A power cable diagnostic method, device, medium, and product

Abstract

The application relates to the technical field of cable state monitoring, in particular to a power cable diagnosis method, device, medium and product, the method comprising the following steps: acquiring preliminary diagnosis parameters and diagnosis confirmation parameters collected by a sensor array, performing cable preliminary diagnosis based on the preliminary diagnosis parameters, when the cable preliminary diagnosis result is abnormal, performing cable secondary diagnosis based on the diagnosis confirmation parameters, and determining a cable final diagnosis result; through combination of the preliminary diagnosis parameters and the diagnosis confirmation parameters, the overall evaluation of the cable state can be realized, and the accuracy of the power cable diagnosis is improved. Furthermore, when the cable final diagnosis result is abnormal, an insulation resistance value is collected through insulation resistance test, a fault detection operation selection is performed based on the insulation resistance value, a target fault detection operation is determined, and the fault detection operation is performed on the cable according to the target fault detection operation. Finally, cable fault analysis is performed based on fault detection data, and a fault point position and a fault type are determined.

Description

Technical Field

[0001] The present application relates to the technical field of cable status monitoring, and in particular to a power cable diagnosis method, equipment, medium and product. Background Art

[0002] With the rapid development of smart grids and the energy internet, power cables, as an indispensable component of power systems, are receiving increasing attention for their operational safety and reliability. Traditional power cable diagnostic methods mostly rely on offline testing, such as using a megohmmeter to measure insulation resistance and conducting continuity tests. While these methods can determine whether a cable is faulty to a certain extent, they cannot quickly and accurately locate the fault point, and the operations are cumbersome and inefficient.

[0003] In recent years, with the rapid development of electronics, signal processing, and artificial intelligence technologies, online power cable testing has become increasingly feasible. This technology, to a certain extent, avoids the downtime required for offline testing. However, current online testing methods typically only utilize one of the following modes: partial discharge detection, temperature measurement, and dielectric loss testing to check the health of power cables. These methods are often limited to specific indicators and fail to fully reflect the true health of the cable, resulting in low accuracy in power cable fault diagnosis.

[0004] Therefore, how to improve the accuracy of power cable diagnosis is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a power cable diagnostic method, device, medium and product to solve at least one of the above technical problems.

[0006] The above-mentioned invention objectives of this application are achieved through the following technical solutions:

[0007] In a first aspect, the present application provides a power cable diagnosis method, which adopts the following technical solution:

[0008] A power cable diagnosis method, comprising:

[0009] Acquiring preliminary diagnostic parameters and diagnostic confirmation parameters collected by the sensor array, wherein the preliminary diagnostic parameters include: cable temperature data and DC component; the diagnostic confirmation parameters include: partial discharge signal, cable current data and cable voltage data;

[0010] Performing a preliminary diagnosis of the cable based on the preliminary diagnostic parameters, and when the preliminary diagnosis result of the cable indicates an abnormality, performing a secondary diagnosis of the cable based on the diagnosis confirmation parameters to determine a confirmed diagnosis result of the cable;

[0011] When the cable is diagnosed as abnormal, performing an insulation resistance test to collect an insulation resistance value, selecting a fault detection operation based on the insulation resistance value, determining a target fault detection operation, and performing a fault detection operation on the cable according to the target fault detection operation;

[0012] Acquire fault detection data, perform cable fault analysis based on the fault detection data, and determine the location of the fault point and the type of fault.

[0013] By adopting the above-mentioned technical solution, the preliminary diagnostic parameters and diagnostic confirmation parameters collected by the sensor array are obtained. The sensor array can cover a wider monitoring range, capture more subtle changes in the cable status, and provide richer data support for cable fault diagnosis. The cable is initially diagnosed based on the preliminary diagnostic parameters. When the preliminary diagnosis result of the cable is abnormal, the cable is secondary diagnosed based on the diagnostic confirmation parameters to determine the cable diagnosis result. By combining the preliminary diagnostic parameters and the diagnostic confirmation parameters, a comprehensive assessment of the cable status can be achieved, thereby improving the accuracy of power cable diagnosis. Furthermore, when the cable diagnosis result is abnormal, an insulation resistance test is performed to collect the insulation resistance value. Based on the insulation resistance value, a fault detection operation is selected, the target fault detection operation is determined, and the fault detection operation is performed on the cable according to the target fault detection operation. Finally, fault detection data is obtained, and cable fault analysis is performed based on the fault detection data to determine the fault point location and fault type.

[0014] In a preferred example, the present application may be further configured as follows: performing secondary cable diagnosis based on the diagnosis confirmation parameters to determine the cable diagnosis result includes:

[0015] Extracting features based on the partial discharge signal to determine partial discharge features, and performing abnormal discharge detection based on the partial discharge features to determine abnormal discharge detection results, wherein the abnormal discharge detection results include: whether abnormal discharge exists and the severity level of the abnormal discharge;

[0016] Performing dielectric loss calculation based on the cable current data and the cable voltage data to determine a dielectric loss factor and a loss tangent;

[0017] Obtaining a standard dielectric loss factor and a standard loss tangent, performing a dielectric loss test diagnosis based on the standard dielectric loss factor, the standard loss tangent, the dielectric loss factor, and the loss tangent, and determining a cable insulation performance diagnosis result, wherein the cable insulation performance diagnosis result includes: good insulation performance and abnormal insulation performance;

[0018] The cable diagnosis result is determined by combining the abnormal discharge detection result and the cable insulation performance diagnosis result.

[0019] In a preferred example, the present application may be further configured as follows: selecting a fault detection operation based on the insulation resistance value and determining a target fault detection operation include:

[0020] obtaining a cable characteristic impedance, performing a resistance comparison based on the insulation resistance value and the cable characteristic impedance, and determining that the target fault detection operation is an impulse high-voltage flashover method or a traveling wave method fault location when the insulation resistance value is greater than the cable characteristic impedance;

[0021] When the insulation resistance value is less than the cable characteristic impedance, the target fault detection operation is determined to be a low-voltage pulse method or a DC high-voltage pulse method.

[0022] In a preferred example, the present application may be further configured as follows: after performing cable fault analysis based on the fault detection data and determining the fault location and fault type, the present application may further include:

[0023] Storing the fault point location and the fault type in a cable fault database, wherein the cable fault data of different areas in the cable fault database are stored in different partitions according to the fault point location;

[0024] When a cable quality assessment instruction carrying a cable laying area is detected, cable fault data is screened in the cable fault database based on the cable laying area in the cable quality assessment instruction to determine target cable fault data;

[0025] A cable health assessment is performed based on the target cable fault data to generate a cable health assessment report.

