determined based on the second sound wave signals of the second sound waves passing through each position collected by the sensor array, including: analyzing each second sound wave signal for the second sound wave signals of the second sound waves passing through each position collected by the sensor array, determining the sound wave propagation speed matched with the sensor corresponding to the second sound wave signal, and the sound wave propagation distance corresponding to the sound wave propagation speed; determining the fault location corresponding to the fault type of the cable based on the sound wave propagation distance matched with each sensor and the setting position of each sensor.

[0010] In an embodiment, the method further includes: obtaining the first humidity and the first temperature of the position where each sensor is located; for each sensor, determining the adjusted sound wave propagation speed matched with the sensor based on the sound wave speed 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.

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

[0012] In an embodiment, the fault type of the cable is determined based on the matching result 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, including: determining a plurality of candidate fault types based on the matching result 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; determining the fault type of the cable based on each candidate fault type and the matching degree score corresponding to the candidate fault type.

[0013] In an embodiment, the method further includes: 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 feature corresponding to the fault location; in the case that the trend value represented by the fault deterioration trend exceeds a threshold value, outputting a fault maintenance suggestion matched with the fault deterioration trend.

[0014] In a second aspect, the present application provides a cable insulation fault detection system, applied to the method of the first aspect or any one of the first aspect, the system comprising: a sound wave generator configured to inject a first sound wave of a preset frequency into a cable; a sensor array configured to collect a first sound wave signal of the first sound wave when passing through each position of the cable; and a controller configured to, in a case where it is determined that the cable has a fault based on a signal analysis result 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 a type of the fault of the cable, and control the sensor array to collect a second sound wave signal of the second sound wave when passing through each position; the second frequency is determined based on a mapping relationship between a preset fault type and a sound wave frequency, and the type of the fault; and the controller is further configured to determine a fault position corresponding to the type of the fault of the cable based on each second sound wave signal.

[0015] In a third aspect, the present application further provides a cable insulation fault detection device. The device comprises: an acquisition module configured to, in a case where a sound wave generator injects a first sound wave of a preset frequency into a cable, acquire a first sound wave signal of the first sound wave when passing through each position of the cable based on a sensor array; a processing module configured to, in a case where it is determined that the cable has a fault based on a signal analysis result 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 a type of the fault of the cable; the second frequency is determined based on a mapping relationship between a preset fault type and a sound wave frequency, and the type of the fault; and an analysis module configured to determine a fault position corresponding to the type of the fault of the cable based on a second sound wave signal of the second sound wave when passing through each position collected by the sensor array.

[0016] In a fourth aspect, the present application further provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program: in a case where a sound wave generator injects a first sound wave of a preset frequency into a cable, acquiring a first sound wave signal of the first sound wave when passing through each position of the cable based on a sensor array; in a case where it is determined that the cable has a fault based on a signal analysis result of each first sound wave signal, controlling the sound wave generator to inject a second sound wave of a second frequency into the cable based on a type of the fault of the cable; the second frequency is determined based on a mapping relationship between a preset fault type and a sound wave frequency, and the type of the fault; and determining a fault position corresponding to the type of the fault of the cable based on a second sound wave signal of the second sound wave when passing through each position collected by the sensor array.

[0017] In a fifth aspect, the present application also provides a computer readable storage medium. The computer readable storage medium stores a computer program. When the computer program is executed by a processor, the following steps are implemented. In a case where a sound wave generator injects a first sound wave of a preset frequency into a cable, a first sound wave signal of the first sound wave passing through each position of the cable is acquired based on a sensor array. In a case where a fault of the cable is determined based on a signal analysis result of each first sound wave signal, a second sound wave of a second frequency is injected into the cable by the sound wave generator based on a fault type of 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. A fault position corresponding to the fault type of the cable is determined based on a second sound wave signal of the second sound wave passing through each position collected by the sensor array.

[0018] In a sixth aspect, the present application also provides a computer program product. The computer program product includes a computer program. When the computer program is executed by a processor, the following steps are implemented. In a case where a sound wave generator injects a first sound wave of a preset frequency into a cable, a first sound wave signal of the first sound wave passing through each position of the cable is acquired based on a sensor array. In a case where a fault of the cable is determined based on a signal analysis result of each first sound wave signal, a second sound wave of a second frequency is injected into the cable by the sound wave generator based on a fault type of 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. A fault position corresponding to the fault type of the cable is determined based on a second sound wave signal of the second sound wave passing through each position collected by the sensor array.

[0019] The cable insulation fault detection method, system, device, computer device, computer readable storage medium, and computer program product described above, in a case where a sound wave generator injects a first sound wave of a preset frequency into a cable, a first sound wave signal of the first sound wave passing through each position of the cable can be acquired based on a sensor array, and in a case where a fault of the cable is determined based on a signal analysis result of each first sound wave signal, a second sound wave of a second frequency is injected into the cable by the sound wave generator based on a fault type of the cable, and then based on a second sound wave signal of the second sound wave passing through each position collected by the sensor array, a fault position corresponding to the fault type of the cable can be determined. Thus, the present application can realize insulation fault detection of the cable without power system outage or power supply disconnection, solve the problem of limited detection scenarios, and expand the detection scenarios. Moreover, the present application detects the insulation fault of the cable from the perspective of the sound wave signal, and the sound wave signal has high sensitivity to the fault existing in the cable, so that the accuracy of the cable insulation fault detection can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 FIG. 1 is an application environment diagram of a cable insulation fault detection method in an embodiment;

[0021] Figure 2 a flowchart of a cable insulation fault detection method in an embodiment;

[0022] Figure 3 a structural diagram of a cable insulation fault detection system in an embodiment;

[0023] Figure 4 a flowchart of a cable insulation fault detection method in another embodiment;

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

[0025] Figure 6 an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0026] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0027] Cables are widely used in power systems and industrial fields, and the health status of their insulation layers is crucial for the stability, safety and long-term reliability of power transmission. With the increasing demand for power transmission and the increasing complexity of systems, cable insulation fault problems have become increasingly prominent.

[0028] Typically, the types of cable insulation layer faults can include, but are not limited to, insulation aging, cracks and damage, moisture intrusion, and partial discharge, etc. Among them, 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 decline in electrical performance; cracks and damage are used to reflect that external mechanical stress or excessive bending can cause cracks or local 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 environmental humidity, resulting in uneven distribution of the electric field inside the cable, and thus causing insulation failure; partial discharge is used to reflect that small defects in the insulation material can cause partial discharge, thereby accelerating the aging and damage of the insulation layer.

[0029] As mentioned above, electrical test methods can be used to detect insulation faults of cables, including insulation resistance testing, dielectric loss factor testing, etc. For electrical test methods, on the one hand, it is necessary to power off or disconnect the power supply before detecting the cable faults, which is difficult to achieve online real-time monitoring and the detection scenarios are limited; on the other hand, the electrical test methods have low accuracy in locating the fault point, especially in long-distance cable systems, it is difficult to provide accurate fault location information.

[0030] In some cases, partial discharge detection methods can also be used to detect insulation faults in cables. Partial discharge detection is an advanced method of insulation fault detection that identifies potential insulation defects by monitoring small discharges in the insulation material. Although partial discharge detection can detect signs of insulation aging or defects early, it 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 use 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 assesses the state of the internal insulation layer by detecting temperature anomalies on the surface of the cable. Although this method can quickly detect overheating phenomena caused by cable faults, it is essentially a detection of external heat from the cable and cannot deeply probe the specific conditions inside the insulation layer, making it difficult to provide precise positioning information for faults. At the same time, thermal imaging is only sensitive to major faults that have already occurred (such as heating caused by severe overloading), and cannot detect early minor faults.

