Cable defect detection device and detection method

Abstract

The invention discloses a cable defect detection device and method. The detection device comprises a high-frequency current sensor, a signal acquisition module, an algorithm analysis module, a high-frequency pulse detection module and a fault diagnosis module, the high-frequency current sensor is used for detecting a current pulse signal on a cable grounding wire, converting the current pulse signal into a voltage signal and transmitting the voltage signal to the signal acquisition module; the signal acquisition module is used for amplifying, filtering and detecting the voltage signal; the algorithm analysis module is used for carrying out feature extraction and analysis on the processed voltage signals and carrying out feature classification and clustering on the voltage signals; the high-frequency pulse detection module is used for carrying out noise reduction processing on the voltage signal and extracting the voltage signal representing the partial discharge defect of the cable; and the fault diagnosis module is used for determining the type and degree of the cable defect according to the voltage signal representing the partial discharge defect of the cable and historical data. Cable line faults are rapidly judged, and the reliability of a power supply system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power cable detection, and in particular to a cable defect detection device and a detection method. Background Art

[0002] Power cables generally have problems such as insulation aging and deterioration, cable joint overheating, mechanical damage to the protective layer, and cable failure caused by breakdown due to harmonics and overvoltage. Cable accidents often cause certain losses.

[0003] The traditional maintenance mode of cables has always been regular power outage maintenance, which requires the organization of a large amount of manpower and material resources. This maintenance method is time-consuming and labor-intensive, prone to misoperation accidents, low maintenance efficiency and high maintenance costs, and will delay power supply time, causing economic losses and social impacts to the power system. Summary of the invention

[0004] The present invention provides a cable defect detection device and a detection method to solve the problems of low maintenance efficiency and high safety risk in a traditional maintenance mode.

[0005] In a first aspect, the present invention provides a cable defect detection device, the detection device comprising: a high-frequency current sensor, a signal acquisition module, an algorithm analysis module, a high-frequency pulse detection module and a fault diagnosis module;

[0006] The high-frequency current sensor is arranged on the cable grounding wire, and the signal acquisition module is connected to the high-frequency current sensor. The high-frequency current sensor is used to detect the current pulse signal on the cable grounding wire and convert it into a voltage signal and transmit it to the signal acquisition module; the signal acquisition module is used to amplify, filter and detect the voltage signal;

[0007] The algorithm analysis module is connected to the signal acquisition module, and is used to extract and analyze the features of the processed voltage signal, and to perform feature classification and clustering on the voltage signal;

[0008] The high-frequency pulse detection module is connected to the algorithm analysis module to perform noise reduction processing on the voltage signal and extract the voltage signal that characterizes the partial discharge defect of the cable;

[0009] The fault diagnosis module is connected to the high-frequency pulse detection module and is used to determine the type and degree of the cable defect based on the voltage signal characterizing the cable partial discharge defect and historical data.

[0010] Optionally, the detection device further includes: a fault location module;

[0011] The fault location module is connected to the high-frequency pulse detection module, and the fault location module is used to determine the location of the cable defect by using a pulse reflection method.

[0012] Optionally, the detection device further includes: a signal processing module;

[0013] The signal processing module is connected between the signal acquisition module and the algorithm analysis module, and is used for time-marking the voltage signal processed by the signal acquisition module.

[0014] Optionally, the detection device further includes: a data processing module;

[0015] The data processing module is connected between the signal acquisition module and the algorithm analysis module, and is used for performing polarity identification on the voltage signal processed by the signal acquisition module.

[0016] Optionally, the data processing module is also used to calculate the discharge amount of the cable based on the voltage signal processed by the signal acquisition module, and determine whether a partial discharge defect occurs in the cable based on the value of the discharge amount, and after determining that a partial discharge defect occurs in the cable, transmit the voltage signal to the algorithm analysis module.

[0017] Optionally, the detection device further includes: a fault warning module;

[0018] The fault warning module is connected to the fault diagnosis module and the fault location module, and is used to generate warning information according to the defect type and degree determined by the fault diagnosis module and the defect location determined by the fault location module, and upload the warning information to the remote terminal.

[0019] Optionally, the signal acquisition module is connected to the high-frequency current sensor via a BNC coaxial cable.

