Method for evaluating metal residue size of 10kV XLPE cable

Abstract

The 10kV XLPE cable metal residue size evaluation method provided by the invention comprises the following steps: radially injecting a microwave signal with a frequency band of 22GHz-30GHz into a 10kV XLPE cable to be evaluated, and extracting a plurality of microwave reflection characteristic frequencies and corresponding amplitudes according to an obtained microwave reflection curve; calculating an amplitude factor corresponding to each microwave reflection characteristic frequency according to each microwave reflection characteristic frequency and the corresponding amplitude; calculating a metal residual factor of the to-be-evaluated cable according to the amplitude and the amplitude factor corresponding to each microwave reflection characteristic frequency; and evaluating the size of the metal residue defect of the to-be-evaluated cable according to the metal residue factor. Thus, non-contact detection is carried out on the 10kV XLPE cable through the high-frequency microwave signal, and effective evaluation of metal residues in the cable can be realized on the premise that the structure of the cable is not damaged.

Description

Technical Field

[0001] The present application relates to the technical field of 10kV XLPE cable detection and evaluation, and in particular to a method for evaluating the size of metal residues in 10kV XLPE cables. Background Art

[0002] Cross-linked polyethylene (XLPE) insulated cables are widely used in various fields, including power, infrastructure, and transportation, due to their excellent electrical properties, heat resistance, and chemical stability. In recent years, with the continued development of related industries, market demand for XLPE cables has maintained steady growth, and is expected to continue to expand steadily in the future.

[0003] As the scope of applications continues to expand, the requirements for cable quality are also increasing. However, during the actual production process, XLPE cables may contain metallic foreign matter due to impure raw materials, improper process control, or equipment and operational issues. This metallic residue can become a source of electric field distortion, inducing problems such as partial discharge, short circuits, and leakage, ultimately degrading the cable's insulation performance and affecting its long-term operational reliability. Therefore, how to efficiently and accurately detect potential metallic residue defects within cables has become a critical technical issue to ensure cable quality and the safe operation of power systems. Summary of the Invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical deficiencies, especially the technical deficiency in the prior art of how to efficiently and accurately detect metal residual defects that may exist inside the cable.

[0005] In a first aspect, the present application provides a method for evaluating the size of metal residue in a 10kV XLPE cable, the method comprising:

[0006] For the 10kV XLPE cable to be evaluated, a microwave signal with a frequency range of 22GHz to 30GHz is radially injected. Based on the acquired microwave reflection curve, multiple microwave reflection characteristic frequencies and their corresponding amplitudes are extracted.

[0007] Calculate the amplitude factor corresponding to each microwave reflection characteristic frequency according to each microwave reflection characteristic frequency and its corresponding amplitude;

[0008] Calculate the metal residual factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency;

[0009] According to the metal residue factor, the size of the metal residue defect of the cable to be evaluated is evaluated.

[0010] In one embodiment, the step of extracting a plurality of microwave reflection characteristic frequencies and their corresponding amplitudes based on the acquired microwave reflection curve includes:

[0011] The frequency band of microwave signals is divided into low frequency band, medium frequency band and high frequency band. The low frequency band is 22GHz~24GHz, the medium frequency band is 24GHz~28GHz, and the high frequency band is 28GHz~30GHz.

[0012] In the microwave reflection curve, the frequencies and amplitudes corresponding to the peak maxima in the low frequency band, the medium frequency band and the high frequency band are extracted respectively to obtain multiple microwave reflection characteristic frequencies and amplitudes.

[0013] In one embodiment, the step of calculating the amplitude factor corresponding to each microwave reflection characteristic frequency according to each microwave reflection characteristic frequency and its corresponding amplitude includes:

[0014] The amplitude factor corresponding to each microwave reflection characteristic frequency is calculated according to the following expression:

[0015]

[0016] in, Indicates the The amplitude factor corresponding to the characteristic frequency of microwave reflection, Indicates the The amplitude corresponding to the characteristic frequency of microwave reflection, Indicates the The microwave reflection characteristic frequency, Indicates the The frequency weight of each microwave reflection characteristic frequency.

[0017] In one embodiment, the frequency weight of each microwave reflection characteristic frequency is expressed as follows:

[0018]

[0019] in, Indicates the The frequency weight of the microwave reflection characteristic frequency, Indicates the The microwave reflection characteristic frequency.

[0020] In one embodiment, the step of calculating the metal residue factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency includes:

[0021] The metal residue factor is calculated as follows:

[0022]

[0023] in, represents the metal residual factor, Indicates the The amplitude factor corresponding to the characteristic frequency of microwave reflection, Indicates the The amplitude corresponding to the characteristic frequency of microwave reflection, represents the outer radius of the cable insulation of the cable to be evaluated, represents the cable conductor radius of the cable to be evaluated, represents the nonlinear factor, Represents the relative dielectric constant of an insulating material.

