Device and method for detecting insulation of cable sheath using electromagnetic interference

Abstract

The present invention discloses a device and method for detecting the insulation of cable sheaths using electromagnetic interference, relating to the field of cable detection technology. The method comprises: constructing a cable distribution line model, simulating and adjusting parameters to mine signal characteristics, configuring a front-end detection strategy based on the characteristics, supervising and training insulation detection units, integrating signal processing units into modules, utilizing the modules to process echo signals, locating insulation defects, generating a test sheet and visualizing it on the distribution line model, displaying it on a terminal interface and issuing an early warning, thereby achieving efficient insulation monitoring. The present invention solves the technical problem that existing cable insulation detection relies on partial discharge detection or insulation resistance measurement, is susceptible to interference from external electromagnetic signals, and has low detection efficiency and accuracy. The present invention achieves the technical effect of improving the efficiency and accuracy of cable insulation defect detection through analysis of cable signal characteristics and a targeted automation strategy.

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, the safety and stability of cables, as core components of power transmission, have received increasing attention. 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 testing 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 test results. Summary of the Invention

[0004] The present application provides a device and method for detecting the insulation of a cable jacket using electromagnetic interference, which is used to solve the technical problems that 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 sheath 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 distribution parameters to mine 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, wherein 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 for terminal interface display and early warning.

[0006] According to a second aspect of the present application, a device for detecting the insulation properties of a cable jacket using electromagnetic interference is provided. The device includes: a distributed line model building module for building a distributed line model of a cable, wherein the distributed line model includes a cable portion and an environmental field portion; a signal feature mining module for simulating and mining a first characteristic and a second characteristic by adjusting distribution parameters based on the distributed line model, 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; a front-end detection strategy configuration module for configuring 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 for supervising the training of an 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 detection list determination module for automatically configuring a front-end detector based on the front-end detection strategy, performing signal preprocessing and insulation defect location on a received echo signal based on the insulation detection module, and determining an insulation detection list; and a visual warning module for visualizing the insulation detection list on the distributed line model for terminal interface display and warning.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] 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 to a flexible DC microgrid electrolytic aluminum system, historical fault information and electrolytic aluminum busbar protection cabinet line information are obtained. After analyzing and obtaining the fault tracing information, the electrolytic aluminum 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 electrolytic aluminum system is protected by the three protection lines. This solves the technical problem that 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. This achieves the technical effect of improving the efficiency and accuracy of cable insulation defect detection through analysis of cable signal characteristics and directional automation strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] 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.

[0010] Figure 1 A schematic flow chart of a method for detecting the insulation of a cable jacket using electromagnetic interference according to an embodiment of the present application;

[0011] Figure 2 A schematic structural diagram of a cable jacket insulation detection device utilizing electromagnetic interference provided in an embodiment of the present application.

[0012] Explanation of the accompanying symbols: 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 warning module 16. DETAILED DESCRIPTION

[0013] The present application provides a device and method for detecting the insulation of a cable jacket using electromagnetic interference, which is used to solve the technical problems that 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.

[0014] The following will be combined with the accompanying 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 some of the embodiments of this application, not all of them. 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.

[0015] 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 clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0016] Example 1, as Figure 1 As shown, the present application provides a method for detecting the insulation of a cable jacket using electromagnetic interference, the method comprising:

[0017] P10: Build a distribution line model of the cable, wherein the distribution line model includes a cable part and an environmental field part.

[0018] Specifically, a cable distribution line model is constructed using virtual modeling software or a platform to simulate the actual state of the cable and its surrounding environment. The distribution line model comprises two main parts: a cable part and an environmental field part.

[0019] The cable is the core component of the model, primarily describing the cable's geometry and electrical properties. The geometry includes the cable's length, cross-sectional shape, and material properties, while the electrical properties encompass the cable's resistance, capacitance, inductance, and impedance. These parameters are crucial for simulating the signal transmission behavior of the cable in its operating environment. This detailed modeling of the cable allows us to analyze the dynamic characteristics of signals, such as the temporal evolution of voltage and current, and their propagation through the cable.

