A high-voltage cable defect identification method, device and storage medium

Abstract

The present invention relates to the field of electrical testing technology, specifically disclosing a high-voltage cable defect identification method, device, and storage medium. The method comprises: real-time monitoring of high-voltage cable lines, determination of the presence of high-voltage cable line defects, acquisition of potentially defective line segments, identification of specific defect types, and feedback on specific defect types. The present invention comprehensively determines whether a high-voltage cable line in a target area has defects based on three aspects: voltage anomalies, current anomalies, and pressure anomalies. This avoids the short-term, random nature of single power parameter anomalies, helps reduce the false alarm rate of high-voltage cable defect detection, and thus provides a comprehensive understanding of the status of the high-voltage cable line.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical detection, and relates to a high-voltage cable defect identification method, equipment and storage medium. Background Art

[0002] High-voltage cables are a vital component of power systems, and their safe operation is crucial to the stability and reliability of the entire system. However, due to long-term use and environmental factors, high-voltage cables may develop various defects and faults, such as insulation aging, wear, and cracking. These problems not only affect the efficiency and quality of power transmission but can also cause serious safety accidents. Therefore, timely detection and accurate identification of high-voltage cable defects are crucial to maintaining and improving the reliability of cable systems.

[0003] Existing methods have circumvented the problems of low detection efficiency and high missed detection rate of traditional manual inspection and offline detection methods. They use high-tech sensor technology and signal processing technology to monitor the operating status of cables in real time and continuously, and promptly detect and identify defects with alarms. Although they can meet existing requirements, they still have certain limitations, which are specifically manifested in the following:

[0004] 1. Existing methods for detecting high-voltage cable defects often focus on monitoring and analyzing abnormalities in a single electrical parameter, such as abnormal voltage or current values ​​or fluctuations. However, due to natural factors or other influences, voltage anomalies or current fluctuations can sometimes be short-lived and accidental. Therefore, this single-parameter monitoring approach does not necessarily accurately reflect the presence of a true defect in the high-voltage cable, and may result in false positives, increasing the workload of maintenance personnel.

[0005] 2. Existing methods cannot provide sufficient positioning accuracy to accurately determine the specific location of defects on high-voltage cable lines, resulting in a lot of time and effort required to check each defect one by one, thereby increasing maintenance and troubleshooting costs. In addition, the slow response speed of cable defects also affects the stability and reliability of the power system.

[0006] 3. The existing technology lacks a digital display of the risk possibility of defect types in various aspects of the high-voltage cable line section, such as sheath defects, accessory defects, insulation defects or conductor defects, or focuses too much on a certain type of defect and is unable to comprehensively detect and identify multiple types of defects, affecting the accuracy, comprehensiveness and effectiveness of high-voltage cable line defect identification, and thus affecting subsequent repair and maintenance work. Summary of the Invention

[0007] In view of this, in order to solve the problems raised in the above background technology, a high-voltage cable defect identification method, device and storage medium are proposed.

[0008] The object of the present invention can be achieved through the following technical solutions: In a first aspect, the present invention provides a method for identifying defects in a high-voltage cable, comprising:

[0009] S1. Real-time monitoring of high-voltage cable lines: Real-time monitoring of the power parameters and pressure parameters of high-voltage cable lines in the target area.

[0010] S2. Determine whether there are defects in the high-voltage cable line: Determine whether there are defects in the high-voltage cable line in the current target area based on the current power parameters and pressure parameters of the high-voltage cable line in the target area. If it is determined to be present, execute S3, otherwise return to S1.

[0011] S3. Acquisition of potential defective line segments: Based on the inductance probes deployed at the starting and ending points of the high-voltage cable lines in the target area, the potential defective line segments in the high-voltage cable lines in the target area are acquired.

[0012] S4. Identify specific defect types: Check the sheath layer, accessories, insulation layer, and conductor layer of the potential defective line section in turn, and analyze the sheath defect risk factor of the potential defective line section Accessory defect risk factor Insulation defect risk factor and conductor defect risk factor Identify specific defect types in potentially defective line sections.

[0013] S5. Specific defect type feedback: Feedback on the specific defect type of the identified potential defect line segment.

[0014] Preferably, the specific determination process of whether the high-voltage cable line in the current target area has defects is as follows: the power parameters include the operating voltage value and the operating current value at each set time point within the set time period, and the pressure parameters include the pressure value of each layout node of the line.

[0015] According to the reasonable range of high-voltage cable operating voltage stored in the WEB cloud, the upper and lower limits of the range are extracted, and the reference operating voltage value U0 of the high-voltage cable is obtained by average calculation. Combined with the operating voltage value U0 at each set time point in the set time period in the current power parameters of the high-voltage cable line in the target area, i , where i is the number of each set time point in the set time period, i = 1, 2, ..., a, by the formula The current voltage anomaly assessment coefficient of the high-voltage cable line in the target area is obtained, where ΔU is the preset reasonable deviation threshold of the high-voltage cable operating voltage, and U i-1 is the operating voltage value of the high-voltage cable line in the target area at the i-1th set time point within the set time period, and a is the number of set time points within the set time period.

[0016] If the current voltage anomaly assessment coefficient of the high-voltage cable line in the target area is greater than or equal to the preset high-voltage cable line voltage anomaly assessment coefficient reasonable threshold, it is determined that there is a current voltage anomaly in the high-voltage cable line in the target area.