[0024] In one embodiment, the step of evaluating the size of the metal residual defect of the cable to be evaluated according to the metal residual factor includes:

[0025] If the metal residue factor is greater than zero and not greater than the first preset threshold, the metal residue defect of the cable to be evaluated is small;

[0026] If the metal residue factor is greater than the first preset threshold value and not greater than the second preset threshold value, the metal residue defect of the cable to be evaluated is medium;

[0027] If the metal residue factor is greater than the second preset threshold value and is not greater than 1, the metal residue defect of the cable to be evaluated is relatively large.

[0028] In one embodiment, the first preset threshold is 0.359807, and the second preset threshold is 0.371104.

[0029] In a second aspect, the present application provides a 10kV XLPE cable metal residue size assessment device, the device comprising:

[0030] The microwave reflection curve acquisition module is used to radially inject microwave signals with a frequency range of 22 GHz to 30 GHz into the 10 kV XLPE cable to be evaluated, and extract multiple microwave reflection characteristic frequencies and their corresponding amplitudes based on the acquired microwave reflection curve;

[0031] An amplitude factor calculation module is used to calculate the amplitude factor corresponding to each microwave reflection characteristic frequency based on each microwave reflection characteristic frequency and its corresponding amplitude;

[0032] A metal residual factor calculation module is used to calculate the metal residual factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency;

[0033] The metal residual defect size assessment module is used to assess the size of the metal residual defects of the cable to be assessed based on the metal residual factor.

[0034] In a third aspect, the present application provides a storage medium: the storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the 10kV XLPE cable metal residue size evaluation method as described in any of the above embodiments.

[0035] In a fourth aspect, the present application provides a computer device, comprising: one or more processors, and a memory;

[0036] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the steps of the method for evaluating the size of metal residues in a 10 kV XLPE cable in any one of the above embodiments are performed.

[0037] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0038] The method for assessing the size of metal residue in 10kV XLPE cables, provided in this application, uses high-frequency microwave signals to perform non-contact inspection of 10kV XLPE cables. This method effectively assesses the metal residue within the cable without damaging the cable structure. Specifically, by injecting microwave signals in the 22GHz to 30GHz frequency range into the cable, the sensitivity to detecting subtle metal foreign matter can be increased, helping to enhance the perception of high-frequency response changes, thereby improving the defect detection rate. By acquiring microwave reflection curves and extracting multiple characteristic frequencies and their amplitudes, the response characteristics of the cable's internal structure to electromagnetic waves can be more comprehensively characterized, enhancing the precision and stability of detection. Furthermore, the calculation of an amplitude factor helps normalize the amplitude response differences at different characteristic frequencies, thereby improving the distinction between the metal foreign matter signal and background noise. Furthermore, by combining the amplitude and amplitude factor to calculate the metal residue factor, multiple local reflection features can be integrated into a unified indicator, enhancing the ability to characterize the overall impact of metal residue. Finally, assessing defect size based on the metal residue factor facilitates quantitative defect characterization, improving the accuracy, reliability, and engineering applicability of cable detection, and possessing significant technical value and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 A schematic diagram of a process for evaluating the residual metal size of a 10kV XLPE cable provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of the structure of a 10kV XLPE cable metal residue size assessment device provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0044] This application provides a method for evaluating the size of metal residues in 10kV XLPE cables. The following embodiments illustrate this method by applying it to a computer device. It is understood that the computer device can be any device with data processing capabilities, including but not limited to a single server, a server cluster, a personal laptop computer, a desktop computer, etc. Figure 1 As shown, the method may include the following steps:

[0045] S101: For the 10kV XLPE cable to be evaluated, a microwave signal with a frequency range of 22GHz to 30GHz is radially injected. Based on the acquired microwave reflection curve, multiple microwave reflection characteristic frequencies and their corresponding amplitudes are extracted.

[0046] The cables under evaluation are 10kV cross-linked polyethylene (XLPE) insulated power cables that have not yet been inspected for residual metal defects. These cables, being evaluated, may contain metallic foreign matter introduced by manufacturing defects. Radial injection involves introducing microwave signals into the cable dielectric in the radial direction, perpendicular to the cable's longitudinal axis, to penetrate the insulation and reflect and scatter from the internal structure. Microwave signals in the 22 GHz to 30 GHz frequency range are continuous or pulsed electromagnetic waves with high penetration and resolution. They are used to stimulate the cable structure to produce an electromagnetic response that can be used for feature extraction. A microwave reflection curve is a frequency-domain plot of the reflection coefficient versus frequency, obtained by collecting the cable's response signal. It reflects the cable's reflection characteristics for microwaves of different frequencies. Microwave reflection characteristic frequencies are frequencies corresponding to local peaks or significant fluctuations on the reflection curve. These frequencies typically exhibit abnormal shifts or enhancements when affected by metallic foreign matter. Amplitude refers to the amplitude of the microwave reflection signal at each characteristic frequency point, reflecting the reflection intensity at that frequency.