[0020] The environmental field modeling component models the cable's external environment, which not only affects the cable's operational stability but also directly impacts its signal transmission characteristics. For example, external factors such as the surrounding electromagnetic field, temperature, and humidity can cause variations in the cable's signal transmission. The inclusion of the environmental field allows the model to reflect the complexities of cable operation in real-world scenarios. For cable insulation testing, external electromagnetic interference (such as electromagnetic radiation from other equipment or lines) can cause partial discharge or other anomalies, making modeling the external environment crucial.

[0021] This model uses distributed parameters to characterize the propagation characteristics of signals along cables. Distributed parameters, such as the cable's resistance, inductance, and capacitance, can vary at different locations and time points as environmental factors change. This distributed model, unlike lumped parameter models, more accurately describes electromagnetic signal variations in long cable runs. Therefore, by adjusting these distributed parameters, the dynamic characteristics of the cable under different environments can be simulated, providing a basis for subsequent signal detection and fault analysis.

[0022] 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.

[0023] It should be understood that when analyzing the constructed distributed line model, the cable's behavior under different operating conditions is first simulated by adjusting distributed parameters. This simulation process relies on numerical calculations and simulation techniques, such as finite element analysis (FEA) or finite-difference time-domain (FDTD), to accurately simulate the interaction between signal propagation and electromagnetic interference. These distributed parameters, including the cable's resistance, capacitance, and inductance, dynamically characterize the various characteristics of the cable during signal transmission. This parameter adjustment allows for a more accurate simulation of the cable's dynamic characteristics in actual operation.

[0024] When adjusting the distributed parameters, we focus on exploring two key characteristics: the first characteristic and the second characteristic. The first characteristic is the dynamic and transmission characteristics of the cable line signal. Dynamic characteristics refer to the temporal variations in voltage and current when the cable transmits an electrical signal; transmission characteristics, on the other hand, refer to the signal propagation behavior within the cable, such as signal delay, attenuation, and reflection. By adjusting the cable's distributed parameters, we can simulate these dynamic variations and analyze the signal's stability and integrity under different conditions.

[0025] The second characteristic relates to the coexistence of pulsed electromagnetic interference (EMI) and partial discharge (PD) signals. Pulsed EMI refers to short, high-intensity EMI signals, typically originating from equipment or the environment surrounding the cable. This interference interacts with partial discharge (PD) within the cable. PD is a high-frequency, short-lived discharge caused by defects in the cable's insulation. By adjusting the distribution parameters, it's possible to analyze how the signal behaves when EMI and PD coexist. This characteristic is crucial for insulation testing, as cable insulation defects are often accompanied by EMI and PD. Capturing this coexistence allows for more accurate identification of insulation issues.

[0026] Furthermore, the mining of the first characteristic, step P20 of the embodiment of the present application also includes: P21: obtaining line simulation data, wherein the line simulation data is identified by 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 signal to mine the dynamic characteristic, performing spatial transmission analysis on the same-phase interval signal to mine the transmission characteristic.

[0027] Alternatively, the detailed process for mining the first characteristic (i.e., the dynamic and transmission characteristics of line signals) can be as follows: first, based on the established distributed line model, adjust distribution parameters (such as cable length, material properties, and environmental conditions) to perform line simulations in multiple scenarios, obtaining a series of line simulation data marked with line distribution parameters. This data forms the basis for subsequent analysis, detailing the propagation and variation patterns of line signals under different parameter configurations.

[0028] Next, these line simulation data undergo in-depth scenario clustering and spatiotemporal analysis. Scenario clustering aims to group simulation data with similar characteristics, allowing for clearer identification of the impact of different parameter configurations on line signals. Spatiotemporal analysis, on the other hand, considers the signal's temporal and spatial variations. For example, while cable signals transmit rapidly, they are still affected by factors such as time delay and spatial attenuation. Therefore, this spatiotemporal analysis allows for comprehensive exploration of the signal's dynamic and transmission characteristics from multiple perspectives.

[0029] Specifically, when exploring dynamic characteristics, we employ a time-phase fluctuation analysis method to analyze signals at the same location over time. By comparing changes in parameters such as signal strength and frequency at the same location at different time points, we can reveal the dynamic patterns of the signal. For example, we can observe signal fluctuation trends within a specific time period and differences in signal fluctuations under different parameter configurations.