[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 address these challenges, detection methods based on acoustic characteristics have gradually become an important development direction for cable fault detection technology due to their high sensitivity, non-invasiveness, and suitability in complex environments.

[0033] Detection methods based on acoustic characteristics assess the health of cables by analyzing the propagation characteristics of sound waves in insulation materials. The advantage of acoustic detection methods is their non-invasiveness and high sensitivity to subtle defects, especially in insulation materials, where sound waves can detect small defects inside the material and provide precise fault location information.

[0034] The acoustic emission technology used in the detection method based on acoustic characteristics is a non-destructive testing method commonly used to detect micro-cracks in materials. This method captures acoustic signals (i.e., acoustic emission) generated inside the material to determine its internal defects. Acoustic emission technology can detect cracks or local damage in the insulation layer, making it suitable for early detection of cable insulation faults. However, this technology relies on high-precision sensors and complex signal processing algorithms, has low tolerance to noise environments, and has high implementation costs. The pulse echo technology sends high-frequency acoustic pulses into the cable interior. When the acoustic wave encounters a defect, it reflects, the sensor receives the reflected signal, and by analyzing the time delay of the echo, the location of the fault point can be calculated. Although the pulse echo technology performs well in laboratory conditions, in practical applications, due to the complex cable environment and the variability of acoustic wave propagation paths, the echo signal is easily disturbed by external noise and multiple reflections, affecting the accuracy of detection.

[0035] As can be seen from the above, although the detection method based on acoustic characteristics has theoretical advantages, existing acoustic detection technologies still face many challenges in practical applications, such as insufficient precision, real-time performance, and anti-interference ability. To address the deficiencies in detection accuracy, real-time performance, and anti-interference ability in complex environments in existing technologies, a specific frequency of acoustic signals can be injected into the cable, combined with a distributed sensor array, time and frequency domain analysis, and machine learning methods to achieve high-precision detection and accurate positioning of cable insulation faults. This can effectively improve the accuracy and real-time performance of fault detection, enabling not only the identification of subtle defects in the cable insulation layer but also the provision of early warnings in the event of a fault, thereby helping operators take timely maintenance measures and avoiding major failures.

[0036] It should be understood that when the acoustic wave generator injects acoustic waves into the cable, acoustic waves can form propagating waves inside the cable, which reflect, transmit, and attenuate when encountering defects in the insulation layer. Therefore, it can provide a basis for cable insulation fault detection. For example, when the acoustic wave encounters a fault point in the insulation layer, part of the acoustic signal is reflected, so by receiving and analyzing these reflected signals, the location of the fault can be inferred; the transmission characteristics (such as frequency, intensity, etc.) of the acoustic wave also change after passing through the fault location, so by analyzing the transmitted signals, the nature and severity of the fault can be further confirmed.

[0037] Therefore, in one embodiment, as 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 injects a first sound wave of a preset frequency into the cable. The sensor array 104 collects the first sound wave signals as it passes through various locations on the cable. The controller 106, based on signal analysis results of the first sound wave signals, determines that a cable fault exists, and, based on the fault type, controls the sound wave generator to inject a second sound wave of a second frequency into the cable, and controls the sensor array to collect the second sound wave signals as it passes through various locations. The controller 106 is also used to determine the fault location corresponding to the fault type of the cable based on the second sound wave signals.

[0038] In one embodiment, such as Figure 2 As shown, a method for detecting cable insulation faults is provided, which can be applied to... Figure 1 Taking controller 106 as an example, the explanation includes the following steps:

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

[0040] The frequency of the acoustic wave generator is adjustable; for example, it can generate acoustic signals from 20 kHz to 500 kHz. The generator's configuration is unrestricted, as long as the sensor array can fully cover the cable's length, ensuring comprehensive monitoring of every potential fault location. For instance, the generator can be positioned relative to the length of the cable. It should be understood that an acoustic signal rate of 20 kHz to 500 kHz effectively excites various potential defects in the cable insulation layer and ensures effective propagation of sound waves within the cable material. For example, high-frequency sound waves around 500 kHz have shorter wavelengths, allowing them to penetrate the material's microstructure and reveal potential defects invisible to the naked eye, such as micro-cracks or aging areas in the insulation layer. Low-frequency sound waves in the 20 kHz to 100 kHz range can penetrate thicker cable material layers, effectively detecting defects deep within the insulation layer (such as severe aging or deep damage). Therefore, by combining sound wave signals of different frequencies, it is possible to comprehensively cover various fault conditions of cable insulation layers and realize defect detection at different depths and scales.

[0041] The sound wave generator can inject the first sound wave into the cable in various forms. For example, the first sound wave can be injected into the cable in the form of a pulse, i.e., the first sound wave propagates in the cable in the form of a pulse signal. The pulse signal has better time resolution, which can more accurately capture the propagation path and reflection points of the sound wave in the cable, and the pulse signal is also easy to distinguish from other electromagnetic signals, thereby reducing the influence of interference. In some cases, the first sound wave can be injected into the conductor or the insulating layer of the cable based on the coupler.

[0042] The sensor array is configured to receive the reflected and transmitted signals of the first sound wave in the cable and obtain the sound wave signals. The sensor array includes a plurality of sensors, so that the sound wave signals of the first sound wave propagating through each position of the cable can be captured by the sensor array. It should be understood that, for abnormal conditions in the insulating layer of the cable, the intensity, frequency change, and time delay of the first sound wave received by each sensor are different, thereby accurately implementing 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 the timing accuracy of the signals. In some embodiments, the sensor array includes a plurality of sensors, which can be uniformly or non-uniformly arranged along the length direction of the cable. For example, the distance between adjacent sensors is less than or equal to a preset distance, which 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 can be detected by the sensor is not limited, for example, the sensor can detect sound wave signals within 20 kHz to 500 kHz to ensure that various potential defects in the insulating 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 the situation that the contact is poor due to the movement or vibration of the cable, in some embodiments, the sensor can be fixed on the surface of the cable using a mounting clamp or a bracket 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 some embodiments, in order to ensure the quality of signal acquisition, 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, in the case of arranging the sensors on the sensor surface, the control signal generator injects an acoustic wave with a fixed frequency and a fixed amplitude into the cable, so that when the acoustic wave propagates along the cable, each sensor can collect the corresponding acoustic wave signal; the control data acquisition system synchronously collects the acoustic wave signals collected by each sensor based on the sampling frequency, and obtains the acoustic wave signals collected by each sensor from the data acquisition system. The fixed frequency can be 100 kHz, the fixed amplitude can be 5V (volts), and the sampling frequency can be 2MHz (megahertz). The above values can also be set in other ways.

