Device and method for detecting insulativity of cable shell by using electromagnetic interference

Abstract

The invention discloses a cable shell insulativity detection device and method using electromagnetic interference, and relates to the technical field of cable detection, and the method comprises the steps: building a cable distribution line model, simulating and adjusting parameter mining signal characteristics, configuring a front-end detection strategy based on the characteristics, monitoring and training an insulation detection unit, and integrating a signal processing unit into a module. Modules are used for processing echo signals, positioning insulation defects, generating detection lists and visualizing the detection lists on a distribution line model, terminal interface display and early warning are carried out, and efficient insulation monitoring is achieved. The technical problems that existing cable insulation detection depends on partial discharge detection or insulation resistance measurement, is prone to being interfered by external electromagnetic signals and is low in detection efficiency and accuracy are solved, and the cable insulation detection efficiency and accuracy are improved through analysis of cable signal characteristics and a directional automation strategy. And the efficiency and accuracy of cable insulation defect detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cable detection, and in particular to a device and method for detecting the insulation of a cable casing using electromagnetic interference. Background Art

[0002] With the continuous development of modern industry, cables, as the core components of power transmission, have received more and more attention for their safety and stability. Especially in the application of high-voltage cables and long-distance transmission cables, the insulation of cables is directly related to the safe operation of the power system.

[0003] Most existing cable insulation detection methods rely on partial discharge detection technology or insulation resistance measurement. In complex environments (such as high electromagnetic interference scenarios), it is often difficult to quickly and effectively detect early defects in the cable insulation layer, resulting in echo signal distortion and affecting the accuracy of the detection results. Summary of the invention

[0004] The present application provides a cable casing insulation detection device and method using electromagnetic interference, which is used to solve the technical problems that the existing cable insulation detection relies on partial discharge detection or insulation resistance measurement, is easily interfered by external electromagnetic signals, and has low detection efficiency and accuracy.

[0005] The first aspect of the present application provides a method for detecting the insulation of a cable casing using electromagnetic interference, the method comprising: building a distributed line model of the cable, wherein the distributed line model includes a cable part and an environmental field part; based on the distributed line model, simulating by adjusting the distribution parameters, mining a first characteristic and a second characteristic, wherein the first characteristic is the dynamic characteristic and transmission characteristic of the line signal, and the second characteristic is the signal coexistence characteristic of pulsed electromagnetic interference and partial discharge; based on the first characteristic and the second characteristic, configuring a front-end detection strategy, the front-end detection strategy is a directional automation strategy based on detection reception; based on the first characteristic and the second characteristic, supervising the training of an insulation detection unit, and pre-positioning a signal processing unit to construct an insulation detection module; based on the front-end detection strategy, automatically configuring the front-end detector, performing signal preprocessing and insulation defect positioning on the received echo signal based on the insulation detection module, and determining an insulation detection list; visualizing the insulation detection list on the distributed line model, and performing terminal interface display and early warning.

[0006] According to a second aspect of the present application, a cable casing insulation detection device using electromagnetic interference is provided, the device comprising: a distributed line model building module, used to build a distributed line model of the cable, wherein the distributed line model includes a cable part and an environmental field part; a signal feature mining module, used to simulate and mine a first characteristic and a second characteristic by adjusting a distributed parameter based on the distributed line model, wherein the first characteristic is a dynamic characteristic and a transmission characteristic of a line signal, and the second characteristic is a signal coexistence characteristic of a pulsed electromagnetic interference and a partial discharge; a front-end detection strategy configuration module, used to configure a front-end detection strategy based on the first characteristic and the second characteristic, wherein the front-end detection strategy is a directional automation strategy based on detection reception; an insulation detection unit training module, used to supervise and train an insulation detection unit based on the first characteristic and the second characteristic, and to pre-position a signal processing unit to construct an insulation detection module; an insulation detection list determination module, used to automatically configure a front-end detector based on the front-end detection strategy, perform signal preprocessing and insulation defect positioning on a received echo signal based on the insulation detection module, and determine an insulation detection list; a visualization warning module, used to visualize the insulation detection list on the distributed line model, and perform terminal interface display and warning.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application provides a cable casing insulation detection device and method using electromagnetic interference, which relate to the field of cable detection technology. By connecting a flexible DC microgrid aluminum electrolytic system, historical fault information and aluminum electrolytic busbar protection cabinet line information are obtained. After analyzing and obtaining the fault tracing information, the aluminum electrolytic busbar protection cabinet line is divided into a first protection area, a second protection area, and a third protection area. The protection lines of the three areas are optimized to generate a first protection line, a second protection line, and a third protection line. Finally, the flexible DC microgrid aluminum electrolytic system is protected by the three protection lines. The technical problem that the existing cable insulation detection relies on local discharge detection or insulation resistance measurement, is easily interfered by external electromagnetic signals, and has low detection efficiency and accuracy is solved. The technical effect of improving the efficiency and accuracy of cable insulation defect detection through analysis of cable signal characteristics and directional automation strategies is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1A schematic diagram of a method for detecting the insulation of a cable housing using electromagnetic interference provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a cable jacket insulation detection device using electromagnetic interference provided in an embodiment of the present application.

[0010] Explanation of the accompanying drawings: distributed line model building module 11, signal feature mining module 12, front-end detection strategy configuration module 13, insulation detection unit training module 14, insulation detection order determination module 15, visual early warning module 16. DETAILED DESCRIPTION

[0011] The present application provides a cable casing insulation detection device and method using electromagnetic interference, which is used to solve the technical problems that the existing cable insulation detection relies on partial discharge detection or insulation resistance measurement, is easily interfered by external electromagnetic signals, and has low detection efficiency and accuracy.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a method for detecting the insulation of a cable housing using electromagnetic interference, the method comprising: P10: Build a distribution line model of the cable, wherein the distribution line model includes a cable part and an environmental field part.