[0047] Specifically, the computer equipment works in conjunction with a microwave signal transmitter to stimulate the 10kV XLPE cable under evaluation. Specifically, the computer equipment controls the signal source module to emit a continuous or step-scan microwave signal in the frequency range of 22GHz to 30GHz. Through connected microwave guide structures, such as coaxial cables and waveguide adapters, the microwave energy is radially injected into the cable surface. The microwave energy penetrates the cable insulation in a direction perpendicular to the cable's axis and interacts with the internal dielectric or any metallic foreign matter, generating a reflected signal.

[0048] Next, a computer uses a vector network analyzer or a reflected wave receiving unit to collect the reflected signal returning from the cable and convert it into a digital signal for processing. The computer then transforms the raw reflected signal into the frequency domain, generating a microwave reflection curve showing how the reflection coefficient varies with frequency. This curve illustrates the response of the cable structure to different excitation frequencies. In particular, if metal residue is present within the cable, the reflection characteristics at certain frequencies may be enhanced or distorted.

[0049] The computer then identifies characteristic points on the reflection curve based on a pre-defined feature extraction algorithm. This algorithm may include first-order derivative analysis, peak detection, local extrema determination, or adaptive threshold extraction to locate characteristic frequency points on the curve with significant changes in reflection intensity. For each identified characteristic frequency point, the computer further extracts the corresponding reflection signal amplitude and records the frequency-amplitude pair. To ensure data quality, a noise filtering module can be configured to remove spurious peaks caused by system background noise or edge effects.

[0050] As can be understood, radial injection of 22 GHz to 30 GHz microwave signals not only avoids damage to the cable structure, but also, due to the strong penetration and high sensitivity of microwaves in this frequency band to metallic media, enables the acquisition of reflection characteristics without contacting the cable core, thus effectively exciting and identifying metallic foreign objects. By extracting characteristic frequencies and amplitudes from frequency-domain reflection curves, the signal's frequency response information can be converted into structural defect characterization information, improving the ability to detect local anomalies.

[0051] S102: Calculating an amplitude factor corresponding to each microwave reflection characteristic frequency according to each microwave reflection characteristic frequency and its corresponding amplitude.

[0052] Among them, the amplitude factor refers to the reflection amplitude parameter that has been normalized or standardized in some way. It is used to enhance the comparability of signal amplitudes at different characteristic frequencies and weaken systematic deviations caused by background conditions, equipment sensitivity or changes in reflection paths, thereby more accurately reflecting the actual impact of possible metal residues inside the cable on microwave reflection.

[0053] Specifically, the computer device can normalize the amplitude corresponding to each frequency based on a preset amplitude factor calculation model. Specific methods may include, but are not limited to, using the maximum amplitude normalization method to divide all amplitudes by the maximum reflection amplitude in the current frequency band, thereby mapping the amplitude range to the [0, 1] interval; or using the baseline ratio method to compare each amplitude with the theoretical defect-free response amplitude at the corresponding frequency to form an offset coefficient; or using the noise threshold filter weighting method to deduct the background noise baseline from the amplitude and then correct it according to the noise weight.

[0054] Each calculated amplitude factor is then bound to its corresponding microwave reflection characteristic frequency, forming a frequency-amplitude factor pair. This pair is then stored as structured data in a subsequent processing module. The computer can then further assess the rationality of the amplitude factor results, such as whether they exceed the normal response range or contain abnormal spikes, ensuring data quality and avoiding subsequent errors due to false detection.

[0055] It can be understood that by calculating the amplitude factor corresponding to each microwave reflection characteristic frequency, the original reflection amplitude data can be converted into a standardized quantitative indicator reflecting the degree of defect, thereby effectively improving the comparability of data at different frequencies, reducing the impact of equipment errors and background interference, and enhancing the sensitivity to tiny metal residue responses.

[0056] S103: Calculate the metal residual factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency.

[0057] Among them, the metal residue factor refers to an indicator obtained by comprehensively calculating the amplitude and amplitude factor of a set of reflection characteristic frequencies, which is used to quantify the comprehensive reflection of the degree of metal foreign matter residue in the cable to be evaluated. The larger the factor, the more serious the interference of metal foreign matter on the electric field distribution of the cable dielectric, thereby affecting its insulation performance.

[0058] Specifically, the computer device obtains the amplitudes and amplitude factors corresponding to multiple microwave reflection characteristic frequencies and inputs this data into the data processing module in a structured format. Each frequency point forms a complete set of data items, including the frequency value, the corresponding amplitude, and the normalized amplitude factor, forming the basic unit for subsequent calculations.