[0045] Further, 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, rising time corresponding to the signal maximum amplitude, and signal duration; the acoustic wave signals are analyzed in the frequency domain to extract the amplitude and phase of the main frequency component. Further, the signal amplitude, signal peak value, rising time corresponding to the signal maximum amplitude, signal duration, amplitude and phase of the main frequency component corresponding to the acoustic wave signals are compared with the respective standard values. In the signal amplitude, signal peak value, rising time corresponding to the signal maximum amplitude, signal duration, amplitude and phase of the main frequency component corresponding to the acoustic wave signals, when there is at least one parameter value with a difference from the standard value greater than a first preset value, it is determined that the sensor corresponding to the acoustic wave signal has a fault; output the maintenance prompt information for the sensor with a fault to remind the calibration and maintenance of the sensor with a fault. Alternatively, the sensor adjacent to the sensor corresponding to the acoustic wave signal is determined, and the signal amplitude, signal peak value, rising time corresponding to the signal maximum amplitude, and signal duration of the acoustic wave signal are compared with the signal amplitude, signal peak value, rising time corresponding to the signal maximum amplitude, and signal duration of the acoustic wave signal collected by the adjacent sensor, respectively. In the signal amplitude, signal peak value, rising time corresponding to the signal maximum amplitude, signal duration, amplitude and phase of the main frequency component corresponding to the acoustic wave signal, in the case that there is at least one parameter value with a difference from the parameter value corresponding to the adjacent sensor greater than a second preset value, and the adjacent sensor does not have a fault, it is determined that the sensor corresponding to the acoustic wave signal has a fault, and the maintenance prompt information for the sensor corresponding to the acoustic wave signal is output to remind the calibration and maintenance of the sensor corresponding to the acoustic wave signal and the adjacent sensor.

[0046] Further, in the case that the sensor has been calibrated and maintained, the control signal generator re-injects the acoustic wave of a fixed frequency and a fixed amplitude into the cable; based on the signal amplitude corresponding to the re-acquired acoustic wave signal of each sensor, the rising time corresponding to the signal peak value, the signal maximum amplitude, the signal duration, the amplitude and phase of the main frequency component, it is determined whether the sensor corresponding to the acoustic 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 acoustic wave signal is not faulty, or in the case that the difference between all parameter values and the parameter values corresponding to the adjacent sensor is less than or equal to the second preset value, and the adjacent sensor is not faulty, it is determined that the sensor corresponding to the acoustic wave signal is not faulty. In some embodiments, calibration and maintenance of each sensor in the sensor array can also be implemented at different times or in different environments, and other settings can also be provided, which are not limited in the present embodiment.

[0047] S204, in the case that the cable exists a fault based on the signal analysis result of each first acoustic wave signal, the control signal generator injects a second acoustic wave of a second frequency into the cable based on the type of the fault existing in the cable.

[0048] The second frequency is determined based on a mapping relationship between the preset fault type and the acoustic wave frequency, and the fault type, so that when the acoustic wave of the same frequency as the acoustic wave frequency is injected into the cable, the position of the fault corresponding to the fault type in the cable can be detected. Specifically, from the mapping relationship between the fault type and the acoustic wave frequency, the preset fault type consistent with the fault type existing in the cable is determined; the acoustic wave frequency corresponding to the matched 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 in 20 kHz to 50 kHz can be determined as the second frequency; when the preset fault type is crack or bubble, any frequency in 300 kHz to 500 kHz can be determined as the second frequency.

[0049] The implementation of determining that the cable exists a fault based on the signal analysis result of each first acoustic wave signal is not limited, and examples are given below in combination with possible implementation manners. In one embodiment, in the case that the preset frequency is in the first frequency range, the frequency domain analysis is performed on each first acoustic wave signal to obtain a preset frequency domain feature; the preset frequency domain feature includes a high frequency component; statistical analysis is performed on the high frequency component corresponding to each first acoustic wave signal to obtain a high frequency component trend result, and if the high frequency component trend result presents a downward trend, or the high frequency component trend result represents that the high frequency component gradually disappears, it is determined that the fault type existing in the cable is a small crack or a bubble.

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

[0051] In some embodiments, based on each first acoustic wave signal, if it is determined that the peak amplitude of the corresponding high-frequency region of the frequency spectrum presents a downward trend, and the high-frequency component energy presents a downward trend, the first frequency can be reduced, i.e., the second frequency is the frequency after the first frequency is reduced. For example, it can be adjusted from 500 kHz to 100 kHz or 50 kHz. Thus, the low-frequency signal has strong penetration ability and 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 signal time delay corresponding to the signal presents an upward trend, and the waveform of the acoustic wave signal presents a fuzzy trend or the amplitude change trend presents a downward trend, the first frequency can be increased, i.e., the second frequency is the frequency after the first frequency is increased. For example, it can be adjusted from 50 kHz to 200 kHz or 300 kHz. Thus, the high-frequency signal is more suitable for detecting small defects (such as cracks, bubbles), can provide higher resolution, and helps to locate the reason for the reflection signal blur.

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

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

[0055] S206, based on the second acoustic wave signals of the second acoustic wave collected by the sensor array when passing through each position, determines the fault position corresponding to the fault type of the cable.

[0056] The fault position refers to a position of a fault existing in the cable, and the fault types corresponding to different fault positions can be the same or different. The implementation of determining the fault position 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 trained based on a plurality of historical acoustic wave signals and the fault positions corresponding to each historical acoustic wave signal, so that each second acoustic wave signal is input into the fault detection model, and the fault position corresponding to the fault type of the cable can be determined. Alternatively, the fault position can also be determined based on the similarity between each second acoustic wave signal.

[0057] In one embodiment, when the severity of the fault type is greater than a preset degree, a 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 a major fault, 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 system downtime risk caused by cable faults. The fault detection includes information such as fault type, fault position corresponding to the fault type, severity, and maintenance recommendations; the fault severity has a corresponding relationship with the fault type, for example, the severity of the partial discharge fault is less than that of the 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, which is convenient for subsequent analysis and tracking. In some cases, the fault detection model can also be updated based on the fault position corresponding to each second acoustic wave signal, so that as the data accumulates, the fault detection model can be continuously optimized to ensure the generalization ability and adaptability of the model.

[0059] Based on Figure 2 As shown in the method, in the case that the acoustic wave generator injects a first acoustic wave of a preset frequency into the cable, the first acoustic wave signal of the first acoustic wave passing through each position of the cable can be acquired based on the sensor array, and in the case that the cable has a fault based on the signal analysis result of each first acoustic wave signal, the second acoustic wave of a second frequency is injected into the cable based on the type of the fault existing in the cable, and then the second acoustic wave signal of the second acoustic wave passing through each position acquired based on the sensor array can be used to determine the fault position corresponding to the fault type of the cable. Thus, the present application does not need to power off or disconnect the power supply of the power system, and can also realize the insulation fault detection of the cable, solving the problem of limited detection scenarios and expanding the detection scenarios. Moreover, the present application detects the insulation fault of the cable from the perspective of the acoustic wave signal, and the acoustic wave signal has high sensitivity to the fault existing in the cable, so that the accuracy of the insulation fault detection of the cable can be improved.

[0060] Based onFigure 2 As shown, the type of cable fault and the position corresponding to the type of fault can be determined by the way of inputting the secondary sound wave into the cable. In an embodiment, the type of fault and the position of the fault can also be determined based on each first sound wave signal. Specifically, time domain analysis is performed on each first sound wave signal to obtain a target time domain feature, and frequency domain analysis is performed on each first sound wave signal to obtain a target frequency domain feature; the target time domain feature and the target frequency domain feature are input into a fault diagnosis model to determine the type of fault, the position of the fault, and a confidence score corresponding to the type of fault. Thus, through intelligent detection, human intervention can be reduced, and the accuracy and efficiency of fault detection can be improved.