[0015] Specifically, a cable distribution line model is constructed using virtual modeling software or platform to simulate the actual state of the cable and its surrounding environment. The distribution line model includes two main parts: a cable part and an environmental field part.

[0016] The cable part is the core component of the model, which mainly describes the geometric structure of the cable and its electrical characteristics. The geometric structure includes the length, cross-sectional shape, material properties, etc. of the cable, while the electrical characteristics cover the resistance, capacitance, inductance and impedance of the cable. These parameters are crucial for simulating the signal transmission behavior of the cable in the working environment. The detailed modeling of the cable part allows us to analyze the dynamic characteristics of the signal, such as the change of voltage and current over time and its propagation in the cable.

[0017] The environmental field part is the modeling of the external environment of the cable, which not only affects the operating stability of the cable, but also directly affects the signal transmission characteristics of the cable. For example, external factors such as the surrounding electromagnetic field, temperature, humidity, etc. will cause changes in the signal transmission of the cable. The introduction of the environmental field enables the model to reflect the complex situation of the cable when it is running in real scenarios. For cable insulation testing, external electromagnetic interference (such as electromagnetic radiation from other equipment or lines) may cause partial discharge or other abnormal phenomena, so modeling the external environment is particularly critical.

[0018] This model uses distributed parameters to characterize the propagation characteristics of signals along the cable. Distributed parameters such as the resistance, inductance, and capacitance of the cable can change at different locations and time points as environmental factors change. This distributed model is different from the lumped parameter model and can more accurately describe the changes in electromagnetic signals in long cable lines. Therefore, by adjusting these distributed parameters, the dynamic characteristics of the cable in different environments can be simulated, providing a basis for subsequent signal detection and fault analysis.

[0019] P20: Based on the distributed line model, simulation is performed by adjusting the distribution parameters to explore the first characteristic and the second characteristic, wherein the first characteristic is the dynamic characteristic and transmission characteristic of the line signal, and the second characteristic is the signal coexistence characteristic of pulsed electromagnetic interference and partial discharge.

[0020] It should be understood that when performing analysis based on the constructed distributed line model, the behavior of the cable under different operating conditions is first simulated by adjusting the distributed parameters. The simulation process relies on numerical calculation and simulation techniques, such as using finite element analysis (FEA) or finite difference time domain method (FDTD), to finely simulate the interaction between signal propagation and electromagnetic interference. The distributed parameters include the resistance, capacitance, inductance, etc. of the cable, which can dynamically characterize the various characteristics of the cable when transmitting signals. Through this parameter adjustment, the dynamic characteristics of the cable in actual work can be simulated more accurately.

[0021] In the process of adjusting the distributed parameters, we focus on exploring two key characteristics: the first characteristic and the second characteristic. Among them, the first characteristic is the dynamic characteristics and transmission characteristics of the cable line signal. The dynamic characteristics refer to the changes in voltage and current over time when the cable transmits electrical signals; while the transmission characteristics refer to the propagation behavior of the signal in the cable, such as signal delay, attenuation and reflection. By adjusting the distributed parameters of the cable, these dynamic changes can be simulated, and then the stability and integrity of the signal under different conditions can be analyzed.

[0022] The second characteristic is related to the coexistence of pulsed electromagnetic interference and partial discharge signals. Pulsed electromagnetic interference refers to a short-term high-intensity electromagnetic interference signal, which usually comes from the equipment or environment around the cable. This interference works together with the partial discharge phenomenon inside the cable. Partial discharge is a high-frequency short-term discharge phenomenon caused by defects in the cable insulation layer. By adjusting the distribution parameters, it is possible to analyze how the signal behaves when electromagnetic interference and partial discharge coexist. This characteristic is crucial for insulation testing, because insulation defects in cables are often accompanied by the occurrence of electromagnetic interference and partial discharge. Capturing this coexistence phenomenon can more accurately identify insulation problems.

[0023] Furthermore, for mining the first characteristic, step P20 of the embodiment of the present application also includes: P21: acquiring line simulation data, wherein the line simulation data is marked with line distribution parameters; P22: performing scene clustering processing and spatiotemporal analysis on the line simulation data to mine the first characteristic; P23: wherein, performing phase fluctuation analysis on the same-position signals to mine the dynamic characteristics, performing spatial transmission analysis on the same-phase interval signals to mine the transmission characteristics.

[0024] Optionally, the detailed process of mining the first characteristic (i.e., the dynamic characteristics and transmission characteristics of the line signal) can be, firstly, based on the established distributed line model, by adjusting the distribution parameters (such as cable length, material properties, environmental field conditions, etc.), multi-scenario line simulation is performed to obtain a series of line simulation data marked with line distribution parameters. These data are the basis for subsequent analysis, and record in detail the propagation and change rules of the line signal under different parameter configurations.

[0025] Next, these line simulation data are subjected to in-depth scene clustering and spatiotemporal analysis. Scene clustering aims to group simulation data with similar characteristics into one category, so as to more clearly identify the impact of different parameter configurations on line signals. Spatiotemporal analysis refers to considering the changing behavior of signals in time and space at the same time. For example, although cable signals are transmitted quickly, they are still affected by factors such as time delay and spatial attenuation. Therefore, through this spatiotemporal analysis, the dynamic characteristics and transmission characteristics of signals can be fully explored from different dimensions.