[0017] Similarly, the operating current values ​​at each set time point within the set time period from the current power parameters of the high-voltage cable line in the target area and the pressure values ​​of each layout node of the line from the pressure parameters are extracted to determine whether there are current abnormalities or pressure abnormalities in the high-voltage cable line in the target area.

[0018] For voltage abnormalities, current abnormalities and pressure abnormalities, if two or more abnormal conditions currently exist in the high-voltage cable line in the target area, it is determined that the high-voltage cable line in the current target area has defects; otherwise, it is determined that the high-voltage cable line in the current target area does not have defects.

[0019] Preferably, the specific process of obtaining the potential defective line section in the high-voltage cable line in the target area is as follows: if there is a defect in the high-voltage cable line in the target area, the defect location on the line will propagate a voltage traveling wave to both ends of the line at the speed of light, and the inductance probes arranged at the starting and ending points of the high-voltage cable line in the target area sense the voltage traveling wave and record the arrival time of the voltage traveling wave. The arrival time points of the voltage traveling wave recorded at the starting and ending points are subtracted to obtain a reference time difference Δt. According to the total length L of the high-voltage cable line in the target area stored in the WEB cloud, the reference time difference Δt is obtained by the formula The line length between the potential defect occurrence point and the terminal point is obtained, where c is the speed of light. The potential defect occurrence point on the high-voltage cable line in the target area is then determined. With this point as the center point, the line is extended to both ends with a set distance Δd to obtain the potential defect line segment in the high-voltage cable line in the target area.

[0020] Preferably, the specific process of inspecting the sheath layer of the potential defective line section and analyzing the sheath defect risk coefficient of the potential defective line section is as follows: remotely controlling a drone to fly to the location of the potential defective line section in the high-voltage cable line in the target area, scanning and capturing images of the sheath layer of the potential defective line section by a high-definition camera carried by the drone, and obtaining the deformation degree γ and the total area s of the damaged area of ​​the sheath layer of the potential defective line section. 破 , length of each crack l q and depth n q , where q is the number of each crack in the sheath layer, q = 1, 2, ..., w.

[0021] Extract the basic dimension parameters of the high-voltage cable sheath layer in the target area stored in the WEN cloud, including the standard surface area s0 and thickness m0 of the sheath layer, and analyze the sheath defect risk coefficient of the potential defective line section The calculation formula is:

[0022]

[0023] Preferably, the specific process of inspecting the accessories of the potential defective line segment and analyzing the defect risk coefficient of the accessories of the potential defective line segment is: using a high-definition camera carried by a drone to capture images of the accessories of the potential defective line segment, and obtaining the appearance quality evaluation coefficient δ of the insulating sleeve of the accessory of the potential defective line segment.

[0024] The contact temperature g and contact resistance r of the contact points between the accessories and high-voltage cables of the potential defective line section are monitored and obtained by the temperature sensor and resistance meter carried by the drone, thereby analyzing the accessory defect risk coefficient of the potential defective line section. The calculation formula is: Where g0 is the reasonable contact temperature threshold of the contact point between the preset accessory and the high-voltage cable, r0 is the reference contact resistance of the contact point between the preset accessory and the high-voltage cable, and e is a natural constant.

[0025] Preferably, the specific process of inspecting the insulation layer of the potential defective line segment and analyzing the insulation defect risk coefficient of the potential defective line segment is: using the partial discharge detection equipment carried by the drone to perform partial discharge detection on the potential defective line segment from left to right, locating the discharge points in the potential defective line segment, and collecting the discharge intensity h of each located discharge point. f , discharge frequency k f and discharge mode, where f is the number of each positioning discharge point, f = 1, 2, ..., x, according to the discharge mode of each positioning discharge point, set the abnormal discharge behavior influence weight θ of each positioning discharge point f , combined with the current monitoring environmental parameters of the potential defective line section, including the ambient temperature value A, humidity value B and total precipitation C, the formula The abnormal discharge behavior coefficient of the insulation layer of the potential defective line section is obtained, where A0, B0, and C0 are the temperature threshold, humidity threshold, and total precipitation threshold of the preset high-voltage cable insulation layer under a suitable environment, h0 and k0 are the discharge intensity and discharge frequency of the preset high-voltage cable positioning discharge point reference, and π is 180°.

[0026] Continue to use the resistance measuring instrument on the drone to detect the insulation resistance R at each node of the potential defective line section j , where j is the number of each node in the potential defective line segment, j = 1, 2, ..., b, according to the formula The abnormal resistance behavior coefficient of the insulation layer of the potential defective line section is obtained, where R0 is a reasonable threshold value of the preset high-voltage cable insulation resistance.

[0027] Analyze the insulation defect risk factor of potential defective line sections The calculation formula is: in The corresponding weight ratios of the preset abnormal discharge behavior coefficient of the insulating layer and the abnormal resistance behavior coefficient of the insulating layer.