[0030] When exploring transmission characteristics, spatial transmission analysis is performed on signals in simultaneous phase intervals. Although electrical signals propagate very quickly in cables, their transmission is still affected by factors such as line impedance, dielectric properties, and external interference, resulting in spatial attenuation and reflection of signals at different locations. By performing a detailed analysis of the signal's spatial transmission process at a reasonable time granularity (typically, the time interval is selected based on the signal transmission speed and analysis requirements), we can accurately explore signal transmission characteristics, such as signal delay, reflection characteristics, and attenuation patterns.

[0031] Furthermore, in mining the second characteristic, step P20 in this embodiment of the application further includes:

[0032] 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 pulsed electromagnetic interference exists, the defect position with electromagnetic penetration coexists with local discharge, and the defect position with local discharge coexists with pulsed electromagnetic interference; P26: Traverse the line simulation data, and based on the first coexistence type and the second coexistence type, explore the signal coexistence characteristics.

[0033] 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 location. Pulsed electromagnetic interference is a high-intensity electromagnetic signal that occurs suddenly 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 indicates that the location where electromagnetic interference occurs is often accompanied by local discharge caused by insulation defects inside the cable.

[0034] Next, we analyze the relationship between partial discharge (PD) and pulsed electromagnetic interference (PEI), focusing on the central location. PD typically occurs at insulation defects in cables, generating high-frequency signals and electromagnetic radiation. Conversely, the location of PD can also serve as a source or amplification area for PEMI. Therefore, we focus on the distribution of PEMI around PD, defining this phenomenon as the second coexistence type. This type reflects the fact that PD defects often lead to the penetration or amplification of PEMI.

[0035] To fully understand these coexistence phenomena, we traverse line simulation data and analyze the distribution of pulsed electromagnetic interference and partial discharge within the line one by one. Based on the first and second coexistence types, we identify the coexistence characteristics of the signals—that is, the correlation between electromagnetic interference and partial discharge. Specifically, if electromagnetic interference is present at a certain location, it is likely that there is also a defect in the cable insulation layer, leading to partial discharge. Conversely, if partial discharge is observed at a certain location, it is also likely to be accompanied by pulsed electromagnetic interference. Signal processing and analysis techniques, such as wavelet transform and Fourier transform, can be used to extract the signal's time and frequency domain features. Simultaneously, machine learning algorithms, such as cluster analysis and support vector machines, can be used to classify and identify the signals, thereby more accurately identifying the signal coexistence characteristics. These traversal analyses can further confirm the location of defects in the cable and provide a more accurate reference for subsequent insulation testing.

[0036] 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.

[0037] Specifically, after identifying the first characteristic (dynamic 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.

[0038] The front-end detection strategy refers to a set of automated rules or algorithms used to preliminarily identify and evaluate cable insulation performance 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 signals at specific locations in the line model, focusing on monitoring areas where partial discharge and electromagnetic interference may coexist. Since these areas are often potential locations for cable insulation defects, directional detection can improve the accuracy and efficiency of detection.

[0039] When developing a front-end detection strategy, first set the parameters of the detection equipment based on the primary characteristics (dynamic and transmission characteristics). For example, the detection equipment's filters and amplifiers can be configured based on the signal's frequency response and phase delay to ensure accurate capture of the target signal. Furthermore, the detection equipment's sensitivity can be adjusted based on signal attenuation to avoid misjudgments due to weak signals.

[0040] Secondly, the second characteristic (the coexistence of pulsed electromagnetic interference and partial discharge signals) is used to guide the detection equipment's detection direction. Because the distribution and interaction of pulsed electromagnetic interference and partial discharge in cables exhibit certain regularities, these patterns can be used to set the detection equipment's detection path and detection frequency. For example, in areas with pulsed electromagnetic interference, increasing the detection frequency and depth can more accurately identify potential partial discharge phenomena.

[0041] Finally, these parameters and direction settings are integrated into the automated control system of the testing equipment, achieving an automated and intelligent testing process. During the actual testing process, the testing equipment will automatically adjust the parameters and direction according to the preset strategy, performing a comprehensive detection and evaluation of the cable. The testing system also records and analyzes test data in real time, providing strong support for subsequent insulation performance evaluation and defect location.