[0026] Specifically, when mining dynamic characteristics, the time-phase fluctuation analysis method is used to analyze the time dimension of the signal at the same location. By comparing the changes in parameters such as signal strength and frequency at the same location at different time points, the dynamic change law of the signal can be revealed. For example, the fluctuation trend of the signal in a specific time period and the difference in signal fluctuation under different parameter configurations can be observed.

[0027] When mining transmission characteristics, spatial transmission analysis is performed on the signals in the same phase interval. Although the propagation speed of electrical signals in cables is very fast, their transmission is still affected by factors such as line impedance, medium characteristics, and external interference, resulting in spatial attenuation and reflection of signals at different locations. By performing a detailed analysis of the spatial transmission process of the signal at a reasonable time granularity (usually the time interval needs to be selected in combination with the signal transmission speed and analysis requirements), the transmission characteristics of the signal can be accurately mined, such as the signal's delay, reflection characteristics, and attenuation law.

[0028] Furthermore, in mining the second characteristic, step P20 in the embodiment of the present application further includes: P24: With pulsed electromagnetic interference as the center position, the distribution of local discharge is the first coexistence type; P25: With local discharge as the center position, the distribution of pulsed electromagnetic interference is the second coexistence type, wherein, if there is pulsed electromagnetic interference, there is a defective position of electromagnetic penetration coexisting with local discharge, and there is a defective position of local discharge coexisting with pulsed electromagnetic interference; P26: Traverse the line simulation data, and based on the first coexistence type and the second coexistence type, mine the signal coexistence characteristics.

[0029] In a possible embodiment of the present application, the specific process of mining the second characteristic may be to first analyze the pulsed electromagnetic interference as the central position. Pulse electromagnetic interference is a high-intensity electromagnetic signal that bursts out in a short period of time, usually generated by electronic equipment or other lines in the external environment. This interference may induce local discharge near the cable. Local discharge is a high-frequency discharge phenomenon caused by defects in the insulation layer inside the cable, accompanied by rapid changes in electromagnetic waves. Therefore, taking the pulsed electromagnetic interference as the center, by analyzing whether there is local discharge in the surrounding area, this phenomenon is defined as the first coexistence type. This type shows that the location where electromagnetic interference occurs is often accompanied by local discharge caused by insulation defects inside the cable.

[0030] Next, the relationship between partial discharge and pulsed electromagnetic interference is analyzed by taking partial discharge as the central location. Partial discharge usually occurs at the insulation defects of the cable, which will generate high-frequency signals and electromagnetic radiation. Conversely, the location of partial discharge may also become the origin or amplification area of ​​pulsed electromagnetic interference. Therefore, the distribution of pulsed electromagnetic interference around partial discharge is taken as the focus, and this phenomenon is defined as the second coexistence type. This type reflects that the defective location of partial discharge is often accompanied by the penetration or amplification of pulsed electromagnetic interference.

[0031] In order to fully understand these coexistence phenomena, the distribution of pulsed electromagnetic interference and partial discharge in the line is analyzed one by one by traversing the line simulation data. According to the first coexistence type and the second coexistence type, the coexistence characteristics of the signal, that is, the correlation between electromagnetic interference and partial discharge, are excavated. Specifically, if there is electromagnetic interference at a certain place, it is very likely that there are defects in the cable insulation layer at that place, resulting in the occurrence of partial discharge; vice versa, if partial discharge is found at a certain place, it may also be accompanied by pulsed electromagnetic interference. Signal processing and analysis techniques, such as wavelet transform and Fourier transform, can be used to extract the time domain and frequency domain characteristics of the signal. At the same time, machine learning algorithms, such as cluster analysis and support vector machine, are used to classify and identify the signal, so as to more accurately excavate the signal coexistence characteristics. Through these traversal analyses, the defect location in the cable can be further confirmed, and a more accurate reference can be provided for subsequent insulation detection.

[0032] P30: Based on the first characteristic and the second characteristic, configure a front-end detection strategy, where the front-end detection strategy is a directional automation strategy based on detection reception.

[0033] Specifically, after discovering the first characteristic (dynamic characteristics and transmission characteristics of line signals) and the second characteristic (signal coexistence characteristics of pulsed electromagnetic interference and partial discharge), the next step is to configure the front-end detection strategy.

[0034] The front-end detection strategy refers to a set of automated rules or algorithms used to preliminarily identify and evaluate the insulation performance of cables during the actual detection process. In an embodiment of the present application, a strategy for directional automated detection based on the reception of detection signals is adopted. The directional detection refers to analyzing the signals at specific locations in the line model and focusing on monitoring areas where local discharge and electromagnetic interference may coexist. Since these areas are usually potential occurrence points of cable insulation defects, directional detection can improve the accuracy and efficiency of detection.

[0035] When formulating the front-end detection strategy, the parameters of the detection equipment are first set according to the first characteristic (dynamic characteristics and transmission characteristics). For example, the filter and amplifier of the detection equipment can be set according to the frequency response and phase delay of the signal to ensure that the target signal can be accurately captured. At the same time, the sensitivity of the detection equipment can also be adjusted according to the attenuation of the signal to avoid misjudgment due to too weak a signal.

[0036] Secondly, the second characteristic (the coexistence characteristic of pulsed electromagnetic interference and partial discharge signals) is used to guide the detection direction of the detection equipment. Since the distribution and interaction of pulsed electromagnetic interference and partial discharge in the cable have certain regularities, the detection path and detection frequency of the detection equipment can be set according to these regularities. For example, in areas where pulsed electromagnetic interference exists, the detection frequency and detection depth are increased to more accurately identify potential partial discharge phenomena.