[0028] Preferably, the specific process of inspecting the conductor layer of the potential defective line segment and analyzing the conductor defect risk coefficient of the potential defective line segment is as follows: an ultrasonic probe of an ultrasonic detection device carried by an unmanned aerial vehicle is brought close to the potential defective line segment, an ultrasonic generator of the ultrasonic detection device transmits an ultrasonic signal of a specific frequency into the conductor layer, and the reflection velocity v of each reflected sound wave signal in the potential defective line segment is collected. ω , reflectivity η ω and the maximum amplitude z ω , where ω is the number of each reflected acoustic wave signal in the potential defect line segment, ω = 1, 2, ..., ψ, calculate the conductor defect risk coefficient of the potential defect line segment in It is the preset correction factor for high voltage cable conductor defect risk assessment.

[0029] Preferably, the identification of the specific defect type of the potential defective line segment includes: comparing the sheath defect risk coefficient of the potential defective line segment with a preset reasonable threshold value of the high-voltage cable sheath defect risk coefficient; if the sheath defect risk coefficient of the potential defective line segment is greater than or equal to the preset reasonable threshold value of the high-voltage cable sheath defect risk coefficient, then the identification of the specific defect type of the potential defective line segment includes a sheath defect.

[0030] Similarly, based on the accessory defect risk coefficient, insulation defect risk coefficient, and conductor defect risk coefficient of the potential defect line section, it is identified whether the specific defect types of the potential defect line section include accessory defects, insulation defects, and conductor defects.

[0031] According to the specific defect types of the potential defective line sections identified, the feedback is arranged in order of priority from large to small according to the values ​​of their corresponding defect risk coefficients.

[0032] A second aspect of the present invention provides a device comprising: a processor, a memory, and a communication bus. The memory stores a computer-readable program executable by the processor. The communication bus enables communication between the processor and the memory. When the processor executes the computer-readable program, it implements the steps of a high-voltage cable defect identification method.

[0033] A third aspect of the present invention provides a storage medium, wherein the computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the high-voltage cable defect identification method.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] (1) The present invention comprehensively determines whether there are defects in the high-voltage cable line in the current target area from three aspects: voltage abnormality, current abnormality and pressure abnormality, avoiding the short-term randomness of a single power parameter abnormality, helping to reduce the false alarm rate of high-voltage cable defect detection, and thus improving the comprehensive understanding of the status of the high-voltage cable line.

[0036] (2) The present invention uses the traveling wave method to obtain the potential defective line segments in the high-voltage cable line in the target area, accurately determine the specific location of the defects on the high-voltage cable line, greatly reduce the time cost and energy investment in defect detection, thereby reducing the cost of maintenance and fault detection, and also provide a basis for the subsequent identification of specific defect types of high-voltage cables in the target area.

[0037] (3) The present invention collects images of the sheath layer and accessories of the potential defective line section in the high-voltage cable line in the target area, and monitors the contact temperature and contact resistance of the contact points between the accessories and the high-voltage cable, so as to reasonably and effectively analyze the sheath defect risk factor and the accessory defect risk factor of the potential defective line section, thereby identifying whether there are sheath defects and accessory defects, and promptly discovering problems such as abnormal deformation and damage of the sheath layer, abnormal wear of the accessories, and abnormal contact between the accessories and the cable, which helps to timely and efficiently identify the defect type at the relatively external level of the potential defective line section.

[0038] (4) The present invention comprehensively examines the insulation defect risk coefficient of the potential defective line segment through the partial discharge detection method and the insulation resistance monitoring method, examines the conductor defect risk coefficient of the potential defective line segment through the ultrasonic detection method, and uses multiple detection methods to respectively perform detailed identification of the insulation defects and conductor defects inside the potential defective line segment, thereby greatly improving the accuracy of the defect identification results at the internal level of the potential defective line segment.

[0039] (5) The present invention determines the specific defect type of the potential defect line segment based on the sheath defect risk coefficient, accessory defect risk coefficient, insulation defect risk coefficient and conductor defect risk coefficient of the potential defect line segment, and arranges the priority feedback order according to the numerical value of its defect risk coefficient. It comprehensively detects and identifies multiple types of defects in the potential defect line segment and displays the risk possibility in a digital way, which helps to objectively evaluate the severity of the potential defects and intuitively determine the priority treatment areas, thereby improving the efficiency of maintenance and repair. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For passengers with ordinary technical skills in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 The present invention provides a flowchart of a method for identifying defects in a high-voltage cable. DETAILED DESCRIPTION

[0042] The above contents described below in conjunction with the implementation of the present invention are merely examples and explanations of the concept of the present invention. Technical passengers in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they shall fall within the scope of protection of the present invention.

[0043] See also Figure 1 As shown, the purpose of the present invention can be achieved through the following technical solutions: The first aspect of the present invention provides a high-voltage cable defect identification method, the method comprising: S1. Real-time monitoring of high-voltage cable lines: real-time monitoring of the power parameters and pressure parameters of the high-voltage cable lines in the target area.

[0044] It should be noted that the above-mentioned real-time monitoring of the power parameters and pressure parameters of the high-voltage cable line in the target area is obtained through the online power monitoring unit in the equipment.

[0045] S2. Determine whether there are defects in the high-voltage cable line: Determine whether there are defects in the high-voltage cable line in the current target area based on the current power parameters and pressure parameters of the high-voltage cable line in the target area. If it is determined to be present, execute S3, otherwise return to S1.

[0046] Specifically, the specific determination process of whether the high-voltage cable line in the current target area has defects is as follows: the power parameters include the operating voltage value and the operating current value at each set time point within the set time period, and the pressure parameters include the pressure value of each layout node of the line.