[0026] In a preferred example, the present application may be further configured as follows: after obtaining the preliminary diagnostic parameters collected by the sensor array, the following steps may be further included:

[0027] Perform cable heating diagnosis based on the cable temperature data in the preliminary diagnostic parameters, and determine a cable heating diagnosis result;

[0028] When the cable heating diagnosis result indicates that the cable is overheated, overheat decomposition gas data collected by the gas sensor is obtained, and an overheat level analysis is performed based on the overheat decomposition gas data to determine the cable overheat level.

[0029] In a preferred example, the present application may be further configured as follows: after performing cable fault analysis based on the fault detection data and determining the fault location and fault type, the present application may further include:

[0030] Acquiring power cable network information, and constructing a model based on the power cable network information to obtain a three-dimensional model of the cable network;

[0031] A fault visualization display is performed based on the fault point location, the fault type and the three-dimensional model of the cable network to obtain a fault source labeled cable model, wherein the fault source labeled cable model is used to intuitively display the distribution location of the fault source in the power cable network.

[0032] In a second aspect, the present application provides an electronic device, which adopts the following technical solution:

[0033] at least one processor;

[0034] Memory;

[0035] At least one application, wherein the at least one application is stored in the memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the power cable diagnosis method.

[0036] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0037] A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute the power cable diagnostic method described above.

[0038] In a fourth aspect, the present application provides a computer program product that adopts the following technical solution:

[0039] A computer program product includes a computer program, wherein the computer program implements the power cable diagnosis method when executed by a processor.

[0040] In summary, this application includes at least one of the following beneficial technical effects:

[0041] By acquiring the preliminary diagnostic parameters and diagnostic confirmation parameters collected by the sensor array, the sensor array can cover a wider monitoring range, capture more subtle changes in cable status, and provide richer data support for cable fault diagnosis. A preliminary cable diagnosis is performed based on the preliminary diagnostic parameters. When the preliminary diagnosis result of the cable indicates an abnormality, a secondary cable diagnosis is performed based on the diagnostic confirmation parameters to determine the cable diagnosis result. By combining the preliminary diagnostic parameters and diagnostic confirmation parameters, a comprehensive assessment of the cable status can be achieved, improving the accuracy of power cable diagnosis. Furthermore, when the cable diagnosis result indicates an abnormality, an insulation resistance test is performed to collect the insulation resistance value. Based on the insulation resistance value, a fault detection operation is selected, the target fault detection operation is determined, and the fault detection operation is performed on the cable according to the target fault detection operation. Finally, fault detection data is obtained, and cable fault analysis is performed based on the fault detection data to determine the fault point location and fault type.

[0042] Because different fault detection methods are suitable for different fault types and resistance ranges, selecting the appropriate fault detection method can significantly improve the accuracy and efficiency of fault detection and reduce the possibility of misjudgments and missed detections. Therefore, the cable characteristic impedance is obtained, and the insulation resistance value is compared with the cable characteristic impedance. When the insulation resistance value is greater than the cable characteristic impedance, the target fault detection operation is determined to be the impulse high-voltage flashover method or the traveling wave method. When the insulation resistance value is less than the cable characteristic impedance, the target fault detection operation is determined to be the low-voltage pulse method or the DC high-voltage pulse method. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of a power cable diagnostic method according to one embodiment of the present application;

[0044] Figure 2 This is a schematic structural diagram of a power cable diagnostic system according to one embodiment of the present application;

[0045] Figure 3 This is a structural diagram of an electronic device according to one embodiment of the present application. DETAILED DESCRIPTION

[0046] The following combination Figures 1 to 3 This application is described in further detail.

[0047] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the present application, they are protected by patent law.

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should be noted that in the optional embodiments of the present application, when the embodiments in the present application are applied to specific products or technologies, the object information and other related data involved need to obtain the object's permission or consent, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0049] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0050] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0051] The embodiment of the present application provides a power cable diagnostic method, which is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited to this. The terminal device and the server can be directly or indirectly connected through wired or wireless communication. The embodiment of the present application does not limit this. Figure 1 As shown, the method includes step S101, step S102, step S103 and step S104, wherein:

[0052] Step S101: obtaining preliminary diagnostic parameters and diagnostic confirmation parameters collected by the sensor array, wherein the preliminary diagnostic parameters include: cable temperature data and DC component; the diagnostic confirmation parameters include: partial discharge signal, cable voltage data and cable current data.

[0053] For the embodiments of the present application, in order to improve the accuracy of power cable diagnosis, a high-precision, high-sensitivity sensor array is used to monitor cable parameters such as temperature, current, voltage, DC component, discharge signal, cable current data and cable voltage data in real time. Compared with traditional sensors, the sensor array can cover a wider monitoring range, capture more subtle changes in cable status, and provide richer data support for cable fault diagnosis.

[0054] Specifically, preliminary diagnostic parameters and diagnostic confirmation parameters collected by the sensor array are obtained. The preliminary diagnostic parameters include cable temperature data and DC component; the diagnostic confirmation parameters include partial discharge signals, cable current data, and cable voltage data. Regarding the cable temperature data in the preliminary diagnostic parameters, temperature sensors measure the cable surface temperature or the temperature distribution within the cable in real time. This cable temperature data can reflect the cable's operating status. Excessively high or low temperatures may indicate cable problems. If unbalanced current or leakage current occurs in the cable, the current sensor can detect the cable's DC component, the magnitude of which can reflect the cable's insulation condition. Furthermore, partial discharge is a key precursor to cable insulation failure. Therefore, a high-frequency partial discharge sensor is used to detect the presence of partial discharge signals in the cable. By detecting parameters such as the discharge signal's amplitude, phase, and frequency, the cable's insulation condition and health can be determined. Cable current and voltage data are obtained from dielectric loss testing of the cable, facilitating the subsequent calculation of the cable's dielectric loss factor and loss tangent. These dielectric loss factor and loss tangent reflect the aging of the cable insulation and are important references for evaluating cable insulation performance.

[0055] Step S102: Perform preliminary diagnosis of the cable based on preliminary diagnosis parameters. When the preliminary diagnosis result of the cable shows abnormality, perform secondary diagnosis of the cable based on diagnosis confirmation parameters to determine the confirmed result of the cable.