[0037] Finally, these parameters and direction settings are integrated into the automatic control system of the detection equipment to realize the automation and intelligence of the detection process. In the actual detection process, the detection equipment will automatically adjust the parameters and directions according to the preset strategy to conduct a full range of detection and evaluation of the cable. At the same time, the detection system can also record and analyze the detection data in real time, providing strong support for the subsequent insulation performance evaluation and defect location.

[0038] In order to implement this strategy, it is necessary to use advanced detection technologies and equipment, such as high-frequency current sensors, ultrasonic sensors, infrared thermal imagers, etc. These devices can provide high-precision and high-sensitivity detection capabilities, and can accurately capture weak signals and abnormal phenomena in the cable. At the same time, it is also necessary to use advanced signal processing and analysis technologies, such as wavelet transform, neural network, etc., to conduct in-depth analysis and mining of detection data, so as to more accurately evaluate the insulation performance of the cable and identify potential defect locations.

[0039] P40: Based on the first characteristic and the second characteristic, supervise the training of the insulation detection unit, and pre-place the signal processing unit to construct an insulation detection module. Optionally, an insulation detection module is constructed based on the first characteristic and the second characteristic. This module is the core part of cable insulation detection and can accurately identify insulation defects in the cable. Specifically, first supervise the training of the insulation detection unit. Supervised training is a machine learning technique that uses a known data set (i.e., sample data with good or bad insulation performance marked) to train a model so that it can identify the insulation performance of new, unlabeled data. In the scenario of the present application, this data set will contain various cable signal characteristics (such as dynamic characteristics, transmission characteristics, coexistence characteristics of pulsed electromagnetic interference and partial discharge, etc.), as well as corresponding insulation performance evaluation results.

[0040] In the supervised training process, we first need to extract these features from line simulation data or actual test data, and use appropriate feature selection methods (such as principal component analysis, mutual information method, etc.) to reduce the number of features and improve the generalization ability of the model. Then, we select a suitable machine learning algorithm (such as support vector machine, neural network, decision tree, etc.) to build an insulation detection unit, and use the extracted features and corresponding insulation performance evaluation results to train this unit.

[0041] However, since cable signals often contain a lot of noise and interference, directly inputting these signals into the insulation detection unit may lead to a decrease in model performance. Therefore, in the insulation detection module, a signal processing unit is also required to pre-process the input signal. The task of the signal processing unit is to remove noise, enhance signal features, and extract information useful for insulation performance evaluation. This can be achieved through steps such as filtering, denoising, and feature extraction.

[0042] Finally, the trained insulation detection unit and signal processing unit are combined to build a complete insulation detection module. This module can receive the signal input from the cable, pre-process it through the signal processing unit, and then use the insulation detection unit to evaluate the insulation performance of the processed signal.

[0043] P50: Based on the front-end detection strategy, the front-end detector is automatically configured, and the received echo signal is preprocessed and the insulation defect is located based on the insulation detection module to determine the insulation detection list.

[0044] It should be understood that after completing the configuration of the front-end detection strategy and the construction of the insulation detection module, the front-end detector is automatically configured to effectively receive the echo signal, and these signals are pre-processed and insulation defects are located through the insulation detection module, thereby achieving effective processing of the received echo signal and accurate positioning of the insulation defects.

[0045] Specifically, the front-end detector is a sensing device of the entire insulation detection system, responsible for real-time monitoring and receiving signals generated during cable transmission, especially echo signals that may reflect insulation defects. In order to ensure that the front-end detector can work efficiently, it is automatically configured based on the front-end detection strategy, that is, under different working conditions (such as changes in signal strength, electromagnetic interference strength, ambient temperature changes, etc.), the detector can adaptively adjust its detection parameters, such as gain, detection sensitivity, sampling frequency, etc., to ensure that the front-end detector always maintains the best signal reception state in a complex environment. This automatic configuration relies on the first and second characteristics previously discovered, especially the dynamic characteristics of the signal and the coexistence characteristics of electromagnetic interference and partial discharge. The detector will automatically adjust the detection parameters according to the signal strength and characteristics received at different locations and times to ensure efficient detection in the target area (such as areas where insulation defects may occur).

[0046] Subsequently, when the front-end detector receives the echo signal from the cable, these signals will first go through a signal preprocessing stage. Signal preprocessing is an important part of the insulation detection module. Advanced signal processing algorithms (such as filtering, denoising, feature extraction, etc.) can be used to remove noise and interference in the signal, while enhancing the useful features in the signal, providing clear and accurate signal input for subsequent insulation defect positioning.

[0047] After signal preprocessing, the insulation detection unit is used to evaluate the insulation performance of the processed signal, and the insulation performance of the cable is judged according to the characteristics in the signal (such as changes in dynamic characteristics, differences in transmission characteristics, coexistence mode of pulsed electromagnetic interference and partial discharge, etc.), and potential insulation defects are identified, and the cable segments or specific locations with insulation defects are marked, and then an insulation test sheet is generated. The insulation test sheet is a report generated by the system, which contains the results of the insulation test, especially the type, location and severity of the defect. This test sheet will serve as an important basis for cable maintenance and repair, and provide detailed test information for operation and maintenance personnel.