[0061] In an embodiment, the cable insulation fault detection system can further include a detection device configured to detect humidity information and temperature information of an environment in which the cable is located; the humidity information includes a humidity average and a humidity change rate, and the temperature information includes a temperature average and a temperature change rate. Specifically, based on the type of fault existing in the cable, the sound wave generator is controlled to inject a second sound wave of a second frequency into the cable, including the following steps:

[0062] S31, frequency domain analysis is performed 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 spectral entropy; wherein the high-frequency peak amplitude refers to the high-frequency peak amplitude, and the high-frequency energy refers to the energy in the high-frequency range (such as 100 kHz to 500 kHz).

[0063] S32, based on the high-frequency energy, the high-frequency peak amplitude, and the spectral entropy of each first sound wave signal, a sound wave feature is constructed, and based on the humidity average and the humidity change rate, and the temperature average and the temperature change rate, an environment feature is constructed.

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

[0065]

[0066] wherein, represents the high-frequency energy of the first sound wave signal collected by the i th sensor in the sensor array, ; represents the high-frequency peak amplitude of the first sound 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] The environment feature E satisfies: wherein, T represents the temperature average, H represents the humidity average, represents the temperature change rate, represents the humidity change rate.

[0068] S33, in a case where the feature correlation degree between the sound wave feature and the environment feature is less than the correlation degree threshold, based on the fault type of the cable, controlling the sound wave generator to inject a second sound wave of a second frequency into the cable.

[0069] The manner of determining the feature correlation degree between the sound wave feature and the environment feature is not limited. For example, the Pearson correlation coefficient between the sound wave feature and the environment feature can be determined as the feature correlation degree; or the Pearson correlation coefficient between the high-frequency energy corresponding to each sensor and the environment feature can be determined as a first sub-correlation degree; the Pearson correlation coefficient between the high-frequency peak amplitude corresponding to each sensor and the environment feature can be determined as a second sub-correlation degree; the Pearson correlation coefficient between the spectral entropy corresponding to each sensor and the environment feature can be determined as a third sub-correlation degree; and the feature correlation degree between the sound wave feature and the environment feature is determined based on the first sub-correlation degree, the second sub-correlation degree, and the third sub-correlation degree, that is, the feature correlation degree includes the first sub-correlation degree, the second sub-correlation degree, and the third sub-correlation degree.

[0070] In some embodiments, the correlation degree threshold includes a first sub-threshold, a second sub-threshold, and a third sub-threshold, and in a case where the first sub-correlation degree is less than the first sub-threshold, the second sub-correlation degree is less than the second sub-threshold, and the third sub-correlation degree is less than the third sub-threshold, it is determined that the feature correlation degree between the sound wave feature and the environment feature is less than the correlation degree 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 content of S31-S33, in a case where it is determined that the cable has a fault based on the signal analysis result of each first sound wave signal, if it is detected that the feature correlation degree between the sound wave feature and the environment feature is less than the correlation degree threshold, it can be concluded that the cable is not caused by environmental factors. The accuracy of cable fault detection can be further improved, and further, based on the fault type of the cable, the sound wave generator is controlled to inject a second sound wave of a second frequency into the cable, which can realize the detection process of the cable fault position.

[0072] Based on the content of S31-S33, from the perspective of the feature correlation degree between the sound wave feature and the environment feature, 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 manner:

[0073] In one embodiment, the humidity information includes a plurality of humidities, the temperature information includes a plurality of temperatures, and the comparison result of the high-frequency signal change trend of the plurality of first acoustic wave signals and the humidity change trend corresponding to the plurality of humidities or the comparison result of the high-frequency signal change trend and the temperature change trend corresponding to the plurality of temperatures can be used to determine whether the environmental factor has an impact on the detection result of the cable fault, such as a humidity increase stage or a temperature rapid heating stage, in which the high-frequency signal attenuation is obvious, or the temperature rapid heating stage can be determined as the environmental factor leading to the detection result of the cable fault.

[0074] In one embodiment, the clustering algorithm (such as K-means method) can be used to classify the plurality of first acoustic wave signals into different categories, and for the signals of the same category, it is determined whether the signal change is consistent with the electrical fault characteristics. If yes, it can be determined that the environmental factor does not lead to the detection result of the cable fault, i.e., the cable itself has a fault. Otherwise, the cable fault is caused by the environmental factor.

[0075] In one embodiment, for the first acoustic wave signals collected by each sensor, the first acoustic wave signals are synchronously analyzed in time domain and frequency domain. If the 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 the environmental factor does not lead to the detection result of the cable fault.

[0076] In some embodiments, if it is determined that the environmental factor leads to the detection result of the cable fault, the maintenance information is output to remind the maintenance personnel to maintain the cable. For example, if the humidity rises from 40% to 80% and the high-frequency energy attenuates by 30%, it can be determined that the moisture intrusion leads to the high-frequency attenuation, and thus it can be concluded that the moisture causes the detection result of the cable fault. Therefore, the maintenance information of checking the sealing property of the cable, drying treatment, and sealing repair can be output. For example, if the high-frequency energy attenuates by 50% at the same time point, but the humidity and temperature do not change significantly, it can be concluded that the environmental factor does not lead to the detection result of the cable fault, i.e., the cable itself has a fault, and the fault type can be a crack in the insulation layer. Therefore, the maintenance suggestion of local insulation repair or replacement of the damaged cable can be output.

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

[0078] S41, time-frequency analysis is performed 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, signal maximum amplitude, signal minimum amplitude, rising duration corresponding to the signal maximum amplitude, and falling duration corresponding to the signal minimum amplitude; wherein the rising duration corresponding to the signal maximum amplitude refers to the duration experienced when rising to the signal maximum amplitude, and the falling duration corresponding to the signal minimum amplitude refers to the duration experienced when falling to the signal minimum amplitude.

[0079] S42, analysis is performed on each time domain feature of each first sound wave signal to determine a time domain feature trend corresponding to each time domain feature.

[0080] S43, analysis is performed on each frequency domain feature of each first sound wave signal to determine a frequency domain feature trend corresponding to each frequency domain feature.

[0081] S44, based on a matching result of each time domain feature trend and / or each frequency domain feature trend and a preset fault feature trend corresponding to each preset fault type, a fault type of the cable is determined.

[0082] The implementation manner of determining the fault type of the cable based on the matching result 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 is not limited, and several modes thereof are exemplarily illustrated below.

[0083] In one embodiment, in a case where the frequency domain feature trend corresponding to the high-frequency energy ratio presents a downward trend, the frequency domain feature trend corresponding to the spectral entropy presents an upward trend, and the frequency domain feature trend corresponding to the spectral main frequency presents a shift trend, the fault type of the cable is determined as a partial discharge fault. That is, in a case where the high-frequency energy significantly decreases, the spectral entropy increases, and the main frequency shifts, the fault type of the cable is determined as a partial discharge fault.

[0084] In one embodiment, in a case where the time domain feature trend corresponding to the signal maximum amplitude presents a downward trend, the time domain feature trend corresponding to the time domain energy presents a downward trend, and the time domain feature trend corresponding to the rising duration or the falling duration presents an upward trend, and the frequency domain feature trend corresponding to the high-frequency energy ratio presents a downward trend, the fault type of the cable is determined as a moisture insulation fault. That is, in a case where the signal maximum amplitude gradually attenuates, the time domain energy decreases, and the rising / falling duration becomes longer, and the high-frequency energy ratio decreases, the fault type of the cable is determined as a moisture insulation fault.