[0056] For the embodiments of the present application, the cable is a key component of power transmission, and its quality and safety are of vital importance. The cable temperature data and DC component are used as preliminary diagnostic parameters to provide preliminary information for diagnosing the operating status of the cable, so that it is possible to quickly and easily preliminarily determine whether the cable is overloaded, has poor heat dissipation, or has insulation damage or aging. Furthermore, when the preliminary diagnostic results show that the cable is abnormal, the diagnostic confirmation parameters are used to perform a more detailed and in-depth fault diagnosis of the cable in order to reveal more complex faults or defects that may exist inside the cable. By combining the preliminary diagnostic parameters and the diagnostic confirmation parameters, a comprehensive assessment of the cable status can be achieved, thereby improving the accuracy of power cable diagnosis.

[0057] Specifically, a temperature alarm threshold is obtained, and a temperature comparison is performed based on the cable temperature data in the preliminary diagnostic parameters and the temperature alarm threshold. If the cable temperature data is higher than the temperature alarm threshold, it indicates that the cable is overheated, and the preliminary diagnosis result is determined to be abnormal. The temperature alarm threshold is a reasonable alarm threshold set by comprehensively considering the material, specifications, operating environment and safety standards of the cable. The specific value of the temperature alarm threshold is no longer limited in this embodiment of the application. At the same time, a DC component alarm threshold is obtained, and a DC component comparison is performed based on the DC component in the preliminary diagnostic parameters and the DC component alarm threshold. If the DC component is higher than the DC component alarm threshold, it indicates that the cable insulation layer is aged, damaged, damp, or has insulation defects such as water trees and electrical trees, and the preliminary diagnosis result is determined to be abnormal. Only when the cable temperature data is not higher than the temperature alarm threshold and the DC component is not higher than the DC component alarm threshold, the preliminary diagnosis result is determined to be normal for the cable.

[0058] Furthermore, when the initial diagnosis result of the cable is abnormal, a secondary diagnosis of the cable is performed based on the diagnosis confirmation parameters to determine the confirmed result of the cable, wherein the secondary diagnosis of the cable is used to perform a more detailed and in-depth fault diagnosis on the cable. There are many specific implementation methods for the secondary diagnosis of the cable, which are no longer limited in the embodiments of the present application. In one achievable method, feature extraction is performed based on the partial discharge signal to determine the partial discharge feature, and abnormal discharge detection is performed based on the partial discharge feature to determine the abnormal discharge detection result, wherein the abnormal discharge detection result includes: whether there is abnormal discharge and the severity level of abnormal discharge; dielectric loss calculation is performed based on the cable current data and the cable voltage data to determine the dielectric loss factor and the loss tangent; standard dielectric loss factor and standard loss tangent are obtained, and dielectric loss test diagnosis is performed based on the standard dielectric loss factor, standard loss tangent, dielectric loss factor and loss tangent to determine the cable insulation performance diagnosis result, wherein the cable insulation performance diagnosis result includes: good insulation performance and abnormal insulation performance; the abnormal discharge detection result and the cable insulation performance diagnosis result are combined to determine the cable diagnosis result.

[0059] Step S103: When the cable is diagnosed as abnormal, perform an insulation resistance test to collect the insulation resistance value, select a fault detection operation based on the insulation resistance value, determine a target fault detection operation, and perform a fault detection operation on the cable according to the target fault detection operation.

[0060] For the embodiments of the present application, when the cable diagnosis result is that there is an abnormality, it indicates that the cable is currently in an abnormal state. In order to more accurately know the fault type and fault location of the cable, it is necessary to perform a fault detection operation on the cable. However, different fault types and locations may require different detection methods, and the insulation resistance value can provide an important basis for selecting a suitable detection method, so as to select the most appropriate fault detection operation.

[0061] Specifically, when the cable is diagnosed as abnormal, an insulation resistance test is performed and the insulation resistance value in the test is collected. The insulation resistance test can select a traditional test method or an online monitoring method. In this regard, the embodiment of the present application is no longer limited, as long as the insulation resistance value corresponding to the cable can be collected. In a traditional insulation resistance test, it is usually necessary to power off the cable under test and use an insulation resistance tester (such as a megohmmeter) to measure the insulation resistance of the cable, that is, apply a certain DC voltage to the cable under test, and measure the leakage current through the insulation layer to calculate the insulation resistance value. With the development of modern electronic technology and automation technology, some special insulation resistance test instruments have been able to realize online monitoring of insulation resistance values, that is, insulation resistance test instruments use differential measurement technology, high impedance input circuits and advanced signal processing algorithms to eliminate or reduce the influence of the working voltage on the cable on the measurement results.

[0062] Furthermore, a fault detection operation is selected based on the insulation resistance value, a target fault detection operation is determined, and a fault detection operation is performed on the cable according to the target fault detection operation. There are many specific implementation methods for selecting the fault detection operation, which are not limited in the embodiments of the present application. In one achievable method, the cable characteristic impedance is obtained, and a resistance value comparison is performed based on the insulation resistance value and the cable characteristic impedance. When the insulation resistance value is greater than the cable characteristic impedance, the target fault detection operation is determined to be the impact high-voltage flashover method or the traveling wave method fault location; when the insulation resistance value is less than the cable characteristic impedance, the target fault detection operation is determined to be the low-voltage pulse method or the DC high-voltage pulse method, wherein the cable characteristic impedance is an electrical parameter possessed by the cable. This parameter is related to factors such as the structure, material, size, and operating frequency of the cable, and is a reference standard for judging the type of cable fault. It is generally believed that a fault resistance below several thousand ohms is a low-resistance fault, and vice versa.

[0063] Step S104: Acquire fault detection data, perform cable fault analysis based on the fault detection data, and determine the fault location and fault type.

[0064] In the embodiments of the present application, fault detection data that can be collected during the target fault detection operation is obtained. The fault detection data includes, but is not limited to, reflected signals and waveform characteristics. Then, based on the fault detection data, cable fault analysis is performed to determine the fault point location. That is, the fault point location is calculated based on the propagation time of the reflected signal and the propagation speed of the signal in the cable. Simultaneously, the specific fault type, such as open circuit, short circuit, or ground fault, is determined by combining the waveform characteristics and location information. Regarding the method for determining the fault type, a morphological analysis is performed based on the waveform characteristics to observe whether the waveform is regular and whether there are abnormal fluctuations or mutations. For example, an open circuit fault may cause a significant break or missing portion of the waveform; a short circuit fault may manifest as a sudden increase in waveform amplitude or an irregular waveform. At the same time, key waveform parameters such as amplitude, phase, and frequency need to be measured. Changes in these parameters can reflect the nature and extent of the cable fault. For example, a ground fault may cause the waveform amplitude to decrease, while a short circuit fault may cause the waveform frequency to change. Of course, when determining the fault type, the location of the fault point can also be comprehensively considered. For example, if the fault point is at the joint of the cable, then the fault type of open circuit or poor contact is more likely; if the fault point is at the bending part of the cable, then the fault type of insulation damage or breakage caused by mechanical stress is more likely.