[0059] Secondly, the computer device executes the comprehensive calculation logic of the metal residue factor. In one implementation, the system uses a weighted summation model to fuse the amplitudes and amplitude factors of all frequency points, for example: ,in, Indicates the The amplitude of the characteristic frequency point, represents the amplitude factor of the point, This parameter represents an optional factor used to increase the weight of specific frequencies. You can configure different weighting strategies based on different cable types or detection scenarios, such as assigning higher weights to frequency bands known to be susceptible to metal interference.

[0060] It can be understood that by combining amplitude and amplitude factor for weighted fusion calculation, the microwave response strength and characteristic sensitivity of the cable at different frequency bands can be comprehensively reflected, improving the overall robustness and accuracy of metal residue assessment. This calculation method can effectively reduce interference factors caused by equipment deviation, background noise, or environmental changes while preserving key reflection characteristics, thereby improving the detection sensitivity of weak metal foreign body responses.

[0061] S104: Evaluate the size of the metal residual defect of the cable to be evaluated based on the metal residual factor.

[0062] Among them, the size of metal residual defects refers to the degree of defects determined based on the numerical results of the metal residual factor, which is used to indicate the scale of the presence of metal foreign matter in the cable. Its size is usually positively correlated with the risk of degradation of the cable insulation performance.

[0063] Specifically, the computer receives or calls the metal residue factor data and uses this value as an input parameter for the defect assessment module. This module is pre-configured with multiple assessment rules or classification models, and can achieve automatic judgment using methods such as threshold segmentation, fuzzy logic judgment, or machine learning models.

[0064] In one implementation, the computer compares the metal residue factor value with multiple preset thresholds. These thresholds are established based on extensive field-measured data and cable failure statistics to distinguish different levels of metal residue defects. Furthermore, to enhance the adaptability of the assessment, the computer can dynamically adjust the parameters of the assessment model or train classifier models, such as support vector machines, decision trees, and neural networks, based on different cable types, operating environments, or historical failure data, to more accurately match the assessment requirements in specific scenarios. By integrating multiple models or policy rules, adaptive assessment capabilities can be implemented for different operating conditions.

[0065] It can be understood that by grading the defect size based on the metal residue factor, the abstract microwave reflection characteristics can be converted into intuitive and quantifiable judgment results, effectively improving the interpretability and engineering practicality of the detection results, making it easier for on-site technicians or remote monitoring systems to make accurate decisions on the cable status and further optimize the allocation of operation and maintenance resources.

[0066] In the above-described embodiment, non-contact inspection of 10kV XLPE cables using high-frequency microwave signals enables effective assessment of metal residue within the cable without damaging the cable structure. Specifically, injecting microwave signals in the 22GHz to 30GHz frequency range into the cable improves the detection sensitivity of subtle metallic foreign matter, enhancing the ability to perceive high-frequency response changes, and thus improving the defect detection rate. By acquiring microwave reflection curves and extracting multiple characteristic frequencies and their amplitudes, the response characteristics of the cable's internal structure to electromagnetic waves can be more comprehensively characterized, enhancing the precision and stability of detection. Furthermore, the inclusion of amplitude factor calculation helps normalize the amplitude response differences at different characteristic frequencies, thereby improving the distinction between metallic foreign matter signals and background noise. Furthermore, by combining the amplitude and amplitude factor to calculate the metal residue factor, multiple local reflection features can be integrated into a unified indicator, enhancing the ability to characterize the overall impact of metal residue. Finally, assessing defect size based on the metal residue factor facilitates quantitative defect characterization, improving the accuracy, reliability, and engineering applicability of cable inspection, and possessing significant technical value and application prospects.

[0067] In one embodiment, the step of extracting a plurality of microwave reflection characteristic frequencies and their corresponding amplitudes based on the acquired microwave reflection curve includes:

[0068] The frequency band of microwave signals is divided into low frequency band, medium frequency band and high frequency band. The low frequency band is 22GHz~24GHz, the medium frequency band is 24GHz~28GHz, and the high frequency band is 28GHz~30GHz.

[0069] In the microwave reflection curve, the frequencies and amplitudes corresponding to the peak maxima in the low frequency band, the medium frequency band and the high frequency band are extracted respectively to obtain multiple microwave reflection characteristic frequencies and amplitudes.

[0070] Specifically, the computer receives complete microwave reflection curve data from the signal processing module, covering the frequency range of 22 GHz to 30 GHz. The computer divides this frequency range into three sub-bands based on preset rules: a low-frequency band of 22 GHz to 24 GHz, a mid-frequency band of 24 GHz to 28 GHz, and a high-frequency band of 28 GHz to 30 GHz. This division allows the original reflection data sequence to be segmented by setting frequency boundary indexes, enabling rapid location of data within the frequency band.