[0020] In a second aspect, the present invention provides a cable defect detection method, wherein the detection method is applied to a cable defect detection device, the detection device comprising: a high-frequency current sensor, a signal acquisition module, an algorithm analysis module, a high-frequency pulse detection module and a fault diagnosis module; the high-frequency current sensor is arranged on the cable grounding wire, the signal acquisition module is connected to the high-frequency current sensor; the algorithm analysis module is connected to the signal acquisition module; the high-frequency pulse detection module is connected to the algorithm analysis module; the fault diagnosis module is connected to the high-frequency pulse detection module; the detection method comprises:

[0021] The high-frequency current sensor detects the current pulse signal on the cable grounding wire and converts it into a voltage signal;

[0022] The signal acquisition module is used to amplify, filter and detect the voltage signal;

[0023] The algorithm analysis module extracts and analyzes the features of the processed voltage signal, and performs feature classification and clustering on the voltage signal;

[0024] The high-frequency pulse detection module performs noise reduction processing on the voltage signal and extracts the voltage signal that represents the partial discharge defect of the cable;

[0025] The fault diagnosis module determines the type and extent of the cable defect based on the voltage signal characterizing the cable partial discharge defect and historical data.

[0026] Optionally, the detection device further includes a fault location module, which is connected to the high-frequency pulse detection module; after the fault diagnosis module determines the type and degree of the cable defect according to the voltage signal characterizing the cable partial discharge defect and the historical data, it also includes:

[0027] The fault location module uses the pulse reflection method to determine the location of cable defects.

[0028] Optionally, the detection device further includes a fault warning module, which is connected to the fault diagnosis module and the fault location module; after the fault location module determines the location of the cable defect using a pulse reflection method, it further includes:

[0029] The fault warning module generates warning information according to the defect type and degree determined by the fault diagnosis module and the defect location determined by the fault location module, and uploads the warning information to the remote terminal.

[0030] The technical solution of the embodiment of the present invention can perform partial discharge detection when the cable is running under power, and based on the statistical analysis algorithm of big data samples, the high-frequency pulse detection technology is used to comprehensively collect and analyze the data in the cable operation. Through the acquisition and processing of the high-frequency pulse current signal by the signal acquisition module, a complete link from the original signal to the analyzable data is constructed, and the key information of the cable under the power state is effectively extracted, so that the cable line fault can be quickly judged without affecting the normal power supply of the cable, the fault repair time can be shortened, and the reliability of the power supply system can be improved. It breaks through the limitation that the operation and maintenance of traditional power cables depends on planned power outages, and the detection can be completed without power outages. It greatly improves the operation and maintenance efficiency and convenience, effectively improves the operation and maintenance level of the distribution network, and reduces the manpower and material consumption, economic losses and social impact caused by power outages. At the same time, the technical solution of the embodiment of the present invention integrates the intelligent cable state discrimination technology, and establishes a database covering various types and degrees of partial discharge through a large number of field experimental simulations. Using this database, the system can extract the spectral characteristic signal, accurately identify the fault type and defect degree, realize the automatic identification and analysis of the cable operation state, and quickly and accurately judge whether the cable has insulation defects and partial discharge problems. This makes the operation and maintenance of the entire system more convenient and efficient, changing the situation where traditional detection technology cannot perform intelligent analysis of cable defect status. The detection device has a series of functions such as real-time detection and data analysis, providing comprehensive, accurate and fast troubleshooting and repair support for operation and maintenance personnel. Operation and maintenance personnel can grasp the operating status of the cable in a timely manner and take corresponding measures at the early stage of the fault or potential fault stage, effectively avoiding further deterioration of the fault and making up for the deficiency of existing traditional cable fault detection technology that cannot detect and analyze in real time.

[0031] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] 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.

[0033] Figure 1 is a structural schematic diagram of a cable defect detection device provided by an embodiment of the present invention;

[0034] Figure 2 is a structural schematic diagram of a high-frequency current sensor provided by an embodiment of the present invention;

[0035] Figure 3 It is a flow chart of a BP neural network algorithm program implementation method provided by an embodiment of the present invention;

[0036] Figure 4 is a structural schematic diagram of another cable defect detection device provided by an embodiment of the present invention;

[0037] Figure 5 It is a schematic diagram of a discharge pulse transmission after partial discharge occurs in a cable provided by an embodiment of the present invention;

[0038] Figure 6 is a structural schematic diagram of another cable defect detection device provided by an embodiment of the present invention;