[0065] It can be seen that in the embodiment of the present application, the preliminary diagnostic parameters and diagnostic confirmation parameters collected by the sensor array are obtained, and the sensor array can cover a wider monitoring range, capture more subtle changes in the cable status, and provide richer data support for cable fault diagnosis. The cable is preliminarily diagnosed based on the preliminary diagnostic parameters. When the preliminary diagnostic result of the cable is that there is an abnormality, the cable is secondary diagnosed based on the diagnostic confirmation parameters to determine the confirmed result of the cable. By combining the preliminary diagnostic parameters and the diagnostic confirmation parameters, a comprehensive assessment of the cable status can be achieved, thereby improving the accuracy of power cable diagnosis. Furthermore, when the confirmed result of the cable is that there is an abnormality, an insulation resistance test is performed to collect the insulation resistance value, and a fault detection operation is selected based on the insulation resistance value, the target fault detection operation is determined, and the fault detection operation is performed on the cable according to the target fault detection operation. Finally, the fault detection data is obtained, and the cable fault analysis is performed based on the fault detection data to determine the location of the fault point and the type of fault.

[0066] Furthermore, in order to achieve a comprehensive assessment of the cable status and improve the accuracy of power cable diagnosis, in an embodiment of the present application, a secondary cable diagnosis is performed based on the diagnostic confirmation parameters to determine the cable diagnosis result, including:

[0067] Extracting features based on the partial discharge signal to determine partial discharge features, and performing abnormal discharge detection based on the partial discharge features to determine abnormal discharge detection results, wherein the abnormal discharge detection results include: whether abnormal discharge exists and the severity level of the abnormal discharge;

[0068] In the embodiments of the present application, filtering and denoising are performed based on the partial discharge signal to remove background noise from the partial discharge signal. Feature parameters reflecting the characteristics of the partial discharge, such as discharge amplitude, number of discharges, and discharge phase, are extracted from the filtered and denoised partial discharge signal. These feature parameters are recorded as partial discharge features. Abnormal discharge detection is then performed based on the partial discharge features to determine the abnormal discharge detection result. Specifically, a discharge amplitude threshold is pre-stored in the electronic device. When the discharge amplitude in the partial discharge feature exceeds the discharge amplitude threshold, abnormal discharge is determined to exist in the cable; otherwise, abnormal discharge is determined to not exist. The discharge amplitude threshold is a threshold that can distinguish between normal and abnormal discharges. Furthermore, a severity analysis is performed based on the number of discharges and discharge phase in the partial discharge feature to determine the severity level of the abnormal discharge. The electronic device pre-stores the correspondence between the number of discharges, discharge phase, and abnormal discharge severity level, so that the abnormal discharge severity level corresponding to the partial discharge can be quickly and accurately determined.

[0069] Calculate dielectric loss based on cable current data and cable voltage data to determine dielectric loss factor and loss tangent;

[0070] Obtaining a standard dielectric loss factor and a standard loss tangent, performing dielectric loss test diagnosis based on the standard dielectric loss factor, the standard loss tangent, the dielectric loss factor, and the loss tangent, and determining a cable insulation performance diagnosis result, wherein the cable insulation performance diagnosis result includes: good insulation performance and abnormal insulation performance;

[0071] The cable diagnosis result is determined by combining the abnormal discharge detection results and the cable insulation performance diagnosis results.

[0072] For the embodiment of the present application, dielectric loss calculation is performed based on cable current data and cable voltage data to determine the dielectric loss factor and loss tangent. The dielectric loss factor and loss tangent are used to reflect the aging of cable insulation and are an important reference for evaluating cable insulation performance. The specific implementation process of node loss calculation is as follows: the effective value is calculated based on the collected cable current data and cable voltage data. The effective value is an important parameter of the AC signal. It represents the DC current value when the average power generated by the signal in one cycle is equal to the power generated by the DC signal on the same resistance; then, the phase difference between the cable current data and the cable voltage data is calculated. The phase difference is a measure of the time difference between the AC signals, which reflects the phase relationship between the signals; then, according to the calculation formula of the dielectric loss factor, the effective values ​​of the voltage and current and the phase difference are substituted into the formula for calculation, where the calculation formula of the dielectric loss factor is: D = (Ic / I) * cos(φ), where D is the dielectric loss factor, Ic is the effective value of the leakage current (which can be measured by the current sensor and obtained by subtracting the leakage current), and φ is the phase difference between the voltage and current; of course, the loss tangent can also be calculated by the following formula: D = tan(δ) * (ωC / I), where ω is the angular frequency (equal to 2π times the frequency f), and C is the capacitance of the cable (which can be measured or calculated based on the cable specifications).

[0073] After this, obtain the standard dielectric loss factor and standard loss tangent. The standard dielectric loss factor and standard loss tangent are determined based on the cable type, voltage level, operating environment, and standards published by the International Standards Organization. They are used to evaluate the insulation performance of the cable. Of course, the user can also make small adjustments to the standard dielectric loss factor and standard loss tangent according to actual conditions. This is no longer limited in the embodiments of the present application. Then, a dielectric loss test diagnosis is performed based on the standard dielectric loss factor, standard loss tangent, dielectric loss factor, and loss tangent to determine the cable insulation performance diagnosis result. That is, if the measured dielectric loss factor and loss tangent values ​​are both within the standard value range, the cable insulation performance diagnosis result is determined to be good insulation performance; otherwise, the cable insulation performance diagnosis result is determined to be abnormal insulation performance. Finally, the abnormal discharge detection result and the cable insulation performance diagnosis result are combined to determine the cable diagnosis result. That is, if any one of abnormal discharge and insulation performance abnormality is included, the cable diagnosis result is determined to be abnormal; otherwise, the cable diagnosis result is determined to be normal. The diagnostic confirmation parameters are used to conduct more detailed and in-depth fault diagnosis of the cable, so as to reveal more complex faults or defects that may exist inside the cable, realize a comprehensive assessment of the cable status, and improve the accuracy of power cable diagnosis.