[0071] Secondly, for each sub-band, the computer equipment performs a peak search operation in turn. Specifically, a local extreme value detection algorithm is used to scan the reflection amplitude data within the frequency band, find all local peak points, and on this basis determine the peak point with the largest amplitude. To ensure the authenticity and stability of the peak, a noise threshold filtering strategy can be combined to eliminate the influence of pseudo-peaks or edge effects, and the sliding window width can be set to avoid overly dense local peak responses. Next, the frequency value and reflection amplitude corresponding to the maximum peak point are recorded to form a frequency-amplitude pair, which respectively represent the most significant microwave reflection characteristics in the frequency band.

[0072] It can be understood that by dividing the complete microwave frequency band into low-frequency band, medium-frequency band and high-frequency band, the local resolution of the reflection curve analysis can be effectively improved, making the feature extraction in each sub-band more targeted, and avoiding the weakening of features or interference superposition caused by full-band analysis. Extracting the maximum peak frequency and its amplitude in each frequency band helps to capture the abnormal reflection performance caused by the microwave response of metal foreign matter in different frequency ranges, and enhance the multi-dimensional expression ability of defect characteristics. In addition, this segmented processing method simplifies the complexity of signal processing, improves the operating efficiency and stability of the algorithm, and provides basic data with clear structure and distinct levels for subsequent defect modeling, thereby significantly improving the accuracy and robustness of metal residue detection.

[0073] In one embodiment, the step of calculating the amplitude factor corresponding to each microwave reflection characteristic frequency according to each microwave reflection characteristic frequency and its corresponding amplitude includes:

[0074] The amplitude factor corresponding to each microwave reflection characteristic frequency is calculated according to the following expression:

[0075]

[0076] in, Indicates the The amplitude factor corresponding to the characteristic frequency of microwave reflection, Indicates the The amplitude corresponding to the characteristic frequency of microwave reflection, Indicates the The microwave reflection characteristic frequency, Indicates the The frequency weight of each microwave reflection characteristic frequency.

[0077] In this embodiment, by multiplying the amplitude and frequency weight, it is possible to achieve weighted enhancement or suppression of the reflected signal in a specific frequency band, thereby strengthening the response characteristics of frequency points that are physically more sensitive or more easily interfered with by metal foreign matter. This processing method not only retains the intensity information provided by the original amplitude, but also introduces the recognition value of the frequency dimension, making the calculation results more targeted and discriminative. At the same time, it improves the distinguishing ability and stability of the amplitude factor, effectively reduces the excessive response to irrelevant frequency responses, and thus enhances the robustness and accuracy of the subsequent metal residual factor calculation. In addition, by reasonably setting the weight function It can flexibly adapt to the characteristic response rules of different cable models and different application scenarios, thereby improving the adaptability, scalability and engineering practicality of the entire detection method.

[0078] In one embodiment, the frequency weight of each microwave reflection characteristic frequency is expressed as follows:

[0079]

[0080] in, Indicates the The frequency weight of the microwave reflection characteristic frequency, Indicates the The microwave reflection characteristic frequency.

[0081] In this embodiment, by normalizing the microwave reflection characteristic frequency and the sum of all characteristic frequencies, the characteristic frequency corresponding to the high-frequency part can be given a higher weight in the amplitude factor. Since high-frequency microwave signals are more sensitive to tiny metal foreign matter inside the cable and have a stronger reflection response, the use of this weight expression can effectively enhance the high-frequency band's response to defects and improve the detection accuracy of early, fine metal residues. At the same time, the formula is concise and easy to calculate, making it easy to deploy quickly; its normalization characteristics ensure the consistency and comparability of calculations under different frequency distribution conditions, avoiding the imbalance of results caused by differences in frequency values. In addition, through dynamic calculation rather than fixed weight setting, this method has stronger adaptability and can maintain the stability and versatility of detection results under different cable structures or different scenarios, thereby significantly improving the engineering practicality and intelligence level of the entire detection system.

[0082] In one embodiment, the step of calculating the metal residue factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency includes:

[0083] The metal residue factor is calculated as follows:

[0084]

[0085] in, represents the metal residual factor, Indicates the The amplitude factor corresponding to the characteristic frequency of microwave reflection, Indicates the The amplitude corresponding to the characteristic frequency of microwave reflection, represents the outer radius of the cable insulation of the cable to be evaluated, represents the cable conductor radius of the cable to be evaluated, represents the nonlinear factor, Represents the relative dielectric constant of an insulating material.