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

[0086] In one embodiment, when the signal amplitude presents a fluctuation trend corresponding to the time domain feature trend, the high-frequency energy ratio presents an upward trend corresponding to the time domain feature trend, and the spectral main frequency presents a shift trend corresponding to the time domain feature trend, it is determined that the fault type of the cable is a mechanical damage fault. That is, when the signal amplitude fluctuates violently, the high-frequency energy abnormally enhances, and the spectral main frequency is unstable, it is determined that the fault type of the cable is a mechanical damage fault.

[0087] In one embodiment, the acoustic wave signal includes a reflected signal, if the signal time delay matched by the sensor corresponding to the acoustic wave signal is less than a delay threshold, and the amplitude of the reflected signal is greater than a preset amplitude threshold, it is determined that the fault type of the cable is a short-circuit fault; if the signal time delay matched by the sensor corresponding to 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, it is determined that the fault type of the cable is an open-circuit fault.

[0088] In one embodiment, the 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, it is determined that the fault type of the cable is an insulation damp fault; the envelope curve amplitude variation trend is obtained based on the envelope curve corresponding to each first acoustic wave signal, if the envelope curve amplitude variation trend presents a downward trend, it is determined that the fault type of the cable is an insulation aging fault; if the envelope curve amplitude variation trend has a regular peak, it is determined that the fault type of the cable is a partial discharge fault.

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

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

[0091] In some embodiments, if high-frequency signal attenuation is accompanied by high humidity, the fault type of the cable can also be determined as an insulation damp fault; and if insulation performance is accompanied by a decrease in high-temperature environment, resulting in a decrease in reflected signal amplitude, the fault type of the cable can be determined as a thermal aging fault.

[0092] Based on the above, the fault type of the cable can be determined based on the matching results of each time-domain feature trend and / or each frequency-domain feature trend and the pre-set fault feature trend corresponding to each pre-set fault type. The fault type can also be determined by the following method, which is as follows:

[0093] In one embodiment, the fault type of the cable can be determined by analyzing each first acoustic wave signal based on a fault type classification model. In some embodiments, a plurality of historical acoustic wave signals can be classified based on a classification algorithm; the classified historical acoustic wave signals are processed to determine insulation fault features; and a machine learning model is trained based on the insulation fault features, so as to obtain a fault classification model. That is, the number of fault type classification models can be multiple, and each fault type classification model corresponds to a fault type. Each first acoustic wave signal is input into each fault type classification model, and the fault type can be obtained, such as partial discharge, insulation aging, mechanical damage, etc. Thus, by training and learning a large amount of fault data, the accuracy of fault identification can be improved, different cable types and use environments can be adapted, manual intervention can be reduced through automatic fault identification, and the efficiency and accuracy of fault detection can be improved.

[0094] It should be understood that the cable interference and other noise sources in the environment where the cable is located can affect the signal acquisition accuracy, and in some cases, each first acoustic wave signal can be preprocessed, and based on the predicted each first acoustic wave signal, the fault type of the cable is determined. Among them, the preprocessing can include denoising processing, normalization processing, time alignment processing, gain adjustment processing, etc. Similarly, each second acoustic wave signal can also be preprocessed, and based on the preprocessed each second acoustic wave signal, the fault position corresponding to the fault type of the cable is determined. Among them, the wavelet transform or the band-pass filter can be used to remove the low-frequency noise and the 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 gain adjustment processing can be used to increase the signal representing the fault characteristics of the acoustic wave signal to ensure that the key fault characteristics will not be covered by the 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, and a preset time domain feature trend and a preset frequency domain feature region corresponding to the fault type. Among them, 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, in the case where 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. Among them, the similarity can be calculated based on the Euclidean distance, the dynamic time warping (DTW), or the cosine similarity method.

[0096] In one embodiment, the fault type of the cable is determined based on the matching result of each time domain feature trend and / or each frequency domain feature trend, and a preset fault feature trend corresponding to each preset fault type, including the following steps:

[0097] S51, determining a plurality of candidate fault types based on the matching result of each time domain feature trend and / or each frequency domain feature trend, and a preset fault feature trend corresponding to each preset fault type. Among them, the plurality of candidate fault types include at least two of the partial discharge fault, the insulation damp fault, the insulation aging fault, and the mechanical damage fault.

[0098] S52, determining the fault type of the cable based on each candidate fault type and the matching degree score corresponding to the candidate fault type.

[0099] The manner of determining the fault type of the cable based on each candidate fault type and the matching degree score corresponding to the candidate fault type is not limited. For example, the candidate fault type with the highest matching degree score can be determined as the fault type of the cable, or the candidate fault types are sorted according to the matching degree scores, and the top M candidate fault types are determined as the fault type of the cable.

[0100] Based on the content of S51-S52, in the case of determining multiple candidate fault types, the fault type of the cable is determined by combining the matching degree scores corresponding to the candidate fault types, which can improve the determination accuracy.

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

[0102] S61, for the second sound wave signals of the second sound waves passing through each position collected by the sensor array, analyzing each second sound wave signal to determine the sound wave propagation speed matched with the sensor corresponding to the second sound wave signal and the sound wave propagation distance corresponding to the sound wave propagation speed.

[0103] The sound wave sensing speed refers to the propagation speed of the second sound wave in the cable, and the implementation manner of determining the sound wave sensing speed matched with the sensor corresponding to the second sound wave signal is not limited. 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 sound wave sensing speed matched with the i-th sensor, that is, .

[0104] 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, and the implementation manner of determining the signal time delay of the sensor corresponding to the second sound wave signal is not limited. For example, generally, the signal collected by the sensor is a reflected signal, and the second sound wave signal includes the reflected signal, the cross-correlation function between the reflected signal and the transmitted signal is determined; the time point corresponding to the peak value of the cross-correlation function is determined as the signal time delay of the sensor corresponding to the second sound wave signal. The transmitted signal refers to the original signal emitted by the sound wave generator.

[0105] For example, represents the transmitted signal, represents the reflected signal corresponding to the i-th sensor, and the cross-correlation function between the reflected signal and the transmitted signal is satisfies:

[0106]

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

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

[0109] S62, based on the sound wave propagation distance matched with each sensor and the setting position of each sensor, determines the fault location corresponding to the fault type of the cable.

[0110] Specifically, the propagation distances of each sound wave, the installation locations, and the fault locations satisfy a set of nonlinear equations, which are expressed as follows:

[0111]

[0112] in, Indicates the location of the fault. This indicates the location of the i-th sensor. Let represent the sound wave propagation distance of the i-th sensor. Then, by solving the nonlinear equations based on the triangulation method or the least squares method, the fault location corresponding to the fault type of the cable can be determined.

[0113] For example, the sensor array includes a first sensor and a second sensor, and the location of the first sensor is represented as follows: The location of the second sensor is indicated as follows: The sound wave propagation distance of the first sensor is expressed as The sound wave propagation distance of the second sensor is expressed as The fault location was determined by the triangulation method. satisfy:

[0114]

[0115]

[0116] Given a distance of Q between the first and second sensors, the fault location is... satisfy:

[0117]

[0118] It should be understood that the temperature and humidity at the location of the sensor affect the speed of sound wave propagation, and in turn affect the sound wave propagation distance matched with the sensor, and in an embodiment, the following steps can also be included:

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

[0120] Wherein, the cable insulation fault detection system can further include a detection device, the detection device includes a plurality of sub-temperature detection devices and a plurality of sub-humidity detection devices, each sub-temperature detection device is used to detect the first temperature at the location of the sensor, and each sub-humidity detection device is used to detect the first humidity at the location of the sensor.