[0074] It can be seen that in the embodiment of the present application, feature extraction is performed based on the local discharge signal to determine the local discharge feature, and abnormal discharge detection is performed based on the local discharge feature to determine the abnormal discharge detection result. Then, dielectric loss calculation is performed based on the cable current data and the cable voltage data to determine the dielectric loss factor and the loss tangent, and dielectric loss test diagnosis is performed based on the standard dielectric loss factor, the standard loss tangent, the dielectric loss factor and the loss tangent to determine the cable insulation performance diagnosis result. Finally, the abnormal discharge detection result and the cable insulation performance diagnosis result are combined to determine the cable diagnosis result. The diagnostic confirmation parameters are used to perform more detailed and in-depth fault diagnosis on the cable, so as to reveal more complex faults or defects that may exist inside the cable, achieve a comprehensive assessment of the cable status, and improve the accuracy of power cable diagnosis.

[0075] Furthermore, in order to significantly improve the accuracy and efficiency of fault detection and reduce the possibility of misjudgment and missed judgment, in an embodiment of the present application, a fault detection operation is selected based on the insulation resistance value, and a target fault detection operation is determined, including:

[0076] Obtain the cable characteristic impedance, compare the insulation resistance value with the cable characteristic impedance, and determine the target fault detection operation as the impulse high-voltage flashover method or the traveling wave method fault location when the insulation resistance value is greater than the cable characteristic impedance;

[0077] When the insulation resistance value is less than the cable characteristic impedance, the target fault detection operation is determined to be the low-voltage pulse method or the DC high-voltage pulse method.

[0078] In the embodiments of the present application, cables are an important component of the power system, and their failures may cause power outages or equipment damage. Timely and accurate fault detection can promptly detect and repair cable faults, ensuring the safe and stable operation of the power system. However, different fault detection methods are suitable for different fault types and resistance ranges. Therefore, selecting an appropriate fault detection method can significantly improve the accuracy and efficiency of fault detection and reduce the possibility of misjudgment and missed judgments.

[0079] Specifically, the cable characteristic impedance is obtained. The cable characteristic impedance is an electrical parameter of the cable that is related to factors such as the cable's structure, material, dimensions, and operating frequency. It serves as a reference standard for determining the type of cable fault. Specifically, when the impedance is greater than the cable characteristic impedance, it indicates a high-resistance fault; when it is less than the cable characteristic impedance, it indicates a low-resistance fault. Then, a resistance comparison is performed based on the insulation resistance and the cable characteristic impedance. When the insulation resistance is greater than the cable characteristic impedance, the target fault detection operation is determined to be either the impulse high-voltage flashover method or the traveling wave method. Preferably, when the cable is located in good field conditions, the more accurate and efficient traveling wave method is selected for fault location; when the cable is located in poor field conditions, the more adaptable impulse high-voltage flashover method is selected. High-resistance faults typically manifest as a high equivalent impedance at the fault point, close to or equal to the cable characteristic impedance, resulting in a small reflection coefficient, making it difficult to accurately measure using conventional testing methods. However, the impulse high-voltage flashover method is suitable for high-resistance faults in cables and has a high recognition rate and accuracy. Furthermore, the traveling wave method is unaffected by line parameters, system operating mode, or fault type, and offers higher reliability and accuracy for fault location.

[0080] When the insulation resistance value is less than the characteristic impedance of the cable, the target fault detection operation is determined to be the low-voltage pulse method or the DC high-voltage pulse method. Low-resistance faults are usually manifested as a significant decrease in the cable insulation resistance value, which may even be close to a short-circuit state. This fault will cause a significant current shunting near the fault point, thereby affecting the normal transmission performance of the cable. However, the low-voltage pulse method has the advantages of simple operation, fast testing speed, and no damage to the cable. It is suitable for detecting low-resistance faults because these faults cause significant signal reflections. At the same time, in some cases, electromagnetic interference around the cable may affect the accuracy of the low-voltage pulse method. In order to overcome the electromagnetic interference around the cable and improve the accuracy of fault detection, the DC high-voltage pulse method is selected. Among them, the DC high-voltage pulse method is based on the low-voltage pulse method. By applying a higher voltage, the propagation capability and penetration of the pulse signal are enhanced to improve the accuracy of cable fault detection.

[0081] It can be seen that in the embodiments of the present application, since different fault detection methods are applicable to different fault types and resistance ranges, selecting an appropriate fault detection method can significantly improve the accuracy and efficiency of fault detection and reduce the possibility of misjudgment and missed judgment. Therefore, the cable characteristic impedance is obtained, and the resistance value is compared based on the insulation resistance value and the cable characteristic impedance. When the insulation resistance value is greater than the cable characteristic impedance, the target fault detection operation is determined to be the impact high-voltage flashover method or the traveling wave method fault location. When the insulation resistance value is less than the cable characteristic impedance, the target fault detection operation is determined to be the low-voltage pulse method or the DC high-voltage pulse method.

[0082] Furthermore, in order to improve the reliability of the power cable, in an embodiment of the present application, after performing cable fault analysis based on the fault detection data and determining the fault location and fault type, the following steps are further included:

[0083] The fault location and fault type are stored in a cable fault database, wherein the cable fault data of different areas are stored in different partitions according to the location of the fault point in the cable fault database;

[0084] When a cable quality assessment instruction carrying a cable laying area is detected, cable fault data is screened in a cable fault database based on the cable laying area in the cable quality assessment instruction to determine target cable fault data;

[0085] Perform cable health assessment based on target cable fault data and generate a cable health assessment report.

[0086] For the embodiment of the present application, in order to establish a complete and systematic record of cable fault history, the fault point location and fault type are stored in the cable fault database to provide data support for subsequent fault analysis, prevention and maintenance. At the same time, a cable health assessment is performed based on the cable fault data of the screened specific area, which helps to understand the health status of the cable in a timely manner, and then perform preventive maintenance to reduce the probability of failure, thereby improving the reliability of the power cable.

[0087] Specifically, the fault location and fault type are stored in a cable fault database. A pre-defined database partitioning strategy is implemented within the cable fault database to partition and store cable data by geographic location or administrative region, facilitating subsequent comprehensive assessments of cable operating conditions in different regions. When a cable quality assessment instruction containing a cable laying area is detected, cable fault data is screened within the cable fault database based on the cable laying area in the instruction to determine target cable fault data. Specifically, the cable laying area in the instruction is used as a query condition for fault data screening, thereby filtering out multiple pieces of cable fault data within the cable laying area from the cable fault database.