[0086] In this embodiment, a nonlinear fusion of the three amplitude factors in the numerator significantly amplifies abnormal feature points with high reflection intensity and high frequency weighting compared to simple linear weighting. This increases sensitivity to abnormal responses of localized metal residue in the cable and improves detection accuracy. The introduction of nonlinear factors also grants the system adjustability, facilitating dynamic control of response characteristics based on different detection requirements. Secondly, the denominator incorporates cable geometry parameters and their squared terms to normalize structural differences between cables of different sizes, avoiding systematic errors caused by varying cable thickness. Furthermore, by combining the relative dielectric constant of the insulating medium, it further compensates for differences in electromagnetic response characteristics between different cable materials, making the calculation of the metal residue factor more physically realistic and comparable to engineering applications. Finally, by summing the original amplitudes and introducing them into the denominator, the overall signal strength level can be adjusted, preventing defect detection from being misled by large background responses and improving the relative resolution of the residue factor.

[0087] In summary, this calculation formula fully introduces multiple factors such as frequency weighting, structural normalization and material compensation on the basis of retaining the reflection characteristics. It can accurately characterize the degree of disturbance of the cable microwave response caused by metal foreign matter, and has good stability, sensitivity and adaptability, thereby significantly improving the detection reliability and quantification ability of metal residual defects, and meeting the detection needs of multiple types and specifications of cables in actual engineering.

[0088] In one embodiment, the step of evaluating the size of the metal residual defect of the cable to be evaluated according to the metal residual factor includes:

[0089] If the metal residue factor is greater than zero and not greater than the first preset threshold, the metal residue defect of the cable to be evaluated is small;

[0090] If the metal residue factor is greater than the first preset threshold value and not greater than the second preset threshold value, the metal residue defect of the cable to be evaluated is medium;

[0091] If the metal residue factor is greater than the second preset threshold value and is not greater than 1, the metal residue defect of the cable to be evaluated is relatively large.

[0092] Among them, the first preset threshold and the second preset threshold are pre-set limit values ​​for judging the degree of metal residue, which are obtained based on a large amount of historical detection data and actual failure case statistics, and correspond to the limits of small and medium defect levels respectively.

[0093] Specifically, the computer device obtains the calculated metal residue factor from the previous module and inputs it into the defect level assessment module. This module is pre-configured with two threshold parameters: a first preset threshold and a second preset threshold. These values ​​can be manually set based on engineering experience or automatically generated by the system based on labeled samples during the training phase.

[0094] Subsequently, conditional judgment logic is used to classify the metal residue factor values ​​into intervals. Specifically, when the metal residue factor satisfies the condition of being greater than 0 but not greater than the first preset threshold, the metal residue defect is judged to be small, indicating only a slight anomaly in microwave reflection that does not affect the stability of cable operation. When the metal residue factor is between the first and second preset thresholds, the output defect is medium, indicating that the cable has a certain degree of metal foreign matter interference, and continuous observation or increased inspections are recommended. When the metal residue factor exceeds the second preset threshold but does not exceed 1, it is judged to be a large defect, indicating a significant microwave reflection anomaly, and it is recommended to immediately check or replace the cable to prevent potential breakdown or partial discharge risks.

[0095] In one embodiment, the first preset threshold is 0.359807, and the second preset threshold is 0.371104. These two values ​​are used to divide the metal residue factor β into different evaluation intervals, enabling quantitative assessment of the degree of metal residue defects in cables. These thresholds can be determined based on a large amount of historical sample data through statistical analysis or machine learning methods, or they can be set based on experimental test results and engineering experience, with clear physical meaning and application context. When the metal residue factor is no greater than 0.359807, it indicates that metal residue interferes minimally with microwave reflection, and the cable is in a normal or slightly defective state. When the metal residue factor is between 0.359807 and 0.371104, the metal interference effect is gradually increasing, and continuous monitoring is recommended. When the metal residue factor exceeds 0.371104, it is considered that significant metal foreign matter is present in the cable, posing a high risk of failure and requiring attention. This grading approach not only improves the sensitivity and interpretability of detection results but also facilitates differentiated management of defect handling, thereby enhancing the practicality and safety of the detection system.

[0096] It can be understood that by segmenting the continuous metal residue factor values ​​into three levels of "small", "medium" or "large", it can not only improve the interpretability and practical operability of the detection results, so that technical personnel can quickly understand the cable status, but also facilitate the system to realize automatic screening and intelligent grading in large-scale monitoring. The introduction of two preset thresholds to achieve multi-level classification judgment helps to improve the accuracy and flexibility of risk identification, so that the cable management strategy can achieve differentiated treatment according to different defect degrees and improve resource allocation efficiency. At the same time, the implementation method based on judgment logic is simple and clear, suitable for embedded deployment or edge computing platform integration, thereby enhancing the engineering adaptability and deployment efficiency of the system. In summary, this embodiment significantly improves the practicality, intelligence level and safety assurance capability of the cable metal residue detection system.