[0047] According to the reasonable range of high-voltage cable operating voltage stored in the WEB cloud, the upper and lower limits of the range are extracted, and the reference operating voltage value U0 of the high-voltage cable is obtained by average calculation. Combined with the operating voltage value U0 at each set time point in the set time period in the current power parameters of the high-voltage cable line in the target area, i , where i is the number of each set time point in the set time period, i = 1, 2, ..., a, by the formula The current voltage anomaly assessment coefficient of the high-voltage cable line in the target area is obtained, where ΔU is the preset reasonable deviation threshold of the high-voltage cable operating voltage, and U i-1 is the operating voltage value of the high-voltage cable line in the target area at the i-1th set time point within the set time period, and a is the number of set time points within the set time period.

[0048] If the current voltage anomaly assessment coefficient of the high-voltage cable line in the target area is greater than or equal to the preset high-voltage cable line voltage anomaly assessment coefficient reasonable threshold, it is determined that there is a current voltage anomaly in the high-voltage cable line in the target area.

[0049] Similarly, the operating current values ​​at each set time point within the set time period from the current power parameters of the high-voltage cable line in the target area and the pressure values ​​of each layout node of the line from the pressure parameters are extracted to determine whether there are current abnormalities or pressure abnormalities in the high-voltage cable line in the target area.

[0050] It should be noted that the data for determining whether there are current anomalies and pressure anomalies in the high-voltage cable lines in the target area are based on the current anomaly assessment coefficient and pressure anomaly assessment coefficient of the high-voltage cable lines in the target area. The calculation method is the same as the current anomaly assessment coefficient of the high-voltage cable lines in the target area, and will not be repeated here.

[0051] For voltage abnormalities, current abnormalities and pressure abnormalities, if two or more abnormal conditions currently exist in the high-voltage cable line in the target area, it is determined that the high-voltage cable line in the current target area has defects; otherwise, it is determined that the high-voltage cable line in the current target area does not have defects.

[0052] The embodiment of the present invention comprehensively determines whether there are defects in the high-voltage cable line in the current target area from three aspects: voltage abnormality, current abnormality and pressure abnormality, avoids the short-term randomness of a single power parameter abnormality, helps to reduce the false alarm rate of high-voltage cable defect detection, and thus improves the comprehensive understanding of the status of the high-voltage cable line.

[0053] S3. Acquisition of potential defective line segments: Based on the inductance probes deployed at the starting and ending points of the high-voltage cable lines in the target area, the potential defective line segments in the high-voltage cable lines in the target area are acquired.

[0054] Specifically, the specific acquisition process of the potential defective line section in the high-voltage cable line in the target area is as follows: if there is a defect in the high-voltage cable line in the target area, the defect location on the line will propagate a voltage traveling wave to both ends of the line at the speed of light. The voltage traveling wave is sensed by the inductance probes arranged at the starting and ending points of the high-voltage cable line in the target area, and the arrival time of the voltage traveling wave is recorded. The arrival time points of the voltage traveling wave recorded at the starting and ending points are subtracted to obtain a reference time difference Δt. According to the total length L of the high-voltage cable line in the target area stored in the WEB cloud, the reference time difference Δt is obtained by the formula The line length between the potential defect occurrence point and the terminal point is obtained, where c is the speed of light. The potential defect occurrence point on the high-voltage cable line in the target area is then determined. With this point as the center point, the line is extended to both ends with a set distance Δd to obtain the potential defect line segment in the high-voltage cable line in the target area.

[0055] It should be noted that the above-mentioned set distance is obtained by extracting the defect occurrence location points and their diffusion lengths recorded in the historical maintenance data of the target area high-voltage cable line stored in the WEB cloud based on the historical maintenance data. The average diffusion length D of the defect occurrence location points of the target area high-voltage cable line is obtained by mean calculation, which is calculated by the formula Get the set distance.

[0056] The embodiment of the present invention utilizes the traveling wave method to obtain the potential defective line segments in the high-voltage cable line in the target area, accurately determines the specific location of the defects on the high-voltage cable line, greatly reduces the time cost and energy investment in defect detection, thereby reducing the cost of maintenance and troubleshooting, and also provides a basis for the subsequent identification of specific defect types of the high-voltage cables in the target area.

[0057] S4. Identify specific defect types: Check the sheath layer, accessories, insulation layer, and conductor layer of the potential defective line section in turn, and analyze the sheath defect risk factor of the potential defective line section Accessory defect risk factor Insulation defect risk factor and conductor defect risk factor Identify specific defect types in potentially defective line sections.

[0058] Specifically, the specific process of inspecting the sheath layer of the potential defective line section and analyzing the sheath defect risk coefficient of the potential defective line section is as follows: remotely controlling the drone to fly to the location of the potential defective line section in the high-voltage cable line in the target area, scanning and capturing images of the sheath layer of the potential defective line section by a high-definition camera carried by the drone, and obtaining the deformation degree γ and the total area s of the damaged area of ​​the sheath layer of the potential defective line section. 破 , length of each crack l q and depth n q, where q is the number of each crack in the sheath layer, q = 1, 2, ..., w.