[0048] Furthermore, the signal preprocessing step P50 in the embodiment of the present application further includes: P51: Acquire an interfering electromagnetic field; P52: Based on the interfering electromagnetic field, determine the interference characteristics of the signal transmission and determine the first interference information; P53: Determine the second attenuation information based on the echo distance between the front-end detector and the cable detection position; P54: Perform signal preprocessing based on the first interference information and the second attenuation information.

[0049] Specifically, when performing signal preprocessing, special consideration should be given to the electromagnetic interference in the cable. When there are insulation defects (such as cracks, damage or aging) in the cable casing, external electromagnetic interference signals will form an electromagnetic field around the cable. These electromagnetic fields penetrate into the cable through the insulation defects, causing induced currents inside, which will be used for insulation detection. But at the same time, the existence of the electromagnetic field may also interfere with the detection signal, causing the echo signal to be distorted. Therefore, the key to signal preprocessing is how to effectively process the echo signal based on these interference characteristics.

[0050] First, it is necessary to obtain the interference electromagnetic field around the cable. This electromagnetic field is usually generated by external equipment or the environment. With the insulation defects of the cable shell, the electromagnetic field enters the cable through these defects, forming electromagnetic interference signals. These interference signals will be mixed with the normal signals inside the cable, which brings challenges to signal detection. Therefore, accurately obtaining the interference electromagnetic field is the first step in signal preprocessing, which can be achieved by placing electromagnetic detectors around the cable.

[0051] After obtaining the interference electromagnetic field, it is necessary to analyze the interference characteristics of these interference signals. The interference characteristics include the frequency, intensity and distribution of the electromagnetic field at different locations. By analyzing the interference characteristics, the specific impact of these interferences on the cable signal transmission can be determined, and the first interference information can be generated. For example, when the interference signal intensity in the cable is high, it may cause a large distortion of the echo signal or cover the partial discharge signal.

[0052] Next, the second attenuation information is determined based on the position of the front-end detector and the echo distance of the detection point in the cable. The signal in the cable will gradually attenuate as the transmission distance increases, especially in the presence of electromagnetic interference, the signal attenuation will be more significant. Therefore, by accurately calculating the transmission distance of the signal, the attenuation of the signal during the transmission process can be determined. This information will serve as the basis for the attenuation characteristics to generate the second attenuation information. This process is usually analyzed using the attenuation model of signal transmission, and is inferred in combination with the actual position of the front-end detector and the received echo signal.

[0053] Finally, the received echo signal is preprocessed by combining the first interference information (the characteristics of the interfering electromagnetic field) and the second attenuation information (the attenuation during signal transmission). The core goal of signal preprocessing is to eliminate or reduce the impact of electromagnetic interference on normal signals, while compensating for signal loss due to attenuation. This step usually involves filtering algorithms and signal recovery techniques, such as using adaptive filters to remove electromagnetic interference, or using deconvolution technology to restore attenuated signals. After preprocessing, the quality of the signal is improved, ensuring more accurate positioning of insulation defects.

[0054] Furthermore, step P54 of the embodiment of the present application also includes: P54-1: Receive the echo signal and convert it into a signal spectrum; P54-2: Based on the first interference information and the second attenuation information, perform interference filtering and attenuation compensation on the signal spectrum to determine the initialization signal spectrum; P54-3: Traverse the initialization signal spectrum, determine the wave distribution clarity based on the signal feature recognition standard, and set the phase frequency conversion information, wherein the phase frequency conversion information identifies the signal segment; P54-4: Based on the phase frequency conversion information, perform positioning and phase expansion processing on the initialization signal spectrum to determine the preprocessing signal.

[0055] Optionally, the clarity and accuracy of the signal can be improved by analyzing and processing the signal spectrum to ensure that the final signal can effectively identify insulation defects. First, the echo signal received by the front-end detector needs to be converted to generate a signal spectrum. Time-frequency analysis techniques, such as Fourier transform (FFT) and short-time Fourier transform (STFT), can be used to convert the time domain signal into a frequency domain spectrum. The signal spectrum is a visual representation of the signal in the time domain and frequency domain. It presents the time change and frequency distribution of the signal to facilitate subsequent processing and analysis. Through the signal spectrum, you can clearly see the characteristics of the signal such as waveform, frequency distribution, and phase change.

[0056] After obtaining the signal spectrum, interference filtering and attenuation compensation are performed on the signal spectrum based on the first interference information (electromagnetic interference characteristics) and the second attenuation information (signal attenuation characteristics) extracted previously. The interference filtering is based on the characteristics of the interfering electromagnetic field (such as frequency and intensity), and uses technologies such as adaptive filters to effectively filter the interference part of the signal, thereby removing or weakening the impact of electromagnetic interference on the signal. The attenuation compensation is to restore the original strength of the signal through a compensation algorithm to restore the signal to a state close to the initial state due to the attenuation caused by factors such as distance during the transmission process of the signal. After this step of processing, the generated initialization signal spectrum is a spectrum that has been corrected for interference and attenuation, ensuring the clarity and accuracy of the signal.

[0057] Furthermore, the initialized signal spectrum is traversed, and the wave distribution clarity is determined according to the signal feature recognition standard (such as the amplitude, frequency, phase and other characteristics of the waveform). Wave distribution clarity refers to the clarity and recognizability of the waveform distribution in the signal spectrum. In order to improve the wave distribution clarity, the phase frequency conversion information is set, that is, the signal segments are divided and identified according to the phase characteristics of the signal. The phase frequency conversion information can more accurately identify the different components and characteristics in the signal, providing a basis for subsequent signal processing.