[0097] The following describes the 10kV XLPE cable metal residue size evaluation device provided by the embodiment of the present application. The 10kV XLPE cable metal residue size evaluation device described below and the 10kV XLPE cable metal residue size evaluation method described above can be referenced to each other. Figure 2 As shown, the present application provides a 10kV XLPE cable metal residue size assessment device, the device comprising:

[0098] The microwave reflection curve acquisition module 201 is used to radially inject a microwave signal with a frequency range of 22 GHz to 30 GHz into the 10 kV XLPE cable to be evaluated, and extract multiple microwave reflection characteristic frequencies and their corresponding amplitudes based on the acquired microwave reflection curve;

[0099] An amplitude factor calculation module 202 is configured to calculate an amplitude factor corresponding to each microwave reflection characteristic frequency based on each microwave reflection characteristic frequency and its corresponding amplitude;

[0100] The metal residual factor calculation module 203 is used to calculate the metal residual factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency;

[0101] The metal residual defect size evaluation module 204 is used to evaluate the size of the metal residual defect of the cable to be evaluated based on the metal residual factor.

[0102] In one embodiment, the microwave reflection curve acquisition module 201 includes:

[0103] A frequency band division unit is used to divide the frequency band of microwave signals into low frequency band, medium frequency band and high frequency band, wherein the low frequency band is 22 GHz to 24 GHz, the medium frequency band is 24 GHz to 28 GHz, and the high frequency band is 28 GHz to 30 GHz;

[0104] The microwave reflection characteristic frequency determination unit is used to extract the frequencies and amplitudes corresponding to the peak maxima in the low frequency band, the medium frequency band and the high frequency band in the microwave reflection curve, and obtain multiple microwave reflection characteristic frequencies and amplitudes.

[0105] In one embodiment, the amplitude factor calculation module 202 includes:

[0106] The amplitude factor calculation unit is used to calculate the amplitude factor corresponding to each microwave reflection characteristic frequency according to the following expression:

[0107]

[0108] in, Indicates the The amplitude factor corresponding to the characteristic frequency of microwave reflection, Indicates the The amplitude corresponding to the characteristic frequency of microwave reflection, Indicates the The microwave reflection characteristic frequency, Indicates the The frequency weight of each microwave reflection characteristic frequency.

[0109] In one embodiment, the frequency weight of each microwave reflection characteristic frequency is expressed as follows:

[0110]

[0111] in, Indicates the The frequency weight of the microwave reflection characteristic frequency, Indicates the The microwave reflection characteristic frequency.

[0112] In one embodiment, the metal residue factor calculation module 203 includes:

[0113] The metal residual factor calculation unit is used to calculate the metal residual factor according to the following expression:

[0114]

[0115] in, represents the metal residual factor, Indicates the The amplitude factor corresponding to the characteristic frequency of microwave reflection, Indicates the The amplitude corresponding to the characteristic frequency of microwave reflection, represents the outer radius of the cable insulation of the cable to be evaluated, represents the cable conductor radius of the cable to be evaluated, represents the nonlinear factor, Represents the relative dielectric constant of an insulating material.

[0116] In one embodiment, the metal residual defect size assessment module 204 includes:

[0117] a first metal residual defect size evaluation unit, configured to determine that the metal residual defect of the cable to be evaluated is small if the metal residual factor is greater than zero and not greater than a first preset threshold;

[0118] a second metal residual defect size evaluation unit, configured to determine that the metal residual defect of the cable to be evaluated is medium if the metal residual factor is greater than a first preset threshold value and not greater than a second preset threshold value;

[0119] The third metal residual defect size evaluation unit is used to determine that if the metal residual factor is greater than a second preset threshold and not greater than 1, the metal residual defect of the cable to be evaluated is large.

[0120] In one embodiment, the first preset threshold is 0.359807, and the second preset threshold is 0.371104.

[0121] In one embodiment, the present application further provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the 10kV XLPE cable metal residue size assessment method as described in any of the above embodiments.

[0122] In one embodiment, the present application further provides a computer device having computer-readable instructions stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the 10kV XLPE cable metal residue size assessment method as described in any one of the above embodiments.

[0123] Schematically, as Figure 3 As shown, Figure 3 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 can be provided as a server. Figure 3 Computer device 300 includes a processing component 302, which further includes one or more processors and memory resources represented by memory 301 for storing instructions executable by processing component 302, such as application programs. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, processing component 302 is configured to execute the instructions to perform the method for assessing the size of metal residue in a 10 kV XLPE cable according to any of the aforementioned embodiments.

[0124] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or the like.

[0125] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0126] Finally, it should be noted that, in this article, relational terms such as first and second are merely used to distinguish one entity or operation from another, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprise," "include," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. Without further restriction, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element. Herein, "one," "said," "the," and "its" may also include plural forms unless the context clearly indicates otherwise. A plurality refers to at least two, such as 2, 3, 5, or 8. "And / or" includes any and all combinations of the relevant listed items.