[0042] To implement this strategy, advanced detection technologies and equipment are needed, such as high-frequency current sensors, ultrasonic sensors, and infrared thermal imagers. These devices provide high-precision and high-sensitivity detection capabilities, accurately capturing weak signals and anomalies in the cable. At the same time, advanced signal processing and analysis techniques, such as wavelet transforms and neural networks, are also needed to conduct in-depth analysis and mining of test data, thereby more accurately evaluating the cable's insulation performance and identifying potential defects.

[0043] 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, based on the first characteristic and the second characteristic, construct an insulation detection module. 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 this 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.

[0044] During supervised training, these features must first be extracted from line simulation data or actual test data. Appropriate feature selection methods (such as principal component analysis and mutual information analysis) are then used to reduce the number of features and improve the model's generalization capabilities. Next, an appropriate machine learning algorithm (such as a support vector machine, neural network, or decision tree) is selected to construct an insulation detection unit. This unit is then trained using the extracted features and the corresponding insulation performance evaluation results.

[0045] However, because cable signals often contain significant noise and interference, directly inputting these signals into the insulation detection module can result in degraded model performance. Therefore, a signal processing unit is required to pre-process the input signal within the insulation detection module. The signal processing unit's task is to remove noise, enhance signal features, and extract information useful for insulation performance evaluation. This can be achieved through filtering, denoising, and feature extraction.

[0046] Finally, the trained insulation detection unit and signal processing unit are combined to form a complete insulation detection module. This module receives the signal input from the cable, pre-processes it through the signal processing unit, and then uses the insulation detection unit to evaluate the insulation performance of the processed signal.

[0047] P50: 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 order is determined.

[0048] 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 signals, and these signals are preprocessed and insulation defects are located through the insulation detection module, thereby achieving effective processing of the received echo signals and accurate location of insulation defects.

[0049] Specifically, the front-end detector is the sensing device of the entire insulation detection system, responsible for real-time monitoring and receiving signals generated during cable transmission, particularly echo signals that may indicate insulation defects. To ensure efficient operation of the front-end detector, automated configuration is implemented based on the front-end detection strategy. This means that the detector adaptively adjusts its detection parameters, such as gain, sensitivity, and sampling frequency, to varying operating conditions (e.g., signal strength fluctuations, electromagnetic interference levels, and ambient temperature variations). This ensures optimal signal reception in complex environments. This automated configuration relies on previously identified primary and secondary characteristics, particularly the dynamic characteristics of the signal and the coexistence of electromagnetic interference and partial discharge. The detector automatically adjusts detection parameters based on the signal strength and characteristics received at different locations and times, ensuring efficient detection in target areas (e.g., areas where insulation defects may occur).

[0050] When the front-end detector receives the echo signal from the cable, it first undergoes a signal preprocessing stage. This is a crucial step in the insulation inspection module. Advanced signal processing algorithms (such as filtering, denoising, and feature extraction) are used to remove noise and interference from the signal while enhancing its useful features, providing clear and accurate signal input for subsequent insulation defect location.

[0051] After signal preprocessing, the insulation testing unit evaluates the insulation performance of the processed signal. Based on signal characteristics (such as changes in dynamic characteristics, differences in transmission characteristics, and the coexistence of pulsed electromagnetic interference and partial discharge), the unit determines the cable's insulation performance and identifies potential insulation defects. The unit then identifies the cable segments or specific locations where insulation defects exist, and generates an insulation test report. This system-generated report contains the insulation test results, specifically the defect type, location, and severity. This report serves as a crucial basis for cable maintenance and repair, providing detailed test information to operators.

[0052] Furthermore, the signal preprocessing step P50 in the embodiment of the present application further includes:

[0053] 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.

[0054] Specifically, signal preprocessing requires special consideration of electromagnetic interference within the cable. When the cable jacket has insulation defects (such as cracks, damage, or aging), external electromagnetic interference signals generate an electromagnetic field around the cable. These electromagnetic fields penetrate the cable through the insulation defects, inducing currents within the cable. These currents are then used for insulation testing. However, the presence of electromagnetic fields can also interfere with detection signals, causing distortion in the echo signal. Therefore, the key to signal preprocessing lies in effectively processing the echo signal based on these interference characteristics.