[0058] Finally, based on the generated phase frequency conversion information, the initialization signal spectrum is located and phase expanded. Phase expansion refers to the phase amplification of certain signal segments with frequent fluctuations or similar waveforms to improve the clarity of these areas. For example, the phase range 2 / π-π in the signal segment can be enlarged and expanded to 2 / π-3 / 4π, 3 / 4π-π, which can make the signal segments that were originally difficult to distinguish clearer. Through this phase expansion processing, it is ensured that all key signal segments in the signal spectrum can be accurately identified, thereby generating the final preprocessed signal, providing high-quality signal data support for the subsequent insulation defect positioning.

[0059] Furthermore, the insulation defect location is performed, and step P50 of the embodiment of the present application further includes: P55: Obtain the first signal class and the second signal class after preprocessing, wherein the first signal class is an induced electrical signal based on electromagnetic penetration, and the second signal class is a local insulation discharge signal; P56: Map the first signal class and the second signal class, determine a mapped signal pair and a non-mapped signal, and the non-mapped signal includes the first signal and the second signal; P57: Based on the mapped signal, determine the insulation detection information, and add it to the insulation detection sheet.

[0060] It should be understood that in the process of locating insulation defects, the embodiment of the present application further improves the accuracy of insulation detection by mapping two types of signals. Specifically, first, the first signal type and the second signal type after preprocessing are obtained. The first signal type is an induced electrical signal based on electromagnetic penetration. This type of signal is usually caused by the penetration of external electromagnetic fields into the interior of the cable through insulation defects in the cable casing. The second signal type is a local insulation discharge signal. This type of signal is caused by local discharge phenomena caused by aging, damage or moisture of the insulation material inside the cable. The preprocessing results of these two types of signals can be obtained by receiving and processing them by the front-end detector.

[0061] Next, the first signal class and the second signal class are mapped to determine the mapped signal pair and the non-mapped signal. The mapped signal pair refers to the detection of both the induced electrical signal based on electromagnetic penetration and the signal based on partial discharge at the same or similar position. This type of signal pair verifies each other, which improves the accuracy and reliability of insulation defect positioning. Non-mapped signals refer to situations where only one of the signals can be detected. Such signals may fail to detect the other signal at the same time due to interference, attenuation or detector performance. Non-mapped signals include one of the first signal (only the electromagnetic penetration signal is detected) and the second signal (only the partial discharge signal is detected), which requires further verification to determine whether there is an insulation defect.

[0062] Finally, based on the mapped signal pairs, the insulation detection information is determined and added to the insulation detection form. The insulation detection information includes key information such as the location, size, and type of insulation defects, which are essential for subsequent cable maintenance, repair, and replacement. Through the mutual verification of the mapped signal pairs, the existence and characteristics of insulation defects can be determined more accurately, providing a strong guarantee for the safe operation of the cable. At the same time, for non-mapped signals, further verification and analysis are also required to determine whether they truly represent the existence of insulation defects.

[0063] Furthermore, the embodiment of the present application further includes step P58, and step P58 further includes: P58-1: Traverse the non-mapped signal, perform distribution verification on the non-mapped signal based on the first coexistence type and the second coexistence type, and determine the verification result; P58-2: If the verification result satisfies the first coexistence type or the second coexistence type, generate insulation detection information and add it to the insulation detection form.

[0064] In a possible embodiment of the present application, the accuracy and reliability of insulation detection are further improved by performing distribution verification on non-mapped signals. First, all non-mapped signals are traversed, and these signals may only contain the first signal (induced electrical signal based on electromagnetic penetration) or the second signal (local insulation discharge signal). In order to verify whether these non-mapped signals truly represent the existence of insulation defects, distribution verification is performed based on two coexistence types. The first coexistence type means that in certain areas of the cable, due to aging, damage or moisture of the insulating material, electromagnetic penetration signals and local discharge signals may be generated at the same time, but due to detection conditions, sometimes only one of them can be detected. The second coexistence type means that in certain specific cases, such as cable joints or places with a small bending radius, due to the uneven distribution of the electric field, only a single type of signal may be detected.

[0065] During the distribution verification process, signal processing techniques and algorithms, such as signal correlation analysis and pattern recognition, are used to extract and compare the features of non-mapped signals to determine whether they meet the characteristics of the two coexistence types mentioned above. Through this verification, it is possible to more accurately determine whether the non-mapped signals are truly caused by insulation defects and avoid false positives or negatives.

[0066] If the verification result meets the characteristics of the first coexistence type or the second coexistence type, it is considered that the non-mapped signal does represent the existence of an insulation defect. At this time, the corresponding insulation detection information is generated, including key information such as the location, possible causes, and severity of the insulation defect, and added to the insulation detection form. If the verification result does not meet the coexistence type requirements, the non-mapped signal may only be interference or a slight abnormality, and does not constitute a serious defect. The system will mark it as "to be observed" or "mild abnormality" for further monitoring and tracking. This information is of great guiding significance for subsequent cable maintenance, repair, and replacement work.

[0067] P60: Visualize the insulation test sheet on the distribution line model to perform terminal interface display and early warning.

[0068] Specifically, firstly, various data in the insulation inspection form (such as the location, type, severity of insulation defects, etc.) are associated with the distribution line model. The distribution line model is a digital representation of the cable line, which accurately reflects the key information such as the direction, branches, joints, etc. of the cable line. By combining the insulation inspection data with this model, it is possible to clearly see which locations have insulation performance problems and the severity of the problems.

[0069] Next, visualization technology is used to display the information in the insulation inspection form in a graphical manner on the terminal interface. This usually includes using different colors, icons or lines to represent different types of insulation defects, and using dynamic effects to display the changing trend of defects. This display method is not only intuitive and easy to understand, but also helps users quickly identify the key to the problem and make accurate decisions.