[0088] Furthermore, a cable health assessment is performed based on the target cable fault data corresponding to the cable laying area, and a cable health assessment report is generated. The target cable fault data can be one, two, or more fault data. The number of cable fault data is not limited in this embodiment of the application. There are multiple specific evaluation criteria for cable health assessment, which can be set by the user according to actual conditions. In one achievable method, the total cable length corresponding to the cable laying area is obtained, and the cable health index is calculated based on the total cable length, the number of faults in the target cable fault data, and the fault type to determine the health index. The health index includes but is not limited to: failure rate, fault type distribution, and fault severity index. The failure rate represents the frequency of failures per unit length of cable, and is calculated as follows: failure rate = number of faults / total cable length; the fault type distribution shows the proportion of different types of faults in the cable faults, which is used to count the number of each fault type and calculate its proportion of the total number of faults; each fault is assigned a severity level based on the fault type and its impact on cable performance and safety, and then a weighted average is calculated, which is recorded as the fault severity index. Finally, the failure rate, fault type distribution, and fault severity index are combined to generate a cable health assessment report.

[0089] As can be seen, in this embodiment of the present application, the fault location and fault type are stored in the cable fault database. When a cable quality assessment instruction containing a cable laying area is detected, the cable fault data in the cable fault database is screened based on the cable laying area in the cable quality assessment instruction to determine the target cable fault data. Furthermore, a cable health assessment is performed based on the target cable fault data, and a cable health assessment report is generated. Performing a cable health assessment helps to timely understand the health status of the cable, thereby performing preventive maintenance to reduce the probability of failure and improve the reliability of the power cable.

[0090] Furthermore, in order to achieve accurate diagnosis of cable overheating faults and avoid false positives and missed positives, in the embodiment of the present application, after obtaining the preliminary diagnostic parameters collected by the sensor array, the following steps are further included:

[0091] Perform cable heating diagnosis based on the cable temperature data in the preliminary diagnosis parameters and determine the cable heating diagnosis result;

[0092] When the cable heating diagnosis result indicates that the cable is overheated, the overheat decomposition gas data collected by the gas sensor is obtained, and the overheat level analysis is performed based on the overheat decomposition gas data to determine the cable overheat level.

[0093] In the embodiments of the present application, cable overheating may cause insulation thermal breakdown, resulting in phase short circuit tripping and even fire, posing a serious threat to the safe operation of the power system. However, traditional cable temperature detection methods, such as thermocouples, distributed optical fibers, and infrared thermal imaging, have problems such as small temperature measurement range, high cost, and blind spots, making it difficult to meet the overheating fault diagnosis needs of massive medium and low voltage cables. Therefore, in the power cable diagnosis method, a superimposed gas sensor is used to collect and analyze overheated decomposition gases, achieving accurate diagnosis of cable overheating faults without direct contact with the cable, avoiding false alarms and missed alarms.

[0094] Specifically, a cable temperature threshold pre-stored in the electronic device is obtained. Cable heating diagnosis is performed based on the cable temperature threshold and cable temperature data from the preliminary diagnostic parameters. If the cable temperature data exceeds the cable temperature threshold, the cable overheating diagnosis is determined; otherwise, the cable temperature is determined to be normal. Furthermore, a gas sensor is pre-deployed in the power cable system. This gas sensor can monitor the decomposition gases generated by the cable in real time and transmit them wirelessly to the electronic device, allowing the electronic device to promptly receive the cable overheating decomposition gas data. This overheating decomposition gas data includes, but is not limited to, gas type and concentration. An overheating level analysis is then performed based on the overheating decomposition gas data to determine the cable overheating level. Cable overheating levels are categorized as normal, warning, and alarm. The specific implementation of the overheating level analysis is as follows: When the cable is operating normally and the temperature does not exceed 90°C, the amount of gas generated by the decomposition of the insulation material is relatively small, primarily consisting of low-boiling-point volatile organic compounds (VOCs). Therefore, when the gas concentration in the overheating decomposition gas data is low and the gas types are relatively uniform, the cable overheating level is determined to be normal. When the cable temperature rises between 120°C and 160°C, the insulation material begins to decompose at an accelerated rate, increasing the types and concentrations of gases produced. In addition to low-boiling-point VOCs, higher-boiling-point compounds such as aldehydes and ketones may also be produced. Therefore, when the gas concentration in the overheated decomposition gas data reaches the corresponding warning level and the gases include these compounds, the cable overheat level is determined to be a warning. When the cable temperature exceeds 180°C, the decomposition rate of the insulation material accelerates dramatically, producing large amounts of pyrolysis gases. These gases may include toxic and hazardous substances such as hydrogen (H2), methane (CH4), and ethylene (C2H4). Furthermore, highly reactive free radicals and unstable compounds may be produced, posing a serious threat to the cable's insulation performance and safety. Therefore, when the overheated decomposition gas data includes these toxic and hazardous substances, the cable overheat level is determined to be an alarm.

[0095] As can be seen, in the embodiments of the present application, cable heating diagnosis is performed based on the cable temperature data in the preliminary diagnostic parameters to determine the cable heating diagnosis result. When the cable heating diagnosis result indicates cable overheating, overheated decomposition gas data collected by the gas sensor is obtained, and overheat level analysis is performed based on the overheated decomposition gas data to determine the cable overheat level. In the power cable diagnosis method, the superimposed gas sensor collects and analyzes overheated decomposition gas, achieving accurate diagnosis of cable overheat faults without direct contact with the cable, avoiding false positives and missed positives.

[0096] Furthermore, in order to improve the accuracy and efficiency of fault location, in an embodiment of the present application, after performing cable fault analysis based on the fault detection data and determining the fault point location and fault type, the following steps are further included:

[0097] Acquire power cable network information, construct a model based on the power cable network information, and obtain a three-dimensional model of the cable network;

[0098] Fault visualization is performed based on the fault point location, fault type, and three-dimensional model of the cable network to obtain a fault source labeled cable model. The fault source labeled cable model is used to intuitively display the distribution location of the fault source in the power cable network.

[0099] For the embodiments of the present application, by constructing a three-dimensional model of the power cable network and performing a visual display in combination with the fault point location and fault type, it helps the fault personnel to quickly locate the fault point through the intuitive three-dimensional model, avoiding the tedious process of manual one-by-one checking in the traditional method, and greatly improving the accuracy and efficiency of fault location.