[0055] First, it's necessary to detect the interference electromagnetic field surrounding the cable. This field is typically generated by external devices or the environment. Insulation defects in the cable jacket allow the field to penetrate the cable, generating electromagnetic interference signals. These interference signals can be mixed with the normal signals within the cable, making signal detection challenging. Therefore, accurately detecting the interference electromagnetic field is the first step in signal preprocessing. This can be achieved using electromagnetic detectors placed around the cable.

[0056] After acquiring the interfering electromagnetic field, the interference characteristics of these interference signals need to be analyzed. 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 cable signal transmission can be determined and first interference information can be generated. For example, when the interference signal intensity in the cable is high, it may cause significant distortion of the echo signal or overlap the partial discharge signal.

[0057] Next, the second attenuation information is determined based on the position of the front-end detector and the echo distance from the detection point within the cable. Signals in cables gradually attenuate with increasing transmission distance, especially in the presence of electromagnetic interference, where signal attenuation is more pronounced. Therefore, by accurately calculating the signal's transmission distance, the attenuation of the signal during transmission can be determined. This information serves as the basis for the attenuation characteristics used to generate the second attenuation information. This process is typically analyzed using a signal transmission attenuation model, combined with the actual position of the front-end detector and the received echo signal for inference.

[0058] Finally, the received echo signal undergoes signal preprocessing, 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 caused by attenuation. This step typically involves filtering algorithms and signal recovery techniques, such as using adaptive filters to remove electromagnetic interference or deconvolution techniques to restore attenuated signals. After preprocessing, signal quality is improved, ensuring more accurate location of insulation defects.

[0059] Furthermore, step P54 of the embodiment of the present application further includes:

[0060] 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.

[0061] Optionally, signal clarity and accuracy can be improved through signal spectrum analysis and processing, ensuring 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 and frequency domains. It shows the signal's temporal changes and frequency distribution, facilitating subsequent processing and analysis. The signal spectrum clearly displays characteristics such as the signal waveform, frequency distribution, and phase changes.

[0062] After obtaining the signal spectrum, interference filtering and attenuation compensation are performed on the signal spectrum based on the previously extracted first interference information (electromagnetic interference characteristics) and second attenuation information (signal attenuation characteristics). Interference filtering uses techniques such as adaptive filtering to effectively filter out interference components in the signal based on the characteristics of the interfering electromagnetic field (such as frequency and intensity), thereby removing or reducing the impact of electromagnetic interference on the signal. Attenuation compensation addresses the attenuation caused by factors such as distance during signal transmission by restoring the original signal strength through a compensation algorithm, restoring the signal to a near-initial state. After this processing step, the generated initial signal spectrum is an interference- and attenuation-corrected spectrum, ensuring signal clarity and accuracy.

[0063] Next, the initialized signal spectrum is traversed, and the waveform clarity is determined based on signal feature recognition criteria (such as waveform amplitude, frequency, and phase). Waveform clarity refers to the clarity and recognizability of the waveform distribution within the signal spectrum. To improve this clarity, phase-frequency information is set, dividing and identifying signal segments based on their phase characteristics. This information allows for more accurate identification of signal components and features, providing a basis for subsequent signal processing.

[0064] Finally, based on the generated phase frequency conversion information, the initialized signal spectrum is located and phase expanded. Phase expansion involves amplifying certain signal segments with frequent fluctuations or similar waveforms to improve the clarity of these areas. For example, the phase range of 2 / π-π in a signal segment can be amplified and expanded to 2 / π-3 / 4π and 3 / 4π-π, thereby making previously difficult-to-distinguish signal segments clearer. This phase expansion process ensures that all key signal segments in the signal spectrum can be accurately identified, thereby generating the final preprocessed signal and providing high-quality signal data support for subsequent insulation defect location.

[0065] Furthermore, in the insulation defect location, step P50 in the embodiment of the present application further includes:

[0066] P55: Obtain the pre-processed first signal class and 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; P56: Map the first signal class and the second signal class, determine the mapped signal pair and the 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 list.

[0067] 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 pre-processed first signal type and second signal type are obtained. The first signal type is an induced electrical signal based on electromagnetic penetration. This type of signal is usually caused by the external electromagnetic field penetrating 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 due to aging, damage or moisture of the insulation material inside the cable. The pre-processing results of these two types of signals can be obtained by receiving and processing them by the front-end detector.