[0070] Furthermore, based on the severity of the detected defects and the coexistence of the signals, if there is a high risk of insulation defects in a certain area (such as the detection of mapped signal pairs), the system will trigger an early warning mechanism. The early warning mechanism can prompt the operation and maintenance personnel to take immediate action through visual reminders (such as highlighting the defect location or changing the color) and audio prompts. In addition, the early warning information can also be sent to the management center through the network for remote monitoring and decision-making.

[0071] By visualizing the insulation test results and the distribution line model, and displaying and warning in real time on the terminal interface, operation and maintenance personnel can intuitively and quickly identify insulation problems in the cable and respond in a timely manner, thereby improving the efficiency and accuracy of cable fault detection and ensuring the safe operation of the cable.

[0072] In summary, the embodiments of the present application have at least the following technical effects: This application constructs a cable distribution line model, simulates and adjusts parameters to mine signal characteristics, configures front-end detection strategies based on the characteristics, supervises and trains insulation detection units, integrates signal processing units into modules, uses modules to process echo signals, locates insulation defects, generates inspection orders and visualizes them on the distribution line model, displays and warns on the terminal interface, and realizes efficient insulation monitoring.

[0073] The technical effect of improving the efficiency and accuracy of cable insulation defect detection has been achieved through analysis of cable signal characteristics and directional automation strategies.

[0074] Embodiment 2 is based on the same inventive concept as the cable housing insulation detection method using electromagnetic interference in the previous embodiment. Figure 2 As shown, the present application provides a cable housing insulation detection device using electromagnetic interference, and the device and method embodiments in the present application are based on the same inventive concept. The device includes: The distributed line model building module 11 is used to build a distributed line model of the cable, wherein the distributed line model includes a cable part and an environmental field part.

[0075] The signal feature mining module 12 is used to simulate and mine the first characteristic and the second characteristic based on the distributed line model by adjusting the distribution parameters, wherein the first characteristic is the dynamic characteristic and transmission characteristic of the line signal, and the second characteristic is the signal coexistence characteristic of pulsed electromagnetic interference and partial discharge.

[0076] The front-end detection strategy configuration module 13 is used to configure the front-end detection strategy based on the first characteristic and the second characteristic, and the front-end detection strategy is a directional automation strategy based on detection reception.

[0077] The insulation detection unit training module 14 is used to supervise the training of the insulation detection unit based on the first characteristic and the second characteristic, and to pre-process the signal to construct an insulation detection module.

[0078] The insulation inspection list determination module 15 is used to automatically configure the front-end detector based on the front-end detection strategy, perform signal preprocessing and insulation defect positioning on the received echo signal based on the insulation detection module, and determine the insulation inspection list.

[0079] The visualization warning module 16 is used to visualize the insulation detection list on the distribution line model to perform terminal interface display and warning.

[0080] Furthermore, the signal feature mining module 12 is also used to perform the following steps: Acquire line simulation data, wherein the line simulation data is marked with line distribution parameters; perform scene clustering processing and spatiotemporal analysis on the line simulation data to mine the first characteristic; wherein, perform phase fluctuation analysis on the same-position signal to mine the dynamic characteristic, and perform spatial transmission analysis on the same-phase interval signal to mine the transmission characteristic.

[0081] Furthermore, the signal feature mining module 12 is also used to perform the following steps: Taking pulsed electromagnetic interference as the center position, the distribution of local discharge is the first coexistence type; taking local discharge as the center position, the distribution of pulsed electromagnetic interference is the second coexistence type, wherein, if there is pulsed electromagnetic interference, there is a defective position of electromagnetic penetration coexisting with local discharge, and there is a defective position of local discharge coexisting with pulsed electromagnetic interference; traversing the line simulation data, based on the first coexistence type and the second coexistence type, the signal coexistence characteristics are mined.

[0082] Furthermore, the insulation test sheet determination module 15 is also used to perform the following steps: Acquire an interfering electromagnetic field; based on the interfering electromagnetic field, perform interference characteristics of signal transmission to determine first interference information; based on the echo distance between the front-end detector and the cable detection position, determine second attenuation information; based on the first interference information and the second attenuation information, perform signal preprocessing.

[0083] Furthermore, the insulation test sheet determination module 15 is also used to perform the following steps: Receive the echo signal and convert it into a signal spectrum; based on the first interference information and the second attenuation information, perform interference filtering and attenuation compensation on the signal spectrum to determine the initialization signal spectrum; traverse the initialization signal spectrum, determine the wave distribution clarity based on the signal feature recognition standard, and set the phase frequency conversion information, wherein the phase frequency conversion information identifies the signal segment; based on the phase frequency conversion information, perform positioning and phase expansion processing on the initialization signal spectrum to determine the preprocessing signal.

[0084] Furthermore, the insulation test sheet determination module 15 is also used to perform the following steps: Obtain a preprocessed first signal class and a second signal class, wherein the first signal class is an induced electrical signal based on electromagnetic penetration, and the second signal class is a local insulation discharge signal; map the first signal class and the second signal class to determine a mapped signal pair and a non-mapped signal, wherein the non-mapped signal includes a first signal and a second signal; determine insulation detection information based on the non-mapped signal, and add it to the insulation detection sheet.

[0085] Furthermore, the insulation test sheet determination module 15 is also used to perform the following steps: The non-mapped signal is traversed, and based on the first coexistence type and the second coexistence type, distribution verification is performed on the non-mapped signal to determine a verification result; if the verification result satisfies the first coexistence type or the second coexistence type, insulation detection information is generated and added to the insulation detection sheet.