[0121] S72, for each sensor, determining an adjusted sound wave propagation speed matched with the sensor based on the sound wave speed 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.

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

[0123]

[0124] Wherein, The adjusted sound wave propagation speed is represented by The sound wave speed under standard conditions is represented by The temperature correction coefficient is represented by The humidity correction coefficient is represented by The temperature is represented by The humidity is represented by The first temperature is represented by The first humidity is represented by

[0125] In an embodiment, in the case of determining the adjusted sound wave propagation speed matched with the sensor, the sound wave propagation distance can be updated based on the adjusted sound wave propagation speed, so that when the fault position is determined based on the updated sound wave propagation distance, the influence of environmental factors on the determination process of the fault position can be avoided, and the determination accuracy of the fault position 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 where each sensor is located, and each first acoustic wave signal. Specifically, frequency domain analysis is performed on each first acoustic wave signal to determine the high-frequency component amplitude and the low-frequency energy corresponding to each first acoustic wave signal; the second vibration signal at the location where each sensor is located corresponding to each first acoustic wave signal is obtained, as well as the signal time delay matched with each sensor; based on the high-frequency component amplitude, the low-frequency energy, the signal time delay, the first temperature, the first humidity, and the vibration amplitude of the second vibration signal corresponding to each first acoustic wave signal, a comprehensive feature vector is constructed; based on machine learning model (such as decision tree, etc.) for classification and identification of comprehensive feature vector, the fault type can be determined.

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

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

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

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

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

[0132] Wherein, the first verification pass 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 pass 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, in the case of the fault type being insulation crack fault or mechanical damage fault, if the second temperature is greater than the temperature at the position adjacent to the fault location, 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 the location adjacent to the fault location, and the difference between the second temperature and the temperature at the adjacent location is within a set range, then a fourth verification result is output.

[0135] In some embodiments, each frequency domain feature may also include a low-frequency energy ratio. In the case of 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 location adjacent to the fault location, then the fifth verification 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 decreasing trend and the average time delay represented by the signal time delay trend is less than the average threshold, then the sixth verification 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 mean time delay represented by the signal time delay trend is greater than or equal to the mean threshold, then the seventh verification result is output.

[0138] Based on the content of S81-S83, after determining the fault location of the cable, the determination of insulation moisture faults and insulation aging faults can be further verified by combining the temperature, humidity and first vibration signal at the fault location, which can improve the reliability of the fault detection process.

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

[0140] S91, when sound waves are periodically injected into the fault location, predicts the fault deterioration trend corresponding to the fault location based on the fault characteristics corresponding to the fault location.

[0141] There are no restrictions on the method of predicting the fault deterioration trend based on the fault characteristics corresponding to the fault location. The following examples illustrate the possible implementation methods.

[0142] In one embodiment, if the sensor adjacent to the fault location is determined, frequency domain analysis and / or time domain analysis are performed on the acoustic signals collected by the adjacent sensor to obtain the fault characteristics corresponding to the fault location; the fault change trend in the fault characteristics is captured by time series analysis method; and the fault deterioration trend corresponding to the fault location is predicted by analyzing the fault change trend based on the trend prediction model.

[0143] The time series analysis methods may include, but are not limited to, sliding window methods and exponentially weighted moving averages (EWMA). Trend prediction models can be obtained by training machine learning models based on historical fault change trends. Machine learning models include, but are not limited to, linear regression, support vector machines (SVM), and long short-term memory networks (LSTM).

[0144] S92, if the trend value representing the fault deterioration trend exceeds the threshold, output fault maintenance recommendations that match the fault deterioration trend.

[0145] In some embodiments, the trend value represented by the reflection amplitude attenuation value in the fault deterioration trend can be characterized by the reflection amplitude attenuation value, or the trend value represented by the high-frequency energy attenuation value in the fault deterioration trend can be characterized by the high-frequency energy attenuation value. For example, if the reflection amplitude attenuation value exceeds 50%, or the high-frequency energy attenuation value exceeds 30%, a fault maintenance recommendation 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 a threshold, categorized as minor, moderate, and severe. A larger difference indicates a degree of deterioration closer to severe. For example, when the fault deterioration trend indicates crack expansion, corresponding maintenance recommendations could include: performing local insulation repairs or replacements; increasing the frequency of inspections to once a week; and installing a protective layer near the fault location to prevent further cracking. When the fault deterioration trend indicates increased moisture intrusion, corresponding maintenance recommendations could include: checking cable sealing and performing waterproof repairs; installing dehumidifiers to reduce ambient humidity; and drying the cable to slow down moisture erosion. When the fault deterioration trend indicates local insulation aging, corresponding maintenance recommendations could include: performing local cable replacement or reinforcement; increasing load monitoring to avoid high-current surges; and conducting regular insulation resistance tests.

[0147] Based on the content of S91-S92, after determining the fault location corresponding to the fault type of the cable, continuous monitoring of the fault location can be achieved 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, fault maintenance suggestions matching the fault deterioration trend are output, thereby enabling timely cable maintenance and improving the cable's operational reliability. Moreover, the trend prediction model further improves prediction efficiency and capability, making the fault detection process more intelligent and automated.

[0148] In conjunction with the above, in one embodiment, such as Figure 3The diagram illustrates the structure of a cable insulation fault detection system. The system includes a sound wave generator 102, a sensor array 104, and a data analysis and processing system 302. The sensor array 104 comprises N sensors, designated as the first sensor to the Nth sensor. The data analysis and processing system 302 includes a controller 106, a preprocessing device 3021, and a display interface 3022. The preprocessing device processes the sound wave signal and sends the processed signal to the controller 106, enabling the controller 106 to execute the method described in the above embodiment. The controller 106 also controls the display interface 3022 to display the verification pass result and fault detection report, etc.

[0149] exist Figure 3 Based on the above, in one embodiment, as shown Figure 4 As shown, a method for detecting cable insulation faults is provided, which can be applied to... Figure 1 Taking controller 106 as an example, the explanation includes the following steps:

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

[0151] S404, perform time-frequency analysis on each first acoustic wave signal to determine each time-domain characteristic of each first acoustic wave signal; each time-domain characteristic includes time-domain energy, signal amplitude, maximum signal amplitude, minimum signal amplitude, rise time corresponding to the maximum signal amplitude, and fall time corresponding to the minimum signal amplitude.

[0152] S406, perform frequency domain analysis on each first acoustic wave signal to determine the frequency domain characteristics of each first acoustic wave; each frequency domain characteristic includes high-frequency energy, high-frequency peak amplitude, spectral entropy, high-frequency energy ratio and spectral dominant frequency.

[0153] S408, analyze the time-domain characteristics of each first acoustic wave signal, determine the time-domain characteristic trend corresponding to each time-domain characteristic, and analyze the frequency-domain characteristics of each first acoustic wave signal, determine the frequency-domain characteristic trend corresponding to each frequency-domain characteristic.

[0154] S410, based on the matching results of the time domain characteristic trends and / or the frequency domain characteristic trends, as well as the preset fault characteristic trends corresponding to each preset fault type, determine the fault type of the cable.

[0155] S412, acquire humidity and temperature information of the environment where the cable is located. Humidity information includes average humidity and rate of change of humidity, and temperature information includes average temperature and rate of change of temperature.