[0068] Next, the first and second signal types are mapped to determine mapped signal pairs and non-mapped signals. A mapped signal pair is one in which both the induced electrical signal based on electromagnetic penetration and the signal based on partial discharge can be detected at the same or similar location. These signal pairs verify each other, improving the accuracy and reliability of insulation defect location. Non-mapped signals are those in which only one of the signals is detected. This may occur due to interference, attenuation, or detector performance, preventing the simultaneous detection of the other signal. Non-mapped signals include either the first signal (only the electromagnetic penetration signal) or the second signal (only the partial discharge signal), requiring further verification to determine whether an insulation defect exists.

[0069] Finally, based on the mapped signal pairs, insulation inspection information is determined and added to the insulation inspection form. This information includes key information such as the location, size, and type of insulation defects, which is crucial for subsequent cable maintenance, repair, and replacement. By cross-verifying the mapped signal pairs, the presence and characteristics of insulation defects can be more accurately determined, providing strong assurance for the safe operation of the cable. Furthermore, non-mapped signals require further verification and analysis to determine whether they truly represent insulation defects.

[0070] Furthermore, the embodiment of the present application further includes step P58, which further includes:

[0071] 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 meets the first coexistence type or the second coexistence type, generate insulation detection information and add it to the insulation detection form.

[0072] 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, which 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, only one of them can be detected sometimes. 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.

[0073] 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. This verification can more accurately determine whether the non-mapped signals are truly caused by insulation defects, avoiding false positives or negatives.

[0074] If the verification result meets the characteristics of the first or second coexistence type, the unmapped signal is deemed to represent an insulation defect. Corresponding insulation inspection information, including key information such as the defect's location, possible cause, and severity, is generated and added to the insulation inspection form. If the verification result does not meet the coexistence type requirements, the unmapped signal is likely merely interference or a minor anomaly, not a serious defect. The system will mark it as "To be observed" or "Minor anomaly" for further monitoring and tracking. This information is crucial for subsequent cable maintenance, repair, and replacement.

[0075] P60: Visualize the insulation inspection form on the distribution line model for terminal interface display and early warning.

[0076] Specifically, the data from the insulation inspection form (such as the location, type, and severity of insulation defects) is first linked to the distribution line model. The distribution line model is a digital representation of the cable line, accurately reflecting key information such as the cable route, branches, and joints. By combining insulation inspection data with this model, it is possible to clearly identify insulation performance issues and their severity.

[0077] Next, visualization technology is used to graphically display the information from the insulation inspection form on the terminal interface. This typically involves using different colors, icons, or lines to represent different types of insulation defects, and using dynamic effects to illustrate the changing trends of defects. This presentation is not only intuitive and easy to understand, but also helps users quickly identify the key issues and make accurate decisions.

[0078] Based on the severity of the detected defects and the coexistence of the signals, if a high-risk insulation defect is detected in a particular area (e.g., detection of a mapped signal pair), the system triggers an early warning mechanism. This warning mechanism prompts maintenance personnel to take immediate action through visual alerts (such as highlighting the defect location or changing its color) and audio cues. Furthermore, the warning information can be transmitted to a management center via the network for remote monitoring and decision-making.

[0079] 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 promptly, thereby improving the efficiency and accuracy of cable fault detection and ensuring the safe operation of the cable.

[0080] In summary, the embodiments of the present application have at least the following technical effects:

[0081] 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 issues warnings on the terminal interface, and realizes efficient insulation monitoring.

[0082] 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.

[0083] Embodiment 2 is based on the same inventive concept as the cable jacket insulation detection method using electromagnetic interference in the above embodiment. Figure 2 As shown, the present application provides a device for detecting the insulation of a cable jacket using electromagnetic interference. The device and method embodiments in the present application are based on the same inventive concept. The device includes:

[0084] 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.

[0085] 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.

[0086] The front-end detection strategy configuration module 13 is 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.

[0087] 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 processing unit to construct an insulation detection module.

[0088] 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.

[0089] The visual warning module 16 is used to visualize the insulation test sheet on the distribution line model to perform terminal interface display and warning.

[0090] Furthermore, the signal feature mining module 12 is further configured to perform the following steps:

[0091] Acquire line simulation data, wherein the line simulation data is identified by 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 co-location signals to mine the dynamic characteristic, and perform spatial transmission analysis on co-phase interval signals to mine the transmission characteristic.