[0059] It should be noted that the specific method for obtaining the deformation degree, total area of ​​the damaged area, length and depth of each crack of the sheath layer of the potential defective line section is as follows: the sheath layer of the potential defective line section is scanned and imaged by a high-definition camera carried by a drone, and the three-dimensional model and image of the sheath layer of the potential defective line section are obtained respectively. The three-dimensional model of the sheath layer of the high-voltage cable per unit length stored in the WEB cloud is repeatedly spliced ​​until the length reaches 2Δd, forming a standard three-dimensional model of the sheath layer of the potential defective line section and obtaining the model volume V, which is compared with the currently obtained three-dimensional model of the sheath layer to obtain the non-overlapping model volume V0 between the two. According to the formula The deformation degree of the sheath layer of the potential defective line section is obtained.

[0060] The sheath layer image of the potential defective line section is preprocessed to identify the sheath damage features and crack features in the image, and the damaged areas and crack areas are extracted. The number of pixels in each damaged area is converted into the area of ​​each damaged area according to a set proportional relationship, and the total area of ​​the sheath layer damaged area of ​​the potential defective line section is accumulated. Similarly, the number of pixels in each crack area is converted into the length of each crack according to a set proportional relationship, and the depth of each crack is obtained by the difference between the edge pixels of each crack area and the pixels of its adjacent area.

[0061] Extract the basic dimension parameters of the high-voltage cable sheath layer in the target area stored in the WEN cloud, including the standard surface area s0 and thickness m0 of the sheath layer, and analyze the sheath defect risk coefficient of the potential defective line section The calculation formula is:

[0062]

[0063] Specifically, the specific process of inspecting the accessories of the potential defective line segment and analyzing the defect risk coefficient of the accessories of the potential defective line segment is: using a high-definition camera carried by a drone to capture images of the accessories of the potential defective line segment, and obtaining the appearance quality evaluation coefficient δ of the insulating sleeve of the accessory of the potential defective line segment.

[0064] It should be noted that the specific method for obtaining the appearance quality evaluation coefficient of the accessory insulation sleeve of the potential defective line section is as follows: through the accessory image of the potential defective line section, the total wear area s of the accessory insulation sleeve layer is obtained. 磨 With the maximum depth n′, extract the size parameters of the accessory insulation layer of the high-voltage cable in the target area stored in the WEN cloud, including the standard surface area s′ and wrapping thickness m′ of the accessory insulation layer, according to the formula The appearance quality evaluation coefficient of the accessory insulation sleeve of the potential defective line section is obtained, where e is a natural constant.

[0065] It should be further explained that the method for obtaining the total area and maximum depth of the wear area of ​​the above-mentioned accessory insulation layer is consistent with the method for obtaining the total area of ​​the damaged area and the depth of each crack of the sheath layer of the potential defective line section, which will not be further elaborated here.

[0066] The contact temperature g and contact resistance r of the contact points between the accessories and high-voltage cables of the potential defective line section are monitored and obtained by the temperature sensor and resistance meter carried by the drone, thereby analyzing the accessory defect risk coefficient of the potential defective line section. The calculation formula is: Where g0 is the preset reasonable contact temperature threshold of the contact point between the accessory and the high-voltage cable, and r0 is the preset reference contact resistance of the contact point between the accessory and the high-voltage cable.

[0067] It should be noted that, under normal circumstances, the contact resistance of high-voltage cable accessories is required to be within a certain resistance range. This resistance range is usually determined by the type, specifications and design requirements of the cable accessories, and needs to be controlled and tested during the design and manufacturing process of the cable accessories. The reference contact resistance of the contact point between the accessories and the high-voltage cable in the above-mentioned potential defective line section is obtained by examining the high-voltage cable design drawings of the potential defective line section in the target area to obtain a reasonable contact resistance range of the contact point between the accessories and the high-voltage cable, and extracting the upper and lower limits of the range for average calculation.

[0068] It should also be noted that high-voltage cable accessories include components such as connectors, terminals, and jumper joints, which can effectively connect and expand high-voltage cable lines. Among them, connectors and terminals are used to connect high-voltage cables and other equipment or conductors, while jumper joints are used to connect two or more high-voltage cables. Generally, accessories appear at the end of high-voltage cables. In a few cases, they can be used to connect two sections of high-voltage cables and appear at the front or middle of the line. For the risk analysis of accessory defects in potential defective line sections, it is necessary to examine the high-voltage cable design drawings of the potential defective line sections in the target area in advance to understand whether there are accessories in the potential defective line sections.

[0069] The embodiment of the present invention acquires images of the sheath layer and accessories of the potential defective line section in the high-voltage cable line in the target area, and monitors the contact temperature and contact resistance of the contact points between the accessories and the high-voltage cable, so as to reasonably and effectively analyze the sheath defect risk factor and the accessory defect risk factor of the potential defective line section, thereby identifying whether there are sheath defects and accessory defects, and promptly discovering problems such as abnormal deformation and damage of the sheath layer appearance, abnormal wear of the accessories appearance, and abnormal contact between the accessories and the cable, which helps to timely and efficiently identify the defect type at the relatively external level of the potential defective line section.