[0100] Specifically, the power cable network information is obtained from official platforms such as the power grid platform, which records the basic parameters of the cables, laying methods, and connection relationships. Then, the modeling tool is used to build a model based on the power cable network information to obtain a three-dimensional model of the cable network. That is, the three-dimensional path of the cable line is drawn according to the power line in the power cable network information; according to the parameter information of the cable, such as diameter, material, etc., the corresponding three-dimensional model elements are added to the cable line; the relative position relationship between the cable and other facilities, such as cable trays, cable wells, substations, etc., is considered to ensure that the cable network in the model is consistent with the actual situation; of course, the material and light and shadow effects in the modeling tool are used to enhance the realism and visualization of the model; finally, an accurate and reliable three-dimensional model of the cable network is obtained to provide strong support for cable management, maintenance, planning and other tasks.

[0101] Furthermore, a fault visualization is performed based on the fault location, fault type, and 3D cable network model, resulting in a fault-source labeled cable model. This model is used to visually display the distribution of fault sources within the power cable network. This model allows maintenance personnel to quickly locate the source of a fault, avoiding the tedious manual troubleshooting required by traditional methods and significantly improving the accuracy and efficiency of fault location.

[0102] As can be seen, in the embodiment of the present application, power cable network information is obtained, and a model is constructed based on the power cable network information to obtain a three-dimensional model of the cable network. Then, a fault visualization is performed based on the fault point location, fault type, and the three-dimensional model of the cable network to obtain a cable model with the fault source labeled. By constructing a three-dimensional model of the power cable network and performing a visualization based on the fault point location and fault type, it is helpful for troubleshooters to quickly locate the fault point through an intuitive three-dimensional model, avoiding the tedious process of manual one-by-one investigation required in traditional methods, and greatly improving the accuracy and efficiency of fault location.

[0103] The above embodiment introduces a power cable diagnosis method from the perspective of method flow, and the following embodiment introduces a power cable diagnosis system from the perspective of a virtual module or a virtual unit. Please refer to the following embodiment for details.

[0104] The present application embodiment provides a power cable diagnostic system, such as Figure 2 As shown, the power cable diagnostic system may specifically include:

[0105] The parameter acquisition module 210 is used to acquire preliminary diagnostic parameters and diagnostic confirmation parameters collected by the sensor array, wherein the preliminary diagnostic parameters include: cable temperature data and DC component; the diagnostic confirmation parameters include: partial discharge signal, cable current data and cable voltage data;

[0106] The cable diagnosis module 220 is configured to perform a preliminary diagnosis of the cable based on the preliminary diagnosis parameters. If the preliminary diagnosis result of the cable indicates an abnormality, a secondary diagnosis of the cable is performed based on the diagnosis confirmation parameters to determine a confirmed result of the cable.

[0107] A detection operation selection module 230 is configured to, when the cable is diagnosed as abnormal, perform an insulation resistance test to collect an insulation resistance value, select a fault detection operation based on the insulation resistance value, determine a target fault detection operation, and perform the fault detection operation on the cable according to the target fault detection operation;

[0108] The fault analysis module 240 is used to obtain fault detection data, perform cable fault analysis based on the fault detection data, and determine the fault location and fault type.

[0109] For the embodiment of the present application, the preliminary diagnostic parameters and diagnostic confirmation parameters collected by the sensor array are obtained. The sensor array can cover a wider monitoring range, capture more subtle changes in the cable status, and provide richer data support for cable fault diagnosis. A preliminary diagnosis of the cable is performed based on the preliminary diagnostic parameters. When the preliminary diagnostic result of the cable is that there is an abnormality, a secondary diagnosis of the cable is performed based on the diagnostic confirmation parameters to determine the confirmed result of the cable. By combining the preliminary diagnostic parameters and the diagnostic confirmation parameters, a comprehensive assessment of the cable status can be achieved, thereby improving the accuracy of power cable diagnosis. Furthermore, when the confirmed result of the cable is that there is an abnormality, an insulation resistance test is performed to collect the insulation resistance value, and a fault detection operation is selected based on the insulation resistance value, the target fault detection operation is determined, and the fault detection operation is performed on the cable according to the target fault detection operation. Finally, fault detection data is obtained, and cable fault analysis is performed based on the fault detection data to determine the location of the fault point and the type of fault.

[0110] In one possible implementation of the embodiment of the present application, the cable diagnosis module 220, when performing secondary cable diagnosis based on the diagnosis confirmation parameters and determining the cable diagnosis result, is configured to:

[0111] Extracting features based on the partial discharge signal to determine partial discharge features, and performing abnormal discharge detection based on the partial discharge features to determine abnormal discharge detection results, wherein the abnormal discharge detection results include: whether abnormal discharge exists and the severity level of the abnormal discharge;

[0112] Calculate dielectric loss based on cable current data and cable voltage data to determine dielectric loss factor and loss tangent;

[0113] Obtaining a standard dielectric loss factor and a standard loss tangent, performing dielectric loss test diagnosis based on the standard dielectric loss factor, the standard loss tangent, the dielectric loss factor, and the loss tangent, and determining a cable insulation performance diagnosis result, wherein the cable insulation performance diagnosis result includes: good insulation performance and abnormal insulation performance;

[0114] The cable diagnosis result is determined by combining the abnormal discharge detection results and the cable insulation performance diagnosis results.

[0115] In one possible implementation of the embodiment of the present application, when the detection operation selection module 230 performs fault detection operation selection based on the insulation resistance value and determines the target fault detection operation, it is configured to:

[0116] Obtain the cable characteristic impedance, compare the insulation resistance value with the cable characteristic impedance, and determine the target fault detection operation as the impulse high-voltage flashover method or the traveling wave method fault location when the insulation resistance value is greater than the cable characteristic impedance;

[0117] When the insulation resistance value is less than the cable characteristic impedance, the target fault detection operation is determined to be the low-voltage pulse method or the DC high-voltage pulse method.

[0118] In a possible implementation of the embodiment of the present application, the power cable diagnostic system further includes:

[0119] A health assessment module is used to store the fault point location and fault type in the cable fault database, wherein the cable fault data of different areas in the cable fault database are stored in different partitions according to the fault point location;

[0120] When a cable quality assessment instruction carrying a cable laying area is detected, cable fault data is screened in a cable fault database based on the cable laying area in the cable quality assessment instruction to determine target cable fault data;

[0121] Perform cable health assessment based on target cable fault data and generate a cable health assessment report.