[0156] S414, based on the high-frequency energy, high-frequency peak amplitude and spectral entropy of each first acoustic wave signal, construct acoustic wave features respectively, and construct environmental features based on the average humidity and humidity change rate, as well as the average temperature and temperature change rate.

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

[0158] S418, for the second sound wave signals collected by the sensor array when passing through each position, analyze each second sound wave signal to determine the sound wave propagation speed matched with the sensor corresponding to the second sound wave signal, and the sound wave propagation distance corresponding to the sound wave propagation speed.

[0159] S420 determines the fault location corresponding to the fault type of the cable based on the sound wave propagation distance matched with each sensor and the setting position of each sensor.

[0160] The specific details of S402-S420 can be found in the aforementioned description and will not be repeated here.

[0161] As discussed above, the cable insulation layer plays a crucial role in the safety and reliability of power transmission systems. However, with cable aging, environmental factors, or other external damage, the insulation layer is prone to failure, leading to instability in power system operation and even serious safety accidents. Existing insulation fault detection technologies largely rely on monitoring electrical parameters, making it difficult to accurately locate minute damage and lacking the ability to monitor in real time in complex environments. The cable insulation fault detection method provided in this 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, and long-distance transmission lines, as well as in monitoring cable insulation damage in complex environments, it can solve the problem of achieving high-precision, real-time detection in existing cable insulation fault detection methods. This can significantly reduce cable maintenance costs and fault risks, reduce downtime, and improve the operating efficiency and safety of power systems. Moreover, the method provided in this application also has significant application value in insulation monitoring of data centers and communication networks, effectively extending cable lifespan and ensuring system stability and reliability.

[0162] In this application, sound waves are injected into the cable. As the sound waves propagate within the cable, they change upon encountering the material medium (such as conductors, insulation layers, and sheaths). Furthermore, when encountering defects (such as cracks, aging, or moisture intrusion), reflection, transmission, or attenuation occurs. The reflected sound wave signals provide crucial information about the fault location and characteristics. The propagation characteristics of the sound wave signals (such as reflection time and amplitude variations) provide a fundamental basis for fault detection and location. Therefore, by further combining sensor array detection and machine learning methods, high-precision, real-time detection and location of cable insulation faults can be achieved. This method offers advantages such as non-invasiveness, high sensitivity, and strong real-time performance, significantly improving the safety and reliability of cable operation and providing a novel technical solution for the operation and maintenance of power and communication systems. Unlike traditional electrical detection methods, sound wave signals can propagate within the cable insulation layer and exhibit high sensitivity to minute defects in the insulation material. Therefore, by combining a sensor array to receive and analyze the reflection and transmission characteristics of sound waves in the cable, subtle faults in the cable insulation layer can be effectively identified, providing accurate fault location and type.

[0163] By performing time-domain analysis on acoustic signals, the temporal characteristics of the signals can be determined, such as arrival time, amplitude, and phase changes. Abnormal changes in these parameters can directly reflect the location and nature of defects encountered by the acoustic waves as they propagate within the cable. For example, by analyzing the arrival time of the acoustic signal, the system can calculate the propagation speed of the sound wave in the cable (i.e., the distance the sound wave travels), thereby inferring the fault location. For instance, amplitude changes can reflect the degree of attenuation of the sound wave when encountering a defect, while waveform distortion reveals the impact on the sound wave as it passes through the defect area. These time-domain characteristics are crucial for accurately locating faults and assessing their severity. Frequency domain analysis of acoustic signals based on Fourier transform can determine the frequency components of the signal. For example, changes in high-frequency components often reveal minute defects or aging areas in the insulation layer. High-frequency components are most easily affected by insulation defects during propagation. By identifying the attenuation and distortion of high-frequency signals, the system can detect minute defects in the insulation layer, such as cracks, voids, or damp areas. For instance, cracks typically lead to an enhancement of high-frequency components, while defects such as bubbles may cause spectral broadening. Through detailed frequency domain analysis, the system can identify different fault types. Therefore, by employing a joint time-frequency analysis method, the accuracy of fault identification can be improved.

[0164] Furthermore, in the process of cable insulation fault detection, this application can dynamically adjust the frequency of the sound waves injected into the cable based on the preset mapping relationship between the fault type and the sound wave frequency, so as to accurately locate the fault location corresponding to the fault type and improve the fault detection accuracy.

[0165] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0166] Based on the same inventive concept, this application also provides a cable insulation fault detection device for implementing the cable insulation fault detection method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the cable insulation fault detection device provided below can be found in the limitations of the cable insulation fault detection method described above, and will not be repeated here.

[0167] In one embodiment, such as 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 acquire first sound wave signals at various positions of the cable when a first sound wave of a preset frequency is injected into the cable by a sound wave generator, based on a sensor array; 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 fault type of the cable, if the cable is determined to have 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; the analysis module 506 is used to determine the fault location corresponding to the fault type of the cable based on the second sound wave signals at various positions collected by the sensor array.

[0168] In one embodiment, the processing module 504 is further configured to: acquire humidity and temperature information of the environment in which the cable is located; the humidity information includes the average humidity and the rate of change of humidity, and the temperature information includes the average temperature and the rate of change of temperature; perform frequency domain analysis on each first acoustic wave signal to determine the frequency domain characteristics of each first acoustic wave; each frequency domain characteristic includes high-frequency energy, high-frequency peak amplitude, and spectral entropy; construct acoustic wave characteristics based on the high-frequency energy, high-frequency peak amplitude, and spectral entropy of each first acoustic wave signal, and construct environmental characteristics based on the average humidity and the rate of change of humidity, as well as the average temperature and the rate of change of temperature; and, if the correlation between the acoustic wave characteristics and the environmental characteristics is less than a correlation threshold, control the acoustic wave generator to inject a second acoustic wave of a second frequency into the cable based on the fault type present in the cable.

[0169] In one embodiment, each frequency domain feature further includes: high-frequency energy ratio and spectral dominant frequency; the processing module 504 is further configured to: perform time-frequency analysis on each first acoustic wave signal to determine each time domain feature of each first acoustic wave signal; each time domain feature includes time domain energy, signal amplitude, maximum signal amplitude, minimum signal amplitude, rise time corresponding to the maximum signal amplitude, and fall time corresponding to the minimum signal amplitude; analyze each time domain feature of each first acoustic wave signal to determine the time domain feature trend corresponding to each time domain feature; analyze each frequency domain feature of each first acoustic wave signal to determine the frequency domain feature trend corresponding to each frequency domain feature; and determine the cable fault type based on the matching result 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 configured to perform at least one of the following: First, when the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a decreasing trend, the frequency domain characteristic trend corresponding to the spectral entropy shows an increasing trend, and the frequency domain characteristic trend corresponding to the spectral dominant frequency shows a shifting trend, the cable fault type is determined to be a partial discharge fault; Second, when the time domain characteristic trend corresponding to the maximum signal amplitude shows a decreasing trend, the time domain characteristic trend corresponding to the time domain energy shows a decreasing trend, the time domain characteristic trend corresponding to the rise duration or fall duration shows an increasing trend, and the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a decreasing trend, the cable fault type is determined to be an insulation moisture fault; Third, when the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a decreasing trend, the time domain characteristic trend corresponding to the signal amplitude shows a slow decreasing trend, and the frequency domain characteristic trend corresponding to the spectral entropy shows an increasing trend, the cable fault type is determined to be an insulation aging fault; Fourth, 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 increasing trend, and the time domain characteristic trend corresponding to the spectral dominant frequency shows a shifting trend, the cable fault type is determined to be a mechanical damage fault.