[0070] Specifically, the specific process of inspecting the insulation layer of the potential defective line segment and analyzing the insulation defect risk coefficient of the potential defective line segment is as follows: using the partial discharge detection equipment carried by the UAV to perform partial discharge detection on the potential defective line segment from left to right, locate the discharge points in the potential defective line segment, and collect the discharge intensity h of each located discharge point. f , discharge frequency k f and discharge mode, where f is the number of each positioning discharge point, f = 1, 2, ..., x, according to the discharge mode of each positioning discharge point, set the abnormal discharge behavior influence weight θ of each positioning discharge point f , combined with the current monitoring environmental parameters of the potential defective line section, including the ambient temperature value A, humidity value B and total precipitation C, the formula The abnormal discharge behavior coefficient of the insulation layer of the potential defective line section is obtained, where A0, B0, and C0 are the temperature threshold, humidity threshold, and total precipitation threshold of the preset high-voltage cable insulation layer under a suitable environment, h0 and k0 are the discharge intensity and discharge frequency of the preset high-voltage cable positioning discharge point reference, and π is 180°.

[0071] It should be noted that the basis for setting the influence weight of abnormal discharge behavior of each located discharge point based on the discharge mode of each located discharge point is: the discharge mode of the local discharge point of the high-voltage cable insulation layer is corona discharge, dielectric breakdown discharge, surface discharge, local discharge in the gap, tree discharge, etc. Different discharge modes may correspond to different insulation layer states and fault types. For example, corona discharge usually occurs on the surface or near the surface of the insulation layer and may be related to problems such as contamination, moisture, and cracks in the insulation layer. Dielectric breakdown discharge usually occurs inside or near the interior of the insulation layer and may be related to problems such as the quality and aging of the insulation material. Therefore, different influence weights can be set according to the type of discharge mode.

[0072] It should also be noted that the current monitoring environmental parameters of the above-mentioned potential defective line sections can be obtained using the temperature and humidity sensors and precipitation sensors installed on the drone.

[0073] It should be further explained that the analysis method of the abnormal discharge behavior coefficient of the insulation layer of the potential defective line section is not only based on the discharge parameters of each located discharge point of the potential defective line section, but also effectively takes into account the possible aggravated impact of the current environmental parameters of the potential defective line section on the discharge behavior of each located discharge point, thereby achieving a comprehensive and accurate analysis of the abnormal discharge behavior coefficient of the insulation layer of the potential defective line section.

[0074] Continue to use the resistance measuring instrument on the drone to detect the insulation resistance R at each node of the potential defective line section j , where j is the number of each node in the potential defective line segment, j = 1, 2, ..., b, according to the formula The abnormal resistance behavior coefficient of the insulation layer of the potential defective line section is obtained, where R0 is a reasonable threshold value of the preset high-voltage cable insulation resistance.

[0075] Analyze the insulation defect risk factor of potential defective line sections The calculation formula is: in The corresponding weight ratios of the preset abnormal discharge behavior coefficient of the insulating layer and the abnormal resistance behavior coefficient of the insulating layer.

[0076] Specifically, the specific process of inspecting the conductor layer of the potential defective line segment and analyzing the conductor defect risk coefficient of the potential defective line segment is as follows: the ultrasonic probe of the ultrasonic detection equipment carried by the drone is placed close to the potential defective line segment, the ultrasonic generator of the ultrasonic detection equipment transmits an ultrasonic signal of a specific frequency into the conductor layer, and the reflection velocity v of each reflected sound wave signal in the potential defective line segment is collected. ω , reflectivity η ω and the maximum amplitude z ω , where ω is the number of each reflected acoustic wave signal in the potential defect line segment, ω = 1, 2, ..., ψ, calculate the conductor defect risk coefficient of the potential defect line segment in It is the preset correction factor for high voltage cable conductor defect risk assessment.

[0077] It should be noted that when there are no problems with the conductor inside the high-voltage cable, the ultrasonic signal will not be reflected back. This is because ultrasonic waves need to propagate in the medium. If the conductor is intact, the ultrasonic waves will propagate directly in the conductor without encountering defects or interfaces and causing reflections. However, if there are defects, such as cracks, voids, or corrosion, the ultrasonic waves will be reflected or scattered when encountering these discontinuous interfaces. These reflected or scattered sound wave signals can be received by the ultrasonic probe and converted into electrical signals. By analyzing the received sound wave signals, it is possible to determine whether there are defects inside the conductor and the related properties of the defects.

[0078] It's also important to note that if the reflection velocity of the acoustic wave reflection signal is low, this may indicate that the ultrasonic wave encountered significant dielectric changes during propagation, such as material interfaces, defects, or cracks. These changes cause the sound wave to reflect or scatter, reducing the reflection velocity. Therefore, the lower the reflection velocity, the more likely it is that there are significant dielectric changes in the object being tested, which can be a sign of a conductor defect.

[0079] The embodiment of the present invention examines the insulation defect risk coefficient of the potential defective line segment through the partial discharge detection method and the insulation resistance monitoring method, examines the conductor defect risk coefficient of the potential defective line segment through the ultrasonic detection method, and utilizes multiple detection methods to respectively perform detailed identification of the insulation defects and conductor defects inside the potential defective line segment, thereby greatly improving the accuracy of the defect identification results at the internal level of the potential defective line segment.