[0039] Figure 7 It is a flow chart of a cable defect detection method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention 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 interchanged where appropriate, so that the embodiments of the present invention 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 device 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 units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] Figure 1 is a structural schematic diagram of a cable defect detection device provided by an embodiment of the present invention, Figure 2 is a schematic diagram of the structure of a high-frequency current sensor provided by an embodiment of the present invention, such as Figure 1 and Figure 2 As shown, the detection device includes: a high-frequency current sensor 1, a signal acquisition module 2, an algorithm analysis module 3, a high-frequency pulse detection module 4 and a fault diagnosis module 5. The high-frequency current sensor 1 is arranged on the cable grounding wire, and the signal acquisition module 2 is connected to the high-frequency current sensor 1. The high-frequency current sensor 1 is used to detect the current pulse signal on the cable grounding wire, and convert it into a voltage signal and transmit it to the signal acquisition module 2. The signal acquisition module 2 is used to amplify, filter and detect the voltage signal. The algorithm analysis module 3 is connected to the signal acquisition module 2, and is used to extract and analyze the features of the processed voltage signal, and to classify and cluster the features of the voltage signal. The high-frequency pulse detection module 4 is connected to the algorithm analysis module 3, and is used to perform noise reduction processing on the voltage signal and extract the voltage signal that characterizes the partial discharge defect of the cable 10. The fault diagnosis module 5 is connected to the high-frequency pulse detection module 4, and is used to determine the type and degree of the defect of the cable 10 according to the voltage signal that characterizes the partial discharge defect of the cable 10 and historical data.

[0043] Specifically, when the cable 10 is energized, the cable 10 is detected in real time by a high-frequency current detection method. When local discharge occurs in the cable 10, a current pulse signal is usually generated on the cable grounding wire. The high-frequency current sensor 1 is arranged on the cable grounding wire. The cable defects are detected by detecting the magnitude of the electromotive force induced in the high-frequency current sensor 1 by detecting the magnetic field.

[0044] like Figure 2As shown, the high-frequency current sensor 1 is a wide-band current sensor based on a Rogowski coil structure, with a ring oxide core F1 as the primary side and a multi-coil M as the secondary side. When a high-frequency current pulse flows through the coil, an alternating magnetic flux appears on the primary side, and the magnetic flux induced by each turn of the coil on the secondary side is proportional to the magnitude of the pulse current. The change in magnetic flux generates an electromotive force on the secondary side, which is proportional to the magnitude of the pulse current.

[0045] like Figure 2 As shown, an integral resistor R1 is connected in parallel to the output end of the self-integrating Rogowski coil, and a signal proportional to the current i in the current-carrying conductor can be obtained. Rs is the equivalent resistance of the coil, Cs is the equivalent stray capacitance of the coil, and R2 is the integral resistance of the coil, which together with the equivalent inductance Ls of the coil form an integral circuit. Therefore. The sensitivity of the high-frequency current sensor 1 is inversely proportional to the number of coil turns and proportional to the integral resistor R2. The frequency band response of the high-frequency current sensor 1 is related to the integral resistor R2, the equivalent inductance Ls and the equivalent resistance Rs. After the size and material of the magnetic core are determined, the equivalent inductance Ls is only related to the number of coil turns. Increasing the number of coil turns and reducing the integral resistor R2 can expand the frequency band of the high-frequency current sensor 1.

[0046] The high-frequency current sensor 1 detects the current pulse signal on the cable grounding wire and converts it into a voltage signal. The signal acquisition module 2 amplifies, filters and detects the voltage signal. The algorithm analysis module 3 uses statistical and machine learning methods to analyze the characteristics and laws of the partial discharge spectrum based on big data and algorithms, and classifies and clusters the defect information of the cable 10 to feed it back to the high-frequency pulse detection module 4. The high-frequency pulse detection module 4 uses the high-frequency current detection method combined with the wavelet noise reduction technology to improve the detection accuracy of the processed and analyzed voltage signal, and extracts the characteristic signal that can effectively identify the partial discharge defect of the live cable 10 when the cable 10 is energized, further improving the accuracy and reliability of the defect detection of the cable 10. The fault diagnosis module 5 receives the analysis results and detection data, and judges the fault type and degree of the cable 10 in combination with the historical data in the database.