[0171] In one embodiment, the sensor array includes multiple sensors; the analysis module 506 is further configured 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 matched with the sensor corresponding to the second sound wave signal, and the sound wave propagation distance corresponding to the sound wave propagation speed; and determine the fault location corresponding to the fault type of the cable based on the sound wave propagation distance matched with each sensor and the setting position of each sensor.

[0172] In one embodiment, the analysis module 506 is further configured to: acquire the first humidity and first temperature at the location of each sensor; and for each sensor, determine an adjusted sound wave propagation speed that matches the sensor based on the sound wave velocity under standard conditions, temperature correction coefficient, humidity correction coefficient, standard temperature, standard humidity, and the first humidity and first temperature.

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

[0174] In one embodiment, the analysis module 506 is further configured to: determine multiple candidate fault types 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; and determine the fault type of the cable based on each candidate fault type and the matching degree score corresponding to the candidate fault type.

[0175] In one embodiment, the analysis module 506 is further configured to: predict the fault deterioration trend corresponding to the fault location based on the fault characteristics corresponding to the fault location when the sound waves are periodically injected into the fault location; and output fault maintenance suggestions that match the fault deterioration trend when the trend value represented by the fault deterioration trend exceeds a threshold.

[0176] Each module in the aforementioned cable insulation fault detection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0177] In one embodiment, a computer device is provided, which may be a cable insulation fault detection system, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data such as various first acoustic wave signals. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a cable insulation fault detection method.

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

[0179] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0180] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

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

[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, data stored, data displayed, 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 will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media 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), magnetic 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. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0184] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for detecting cable insulation faults, characterized in that, The method includes: When a first sound wave of a preset frequency is injected into a cable by a sound wave generator, the first sound wave signal is acquired based on a sensor array when the first sound wave passes through each position of the cable. If, based on the signal analysis results of each of the first acoustic wave signals, it is determined that the cable has a fault, the acoustic wave generator is controlled to inject a second acoustic wave of a second frequency into the cable based on the fault type of the cable; the second frequency is determined based on the mapping relationship between the preset fault type and the acoustic wave frequency, as well as the fault type. Based on the second acoustic wave signals collected by the sensor array when the second acoustic wave passes through each of the aforementioned locations, the fault location 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 fault type present in the cable includes: Obtain humidity and temperature information of the environment where the cable is located; the humidity information includes average humidity and rate of humidity change, and the temperature information includes average temperature and rate of temperature change. Frequency domain analysis is performed on each of the first acoustic wave signals to determine the frequency domain characteristics of each of the first acoustic waves; each frequency domain characteristic includes high-frequency energy, high-frequency peak amplitude, and spectral entropy; Based on the high-frequency energy, high-frequency peak amplitude, and spectral entropy of each of the first acoustic wave signals, acoustic wave features are constructed respectively, and environmental features are constructed based on the average humidity and the rate of humidity change, as well as the average temperature and the rate of temperature change. If the correlation between the acoustic wave characteristics and the environmental characteristics is less than a correlation threshold, the acoustic wave generator is controlled to inject a second acoustic wave of a second frequency into the cable based on the fault type present in the cable.

3. The method according to claim 2, characterized in that, The frequency domain features mentioned above also include: high-frequency energy ratio and spectral dominant frequency; the method further includes: Time-frequency analysis is performed on each of the first acoustic wave signals to determine the time-domain characteristics of each of the first acoustic wave signals; each time-domain characteristic includes time-domain energy, signal amplitude, maximum signal amplitude, minimum signal amplitude, rise time corresponding to the maximum signal amplitude, and fall time corresponding to the minimum signal amplitude; The time-domain characteristics of each of the first acoustic wave signals are analyzed to determine the time-domain characteristic trends corresponding to each of the time-domain characteristics; The frequency domain characteristics of each of the first acoustic wave signals are analyzed to determine the frequency domain characteristic trends corresponding to each frequency domain characteristic; The fault type of the cable is determined based on the matching results of the time-domain characteristic trends and / or the frequency-domain characteristic trends of each of the aforementioned fault types, as well as the preset fault characteristic trends corresponding to each preset fault type.

4. The method according to claim 3, characterized in that, It also includes at least one of the following: First item: If the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a decreasing trend, the frequency domain characteristic trend corresponding to the spectral entropy shows an increasing trend, and the frequency domain characteristic trend corresponding to the spectral main frequency shows a shifting trend, then the fault type of the cable is determined to be a partial discharge fault. Second item: If 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, the time-domain characteristic trend corresponding to the rising duration or the falling duration shows an upward trend, and the frequency-domain characteristic trend corresponding to the high-frequency energy ratio shows a downward trend, then the fault type of the cable is determined to be an insulation moisture fault. Third item: If the frequency domain characteristic trend corresponding to the high-frequency energy ratio shows a decreasing trend, the time domain characteristic trend corresponding to the signal amplitude shows a slow decreasing trend, and the frequency domain characteristic trend corresponding to the spectral entropy shows an increasing trend, then the fault type of the cable is determined to be an insulation aging fault. Fourth item: If 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, then the fault type of the cable is determined to be a mechanical damage fault.

5. The method according to claim 1, characterized in that, The sensor array includes multiple sensors; determining the fault location corresponding to the fault type of the cable based on the second acoustic wave signals collected by the sensor array when the second acoustic wave passes through each of the aforementioned locations includes: For the second sound wave signals collected by the sensor array when the second sound wave passes through each of the positions, each second sound wave signal is analyzed to determine the sound wave propagation speed matched with 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 acoustic wave propagation distance matched with each of the sensors and the setting position of each of the sensors, the fault location corresponding to the fault type of the cable is determined.

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

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

8. The method according to claim 3, characterized in that, The determination of the cable fault type 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 each preset fault type, includes: Based on the matching results of the time-domain feature trends and / or the frequency-domain feature trends of each of the aforementioned fault types, and the preset fault feature trends corresponding to each preset fault type, multiple candidate fault types are determined. The fault type of the cable is determined based on each of the candidate fault types and the matching score corresponding to the candidate fault types.

9. The method according to claim 1, characterized in that, The method further includes: By periodically injecting sound waves into the fault location, the fault deterioration trend corresponding to the fault location is predicted based on the fault characteristics corresponding to the fault location. If the trend value representing the fault deterioration trend exceeds a threshold, a fault maintenance recommendation matching the fault deterioration trend is output.

10. A cable insulation fault detection system, characterized in that, The system, applied to the method of any one of claims 1 to 9, comprises: A sound wave generator is used to inject a first sound wave of a preset frequency into the cable. A sensor array is used to collect the first acoustic wave signal at various locations along the cable as the first acoustic wave passes through. The controller is configured to, based on the signal analysis results of each of the first acoustic wave signals, determine that the cable has a fault, and based on the fault type of the cable, control the acoustic wave generator to inject a second acoustic wave of a second frequency into the cable, and control the sensor array to collect the second acoustic wave signal when the second acoustic wave passes through each of the aforementioned locations; the second frequency is determined based on a preset mapping relationship between the fault type and the acoustic wave frequency, and the fault type. The controller is also configured to determine the fault location corresponding to the fault type of the cable based on each of the second acoustic signals.

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