[0080] Specifically, the identification of the specific defect type of the potential defect line segment includes: comparing the sheath defect risk coefficient of the potential defect line segment with a preset reasonable threshold value of the high-voltage cable sheath defect risk coefficient; if the sheath defect risk coefficient of the potential defect line segment is greater than or equal to the preset reasonable threshold value of the high-voltage cable sheath defect risk coefficient, then the identification of the specific defect type of the potential defect line segment includes a sheath defect.

[0081] Similarly, based on the accessory defect risk coefficient, insulation defect risk coefficient, and conductor defect risk coefficient of the potential defect line section, it is identified whether the specific defect types of the potential defect line section include accessory defects, insulation defects, and conductor defects.

[0082] According to the specific defect types of the potential defective line sections identified, the feedback is arranged in order of priority from large to small according to the values ​​of their corresponding defect risk coefficients.

[0083] S5. Specific defect type feedback: Feedback on the specific defect type of the identified potential defect line segment.

[0084] The embodiment of the present invention determines the specific defect type of the potential defect line segment based on the sheath defect risk coefficient, accessory defect risk coefficient, insulation defect risk coefficient and conductor defect risk coefficient of the potential defect line segment, and arranges them in priority feedback order according to the numerical values ​​of their defect risk coefficients. It comprehensively detects and identifies multiple types of defects in the potential defect line segment and displays the risk possibility in a digital way, which helps to objectively evaluate the severity of the potential defects and intuitively determine the priority areas for treatment, thereby improving the efficiency of maintenance and repair.

[0085] A second aspect of the present invention provides a device comprising: a processor, a memory, and a communication bus. The memory stores a computer-readable program executable by the processor. The communication bus enables communication between the processor and the memory. When the processor executes the computer-readable program, it implements the steps of a high-voltage cable defect identification method.

[0086] A third aspect of the present invention provides a storage medium, wherein the computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the high-voltage cable defect identification method.

[0087] The above contents are merely examples and explanations of the concept of the present invention. Technical passengers in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A method for identifying defects in high-voltage cables, characterized in that: include: S1. Real-time monitoring of high-voltage cable lines: Real-time monitoring of power and pressure parameters of high-voltage cable lines in the target area; S2. Determine whether there are defects in the high-voltage cable line: Determine whether there are defects in the high-voltage cable line in the current target area based on the current power parameters and pressure parameters of the high-voltage cable line in the target area. If so, execute S3, otherwise return to S1; S3. Acquisition of potential defective line segments: Based on the inductance probes arranged at the starting and ending points of the high-voltage cable lines in the target area, potential defective line segments in the high-voltage cable lines in the target area are acquired; S4. Identify specific defect types: Check the sheath layer, accessories, insulation layer, and conductor layer of the potential defective line section in turn, and analyze the sheath defect risk factor of the potential defective line section , Accessory defect risk factor , Insulation defect risk factor and conductor defect risk factor ,identify the specific defect type of the potential defective line segment; S5. Specific defect type feedback: Provides feedback on the specific defect type of the identified potential defect line segment; The specific process of inspecting the insulation layer of the potential defective line section and analyzing the insulation defect risk coefficient of the potential defective line section is as follows: using the partial discharge detection equipment carried by the drone to perform partial discharge detection on the potential defective line section from left to right, locate the discharge points in the potential defective line section, and collect the discharge intensity of each located discharge point. , discharge frequency and discharge mode, where is the number of each positioning discharge point, , according to the discharge mode of each positioning discharge point, set the influence weight of abnormal discharge behavior of each positioning discharge point , combined with the current monitoring environmental parameters of the potential defective line section, including the ambient temperature value , humidity value and total precipitation , according to the formula The abnormal discharge behavior coefficient of the insulation layer of the potential defective line section is obtained, where The temperature threshold, humidity threshold and total precipitation threshold of the high-voltage cable insulation layer in the preset suitable environment are: The discharge intensity and discharge frequency are referenced for the preset high-voltage cable discharge point positioning. for ; Continue to use the resistance measuring instrument carried by the drone to detect the insulation resistance of each node on the potential defective line section ,in is the number of each node of the potential defective line segment, , according to the formula The abnormal resistance behavior coefficient of the insulation layer of the potential defective line section is obtained, where A reasonable threshold value for the preset high-voltage cable insulation resistance; Analyze the insulation defect risk factor of potential defective line sections , and its calculation formula is: ,in The corresponding weight ratios of the preset abnormal discharge behavior coefficient of the insulating layer and the abnormal resistance behavior coefficient of the insulating layer.

2. A high-voltage cable defect identification method according to claim 1, characterized in that: The specific process of determining whether the high-voltage cable line in the current target area has defects is as follows: the power parameters include the operating voltage value and the operating current value at each set time point within the set time period, and the pressure parameters include the pressure value of each layout node of the line; According to the reasonable range of high-voltage cable operating voltage stored in the WEB cloud, the upper and lower limits of the range are extracted, and the reference operating voltage value of the high-voltage cable is obtained by average calculation. , combined with the current power parameters of the high-voltage cable line in the target area, the operating voltage value at each set time point within the set time period ,in It is the number of each set time point within the set time period. , according to the formula Get the current voltage anomaly assessment coefficient of the high-voltage cable line in the target area, where It is the preset reasonable deviation threshold of high voltage cable operating voltage. Set the target area high voltage cable line within the time period The operating voltage value at a set time point, Set the number of time points within the set time period; If the current voltage anomaly assessment coefficient of the high-voltage cable line in the target area is greater than or equal to the preset high-voltage cable line voltage anomaly assessment coefficient reasonable threshold, it is determined that the high-voltage cable line in the target area currently has a voltage anomaly; Similarly, the operating current value at each set time point within the set time period in the current power parameters of the high-voltage cable line in the target area and the pressure value of each node of the line in the pressure parameters are extracted to determine whether there are abnormal current and pressure conditions in the high-voltage cable line in the target area. For voltage abnormalities, current abnormalities and pressure abnormalities, if two or more abnormal conditions currently exist in the high-voltage cable line in the target area, it is determined that the high-voltage cable line in the current target area has defects; otherwise, it is determined that the high-voltage cable line in the current target area does not have defects.