[0092] Furthermore, the signal feature mining module 12 is further configured to perform the following steps:

[0093] Taking pulsed electromagnetic interference as the center position, the distribution of partial discharge is the first coexistence type; taking partial discharge as the center position, the distribution of pulsed electromagnetic interference is the second coexistence type, wherein, if pulsed electromagnetic interference exists, the defect position with electromagnetic penetration coexists with partial discharge, and the defect position with partial discharge coexists 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.

[0094] Furthermore, the insulation test list determination module 15 is further configured to perform the following steps:

[0095] Acquire an interference electromagnetic field; based on the interference 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; and perform signal preprocessing based on the first interference information and the second attenuation information.

[0096] Furthermore, the insulation test list determination module 15 is further configured to perform the following steps:

[0097] 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.

[0098] Furthermore, the insulation test list determination module 15 is further configured to perform the following steps:

[0099] Acquire 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 the first signal and the second signal; determine insulation detection information based on the non-mapped signal and add it to the insulation detection list.

[0100] Furthermore, the insulation test list determination module 15 is further configured to perform the following steps:

[0101] 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 a verification result; if the verification result meets the first coexistence type or the second coexistence type, generate insulation detection information and add it to the insulation detection sheet.

[0102] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0103] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection 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 is 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, configured to simulate and mine a first characteristic and a second characteristic based on the distributed line model by adjusting distribution parameters, wherein the first characteristic is the dynamic and transmission characteristics of the line signal, and the second characteristic is the coexistence characteristic of pulsed electromagnetic interference and partial discharge signals; 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, configured to supervise and train the insulation detection unit based on the first characteristic and the second characteristic, and to pre-place a signal processing unit to construct an insulation detection module; An insulation inspection order 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 order; The visual warning module is used to visualize the insulation detection sheet on the distribution line model to perform terminal interface display and warning.

2. The cable jacket insulation detection device using electromagnetic interference according to claim 1, characterized in that: The mining of the first characteristic includes: Acquiring line simulation data, where the line simulation data is identified by line distribution parameters; performing scene clustering processing and spatiotemporal analysis on the line simulation data to mine the first characteristic; Among them, a phase fluctuation analysis is performed on the signals at the same position to explore the dynamic characteristics, and a spatial transmission analysis is performed on the signals at the same phase interval to explore the transmission characteristics.

3. The cable jacket insulation detection device using electromagnetic interference according to claim 2, characterized in that: Exploiting secondary characteristics, including: The first coexistence type is based on the distribution of partial discharge and the pulsed electromagnetic interference as the central location. The second coexistence type is based on the distribution of pulsed electromagnetic interference and the central position of partial discharge. If pulsed electromagnetic interference exists, the defect position with electromagnetic penetration coexists with partial discharge, and the defect position with partial discharge coexists with pulsed 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 jacket insulation detection device using electromagnetic interference according to claim 1, wherein: The signal preprocessing comprises: Acquiring interference electromagnetic fields; performing interference characteristic analysis of signal transmission based on the interfering electromagnetic field to determine first interference information; 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 jacket insulation detection device using electromagnetic interference according to claim 4, characterized in that: Performing signal preprocessing based on the first interference information and the second attenuation information includes: Receive echo signals and convert them into signal spectra; performing interference filtering and attenuation compensation on the signal spectrum based on the first interference information and the second attenuation information 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 phase frequency conversion information, wherein the phase frequency conversion information is identified by a signal segment; Based on the phase frequency conversion information, positioning and phase expansion processing are performed on the initialization signal spectrum to determine a preprocessing signal.

6. The cable jacket insulation detection device using electromagnetic interference according to 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 partial 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 the first signal and the second signal; Based on the non-mapped signal, insulation detection information is determined and added to the insulation detection list.

7. The cable jacket insulation detection device using electromagnetic interference according to 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 sheet.

8. A method for detecting the insulation of a cable jacket 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 explored by adjusting distribution parameters for simulation, wherein the first characteristic is the dynamic and transmission characteristics of the line signal, and the second characteristic is the coexistence characteristic of pulsed electromagnetic interference and partial discharge signals; 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 a 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 order is determined; The insulation inspection sheet is visualized on the distribution line model for terminal interface display and early warning.

Images