[0122] In a possible implementation of the embodiment of the present application, the power cable diagnostic system further includes:

[0123] An overheating level analysis module is used to perform cable heating diagnosis based on the cable temperature data in the preliminary diagnosis parameters and determine the cable heating diagnosis result;

[0124] When the cable heating diagnosis result indicates that the cable is overheated, the overheat decomposition gas data collected by the gas sensor is obtained, and the overheat level analysis is performed based on the overheat decomposition gas data to determine the cable overheat level.

[0125] In a possible implementation of the embodiment of the present application, the power cable diagnostic system further includes:

[0126] The fault display module is used to obtain power cable network information, build a model based on the power cable network information, and obtain a three-dimensional model of the cable network;

[0127] Fault visualization is performed based on the fault point location, fault type, and three-dimensional model of the cable network to obtain a fault source labeled cable model. The fault source labeled cable model is used to intuitively display the distribution location of the fault source in the power cable network.

[0128] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the power cable diagnostic system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0129] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3The electronic device 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0130] Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0131] Bus 302 may include a path for transmitting information between the above components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or one type of bus.

[0132] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0133] The memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0134] Electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. They may also include servers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0135] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0136] The embodiment of the present application provides a computer program product, which includes a computer program, which, when executed by a processor, implements the method in any of the above embodiments. Compared with the related art, the embodiment of the present application obtains preliminary diagnostic parameters and diagnostic confirmation parameters collected by the sensor array. The sensor array can cover a wider monitoring range, capture more subtle changes in the cable status, and provide richer data support for cable fault diagnosis. Based on the preliminary diagnostic parameters, a preliminary diagnosis of the cable is performed. When the preliminary diagnosis result of the cable is abnormal, a secondary diagnosis of the cable is performed based on the diagnostic confirmation parameters to determine the cable diagnosis result. By combining the preliminary diagnostic parameters and the diagnostic confirmation parameters, a comprehensive assessment of the cable status can be achieved, thereby improving the accuracy of power cable diagnosis. Furthermore, when the cable diagnosis result is abnormal, an insulation resistance test is performed to collect the insulation resistance value, and a fault detection operation is selected based on the insulation resistance value, a target fault detection operation is determined, and a fault detection operation is performed on the cable according to the target fault detection operation. Finally, fault detection data is obtained, and cable fault analysis is performed based on the fault detection data to determine the fault point location and fault type.

[0137] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified 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 of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0138] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A power cable diagnostic method, characterized in that: include: Acquiring preliminary diagnostic parameters and diagnostic confirmation parameters collected by the sensor array, wherein the preliminary diagnostic parameters include: cable temperature data and DC component; the diagnostic confirmation parameters include: partial discharge signal, cable current data and cable voltage data; Performing a preliminary diagnosis of the cable based on the preliminary diagnostic parameters, and when the preliminary diagnosis result of the cable indicates an abnormality, performing a secondary diagnosis of the cable based on the diagnosis confirmation parameters to determine a confirmed diagnosis result of the cable; When the cable is diagnosed as abnormal, performing an insulation resistance test to collect an insulation resistance value, selecting a fault detection operation based on the insulation resistance value, determining a target fault detection operation, and performing a fault detection operation on the cable according to the target fault detection operation; Acquire fault detection data, perform cable fault analysis based on the fault detection data, and determine the location of the fault point and the fault type; The performing secondary diagnosis of the cable based on the diagnosis confirmation parameters to determine the cable diagnosis result includes: Extracting features based on the partial discharge signal to determine partial discharge features, and performing abnormal discharge detection based on the partial discharge features to determine abnormal discharge detection results, wherein the abnormal discharge detection results include: whether abnormal discharge exists and the severity level of the abnormal discharge; Performing dielectric loss calculation based on the cable current data and the cable voltage data to determine a dielectric loss factor and a loss tangent; Obtaining a standard dielectric loss factor and a standard loss tangent, performing a dielectric loss test diagnosis based on the standard dielectric loss factor, the standard loss tangent, the dielectric loss factor, and the loss tangent, and determining a cable insulation performance diagnosis result, wherein the cable insulation performance diagnosis result includes: good insulation performance and abnormal insulation performance; Determine a cable diagnosis result by combining the abnormal discharge detection result and the cable insulation performance diagnosis result; The selecting a fault detection operation based on the insulation resistance value and determining a target fault detection operation includes: obtaining a cable characteristic impedance, performing a resistance comparison based on the insulation resistance value and the cable characteristic impedance, and determining that the target fault detection operation is an impulse high-voltage flashover method or a traveling wave method fault location when the insulation resistance value is greater than the cable characteristic impedance; When the insulation resistance value is less than the cable characteristic impedance, the target fault detection operation is determined to be a low-voltage pulse method or a DC high-voltage pulse method.

2. The power cable diagnostic method according to claim 1, characterized in that: After performing cable fault analysis based on the fault detection data and determining the fault location and fault type, the method further includes: Storing the fault point location and the fault type in a cable fault database, wherein the cable fault data of different areas in the cable fault database are stored in different partitions according to the fault point location; When a cable quality assessment instruction carrying a cable laying area is detected, cable fault data is screened in the cable fault database based on the cable laying area in the cable quality assessment instruction to determine target cable fault data; A cable health assessment is performed based on the target cable fault data to generate a cable health assessment report.

3. The power cable diagnosis method according to claim 1, characterized in that: After obtaining the preliminary diagnostic parameters collected by the sensor array, the method further includes: Perform cable heating diagnosis based on the cable temperature data in the preliminary diagnostic parameters, and determine a cable heating diagnosis result; When the cable heating diagnosis result indicates that the cable is overheated, overheat decomposition gas data collected by the gas sensor is obtained, and an overheat level analysis is performed based on the overheat decomposition gas data to determine the cable overheat level.

4. The power cable diagnosis method according to claim 1, characterized in that: After performing cable fault analysis based on the fault detection data and determining the fault location and fault type, the method further includes: Acquiring power cable network information, and constructing a model based on the power cable network information to obtain a three-dimensional model of the cable network; A fault visualization display is performed based on the fault point location, the fault type and the three-dimensional model of the cable network to obtain a fault source labeled cable model, wherein the fault source labeled cable model is used to intuitively display the distribution location of the fault source in the power cable network.

5. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the power cable diagnostic method according to any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the power cable diagnosis method according to any one of claims 1 to 4.

7. A computer program product, characterized in that The method comprises a computer program, wherein the computer program is executed by a processor to perform the power cable diagnosis method according to any one of claims 1 to 4.

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