3. A high-voltage cable defect identification method according to claim 2, characterized in that: The specific acquisition process of the potential defective line section in the high-voltage cable line in the target area is as follows: if there is a defect in the high-voltage cable line in the target area, the defect location point on the line will propagate a voltage traveling wave to both ends of the line at the speed of light. The inductance probes arranged at the starting and ending points of the high-voltage cable line in the target area sense the voltage traveling wave and record the arrival time of the voltage traveling wave. The reference time difference is obtained by subtracting the arrival time of the voltage traveling wave recorded at the starting and ending points. , based on the total length of high-voltage cable lines in the target area stored in the WEB cloud , according to the formula Get the line length from the potential defect location to the terminal point, where The speed of light is used to determine the potential defect location on the high-voltage cable line in the target area, and the distance is set to the center point. Extend to both ends of the line to obtain potential defective line sections in the high-voltage cable line in the target area.

4. A high-voltage cable defect identification method according to claim 3, characterized in that: The specific process of inspecting the sheath layer of the potential defective line section and analyzing the sheath defect risk coefficient of the potential defective line section is as follows: remotely controlling the drone to fly to the location of the potential defective line section in the high-voltage cable line in the target area, scanning and capturing images of the sheath layer of the potential defective line section by a high-definition camera carried by the drone, and obtaining the deformation degree of the sheath layer of the potential defective line section. , total area of ​​damaged area , length of each crack and depth ,in is the number of each crack in the sheath layer, ; Extract the basic dimension parameters of the high-voltage cable sheath layer in the target area from the WEN cloud storage, including the standard surface area of ​​the sheath layer and thickness , analyze the sheath defect risk factor of the potential defect line section , and its calculation formula is: .

5. A high-voltage cable defect identification method according to claim 1, characterized in that: The specific process of inspecting the accessories of the potential defective line section and analyzing the defect risk coefficient of the accessories of the potential defective line section is as follows: using the high-definition camera carried by the drone to capture images of the accessories of the potential defective line section, and obtaining the appearance quality evaluation coefficient of the insulation sleeve of the accessories of the potential defective line section. ; The contact temperature of the contact points between the accessories of the potential defective line section and the high-voltage cable is measured by the temperature sensor and resistance meter carried by the drone. , contact resistance Monitor and obtain, and analyze the accessory defect risk coefficient of the potential defect line section , and its calculation formula is: ,in is the reasonable threshold value of the contact temperature between the preset accessory and the high-voltage cable. is the reference contact resistance of the contact point between the preset accessory and the high-voltage cable, is a natural constant.

6. A high-voltage cable defect identification method according to claim 1, characterized in that: The specific process of inspecting the conductor layer of the potential defective line segment and analyzing the conductor defect risk coefficient of the potential defective line segment is as follows: an ultrasonic probe of an ultrasonic detection device carried by a drone is brought close to the potential defective line segment, an ultrasonic generator of the ultrasonic detection device transmits an ultrasonic signal into the conductor layer, and the reflection speed of each reflected sound wave signal in the potential defective line segment is collected. , reflectivity and the maximum amplitude ,in is the number of each reflected acoustic wave signal in the potential defect line section, , calculate the conductor defect risk coefficient of the potential defect line segment , ,in It is the preset correction factor for high voltage cable conductor defect risk assessment.

7. A high-voltage cable defect identification method according to claim 1, characterized in that: The identifying of the specific defect type of the potential defective line segment includes: comparing the sheath defect risk coefficient of the potential defective line segment with a preset reasonable threshold value of the high-voltage cable sheath defect risk coefficient; if the sheath defect risk coefficient of the potential defective line segment is greater than or equal to the preset reasonable threshold value of the high-voltage cable sheath defect risk coefficient, then identifying that the specific defect type of the potential defective line segment includes a sheath defect; Similarly, based on the accessory defect risk coefficient, insulation defect risk coefficient, and conductor defect risk coefficient of the potential defect line section, it is determined whether the specific defect types of the potential defect line section include accessory defects, insulation defects, and conductor defects; According to the specific defect types of the potential defective line sections identified, the feedback is arranged in order of priority from large to small according to the values ​​of their corresponding defect risk coefficients.

8. A device, characterized in that: include: Processor, memory and communication bus; The memory stores a computer-readable program that can be executed by the processor; the communication bus realizes the connection and communication between the processor and the memory; when the processor executes the computer-readable program, it implements the steps in the high-voltage cable defect identification method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the high-voltage cable defect identification method according to any one of claims 1 to 7.

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