[0127] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0128] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the residual metal size of a 10kV XLPE cable, characterized in that: The method comprises: For the 10kV XLPE cable to be evaluated, a microwave signal with a frequency range of 22GHz to 30GHz is radially injected. Based on the acquired microwave reflection curve, multiple microwave reflection characteristic frequencies and their corresponding amplitudes are extracted. Calculating an amplitude factor corresponding to each microwave reflection characteristic frequency according to each microwave reflection characteristic frequency and its corresponding amplitude; Calculating the metal residual factor of the cable to be evaluated according to the amplitude and amplitude factor corresponding to each of the microwave reflection characteristic frequencies; The size of the metal residual defect of the cable to be evaluated is evaluated according to the metal residual factor.

2. The method for evaluating the residual metal size of a 10kV XLPE cable according to claim 1, wherein: The step of extracting a plurality of microwave reflection characteristic frequencies and their corresponding amplitudes based on the acquired microwave reflection curve includes: The frequency band of the microwave signal is divided into a low frequency band, a medium frequency band and a high frequency band, wherein the low frequency band is 22 GHz to 24 GHz, the medium frequency band is 24 GHz to 28 GHz, and the high frequency band is 28 GHz to 30 GHz; In the microwave reflection curve, the frequencies and amplitudes corresponding to the peak values ​​in the low frequency band, the medium frequency band and the high frequency band are extracted respectively to obtain a plurality of microwave reflection characteristic frequencies and amplitudes.

3. The method for evaluating the residual metal size of a 10kV XLPE cable according to claim 1, wherein: The step of calculating the amplitude factor corresponding to each microwave reflection characteristic frequency according to each microwave reflection characteristic frequency and its corresponding amplitude includes: The amplitude factor corresponding to each microwave reflection characteristic frequency is calculated according to the following expression: in, Indicates the The amplitude factor corresponding to the microwave reflection characteristic frequency, Indicates the The amplitude corresponding to the characteristic frequency of microwave reflection, Indicates the The microwave reflection characteristic frequency, Indicates the The frequency weight of each of the microwave reflection characteristic frequencies.

4. The method for evaluating the residual metal size of a 10kV XLPE cable according to claim 3, wherein: The frequency weight of each microwave reflection characteristic frequency is expressed as follows: in, Indicates the The frequency weight of each of the microwave reflection characteristic frequencies, Indicates the The microwave reflection characteristic frequency.

5. The method for evaluating the residual metal size of a 10kV XLPE cable according to claim 1, wherein: The step of calculating the metal residual factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each of the microwave reflection characteristic frequencies comprises: The metal residue factor is calculated according to the following expression: in, represents the metal residue factor, Indicates the The amplitude factor corresponding to the microwave reflection characteristic frequency, Indicates the The amplitude corresponding to the characteristic frequency of microwave reflection, represents the outer radius of the cable insulation layer of the cable to be evaluated, represents the cable conductor radius of the cable to be evaluated, represents the nonlinear factor, Represents the relative dielectric constant of an insulating material.

6. The method for evaluating the residual metal size of a 10kV XLPE cable according to claim 1, wherein: The step of evaluating the size of the metal residual defect of the cable to be evaluated according to the metal residual factor comprises: If the metal residue factor is greater than zero and not greater than a first preset threshold, the metal residue defect of the cable to be evaluated is small; If the metal residue factor is greater than the first preset threshold value and not greater than the second preset threshold value, the metal residue defect of the cable to be evaluated is medium; If the metal residue factor is greater than the second preset threshold value and is not greater than 1, the metal residue defect of the cable to be evaluated is relatively large.

7. The method for evaluating the residual metal size of a 10kV XLPE cable according to claim 6, wherein: The first preset threshold is 0.359807, and the second preset threshold is 0.371104.

8. A 10kV XLPE cable metal residue size assessment device, characterized in that: The device comprises: The microwave reflection curve acquisition module is used to radially inject microwave signals with a frequency range of 22 GHz to 30 GHz into the 10 kV XLPE cable to be evaluated, and extract multiple microwave reflection characteristic frequencies and their corresponding amplitudes based on the acquired microwave reflection curve; an amplitude factor calculation module, configured to calculate an amplitude factor corresponding to each of the microwave reflection characteristic frequencies according to each of the microwave reflection characteristic frequencies and its corresponding amplitude; a metal residual factor calculation module, configured to calculate the metal residual factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each of the microwave reflection characteristic frequencies; The metal residual defect size evaluation module is used to evaluate the metal residual defect size of the cable to be evaluated based on the metal residual factor.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, which, when executed by one or more processors, enable the one or more processors to perform the steps of the method for evaluating the size of metal residues in a 10 kV XLPE cable as claimed in any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the method for evaluating the size of metal residues in a 10 kV XLPE cable according to any one of claims 1 to 7.

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