[0086] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application. This specification and the drawings are merely exemplary illustrations of the present application and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application intends to include these modifications and variations.

Claims

1. A cable sheath insulation detection device using electromagnetic interference, characterized in that: The device comprises: A distribution line model building module, used to build a distribution line model of the cable, wherein the distribution line model includes a cable part and an environmental field part; A signal feature mining module, for mining a first characteristic and a second characteristic by adjusting distribution parameters for simulation based on the distributed line model, wherein the first characteristic is a dynamic characteristic and a transmission characteristic of a line signal, and the second characteristic is a signal coexistence characteristic of pulsed electromagnetic interference and partial discharge; A front-end detection strategy configuration module, configured to configure a front-end detection strategy based on the first characteristic and the second characteristic, wherein the front-end detection strategy is a directional automation strategy based on detection reception; An insulation detection unit training module, used for supervising the training of the insulation detection unit based on the first characteristic and the second characteristic, and pre-positioning a signal processing unit to construct an insulation detection module; An insulation inspection list determination module is used to automatically configure the front-end detector based on the front-end detection strategy, perform signal preprocessing and insulation defect positioning on the received echo signal based on the insulation detection module, and determine the insulation inspection list; The visual warning module is used to visualize the insulation detection list on the distribution line model to perform terminal interface display and warning.

2. The cable sheath insulation detection device using electromagnetic interference as claimed in claim 1, characterized in that: The mining of the first characteristic comprises: Acquire line simulation data, where the line simulation data is identified with line distribution parameters; Performing scene clustering processing and spatiotemporal analysis on the line simulation data to mine the first characteristic; Among them, the phase fluctuation analysis is performed on the signals at the same position to explore the dynamic characteristics, and the spatial transmission analysis is performed on the signals at the same phase interval to explore the transmission characteristics.

3. The cable sheath insulation detection device using electromagnetic interference as claimed in claim 2, characterized in that: Exploring the second characteristic, including: The first coexistence type is based on the pulsed electromagnetic interference as the central position and the distribution of partial discharge as the first coexistence type; The distribution of pulse electromagnetic interference is the second coexistence type, with partial discharge as the center position, wherein if pulse electromagnetic interference exists, the defect position with electromagnetic penetration coexists with partial discharge, and the defect position with partial discharge coexists with pulse electromagnetic interference; The line simulation data is traversed, and the signal coexistence characteristics are mined based on the first coexistence type and the second coexistence type.

4. The cable sheath insulation detection device using electromagnetic interference as claimed in claim 1, characterized in that: The signal preprocessing comprises: Acquiring interference electromagnetic fields; Based on the interference electromagnetic field, determine first interference information based on interference characteristics of signal transmission; determining second attenuation information based on an echo distance between the front-end detector and the cable detection position; Signal preprocessing is performed based on the first interference information and the second attenuation information.

5. The cable sheath insulation detection device using electromagnetic interference as claimed in claim 4, characterized in that: Based on the first interference information and the second attenuation information, signal preprocessing is performed, including: Receive the echo signal and convert it into a signal spectrum; Based on the first interference information and the second attenuation information, performing interference filtering and attenuation compensation on the signal spectrum to determine an initialization signal spectrum; Traversing the initialization signal spectrum, determining the wave distribution clarity based on the signal feature recognition standard, and setting the phase frequency conversion information, wherein the phase frequency conversion information is marked with a signal segment; Based on the phase frequency conversion information, the initialization signal spectrum is positioned and phase expanded to determine a preprocessing signal.

6. The cable sheath insulation detection device using electromagnetic interference as claimed in claim 3, characterized in that: Conduct insulation defect location, including: Acquire a first signal type and a second signal type after preprocessing, wherein the first signal type is an induced electrical signal based on electromagnetic penetration, and the second signal type is a local insulation discharge signal; Mapping the first signal class and the second signal class to determine a mapped signal pair and a non-mapped signal, wherein the non-mapped signal includes a first signal and a second signal; Based on the non-mapped signal, insulation detection information is determined and added to the insulation detection list.

7. The cable sheath insulation detection device using electromagnetic interference as claimed in claim 6, characterized in that: Also includes: Traversing the non-mapped signal, performing distribution verification on the non-mapped signal based on the first coexistence type and the second coexistence type, and determining a verification result; If the verification result satisfies the first coexistence type or the second coexistence type, insulation detection information is generated and added to the insulation detection list.

8. A method for detecting the insulation of a cable sheath using electromagnetic interference, characterized in that: The method comprises: Building a distribution line model of the cable, wherein the distribution line model includes a cable part and an environmental field part; Based on the distributed line model, a first characteristic and a second characteristic are mined by adjusting the distribution parameters for simulation, wherein the first characteristic is a dynamic characteristic and a transmission characteristic of a line signal, and the second characteristic is a signal coexistence characteristic of pulsed electromagnetic interference and partial discharge; Based on the first characteristic and the second characteristic, configuring a front-end detection strategy, wherein the front-end detection strategy is a directional automation strategy based on detection reception; Based on the first characteristic and the second characteristic, supervise the training of the insulation detection unit, and pre-place the signal processing unit to construct an insulation detection module; Based on the front-end detection strategy, the front-end detector is automatically configured, the received echo signal is preprocessed and the insulation defect is located based on the insulation detection module, and the insulation detection list is determined; the insulation detection list is visualized on the distributed line model, and the terminal interface is displayed and early warning is issued.

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