[0047] Artificial neural network (ANNS) is introduced as the core tool in algorithm analysis module 3 and fault diagnosis module 5. ANNS is composed of many processing units that simulate biological neurons, forming a highly nonlinear and complex network system. Through precise mathematical models, the structure of biological neural networks is simulated, and under the guidance of advanced algorithms, ANNS is able to simulate biological intelligent behaviors to solve the intelligent information processing problems that are difficult to handle with traditional algorithms. As the basis for massive information parallel processing and large-scale parallel computing, ANNS is both a highly nonlinear dynamic system and a self-organizing and adaptive system, suitable for describing intelligent behaviors such as cognition, decision-making and control.

[0048] The Back Propagation (BP) neural network algorithm has significant advantages. The BP neural network algorithm can fully train the ANNS and improve the performance of the ANNS. The BP neural network algorithm has a high-speed information processing ability, a large knowledge storage capacity, a strong ability to process uncertain information, and shows extremely strong robustness. These characteristics enable the BP neural network algorithm to exhibit excellent performance when dealing with complex and large-scale data sets. Figure 3 is a flowchart of a method for implementing a BP neural network algorithm program provided by an embodiment of the present invention, as Figure 3 shown, the method for implementing a BP neural network algorithm program includes:

[0049] S100: Initialize the weight matrices V and W, and initialize the counters i and p to 1.

[0050] Specifically, first initialize the weight matrices V and W, which are used to connect neurons in different layers in the neural network. At the same time, initialize both the counters i and p to 1. Here, i and p may be used to record the number of iterations and the sample number respectively.

[0051] S110: Input a sample and calculate the outputs of each layer.

[0052] Specifically, input a sample data, and calculate the outputs of each layer of the neural network through forward propagation according to the current weight matrices V and W. This step starts from the input layer and passes through the hidden layer in turn, and finally obtains the result of the output layer.

[0053] S120: Calculate the error E.

[0054] Specifically, calculate the error E according to the output of the neural network and the expected output (actual label). The error is usually calculated using methods such as the Mean Squared Error (MSE).

[0055] S130: Determine whether p is less than the total number of samples P.

[0056] Specifically, check whether the current sample number p is less than the total number of samples P. If p < P, then p is incremented by 1 (i.e., process the next sample), and then return to S110 to continue processing the next sample. If p ≥ P, then execute S141.

[0057] S141: Calculate the error signals of each layer.

[0058] Specifically, calculate the error signals of each layer of the neural network through the backpropagation algorithm according to the error E. This step starts from the output layer and calculates the error of each layer in reverse to update the weights.

[0059] S142: Increment p by 1.

[0060] Specifically, if p < P, then p is incremented by 1 (i.e., the next sample is processed), and then S110 is returned to continue processing the next sample.

[0061] S150: Adjust the weights of each layer.

[0062] Specifically, according to the calculated error signals of each layer, the weight matrices V and W are adjusted. This step updates the weights through optimization algorithms such as gradient descent to reduce the error.

[0063] S160: Determine whether the error E is less than the minimum error Emin.

[0064] Specifically, check whether the current error E is less than the preset minimum error Emin. If E < Emin, then S171 is executed and the algorithm ends, indicating that the neural network has converged to a satisfactory result. If E ≥ Emin, then the sample number p is reset to 1, the iteration number i is incremented by 1, and then S110 is returned to restart processing the samples for the next iteration. The BP neural network algorithm adjusts the weights through continuous iteration to make the output of the neural network as close as possible to the actual label, thereby achieving the learning and prediction of data.

[0065] S171: The algorithm ends.

[0066] S172: Set p to 1 and increment i by 1.

[0067] Specifically, if E ≥ Emin, then the sample number p is reset to 1, the iteration number i is incremented by 1, and then S110 is returned to restart processing the samples for the next iteration.

[0068] Through the deep integration of the statistical analysis model and the neural network algorithm, the efficiency and accuracy of cable defect detection have been successfully improved, providing a solid technical support for the comprehensive monitoring of cable status.

[0069] The technical solution of the embodiment of the present invention can perform partial discharge detection when the cable is running under power, and based on the statistical analysis algorithm of big data samples, the high-frequency pulse detection technology is used to comprehensively collect and analyze the data in the cable operation. Through the acquisition and processing of the high-frequency pulse current signal by the signal acquisition module, a complete link from the original signal to the analyzable data is constructed, and the key information of the cable under the power state is effectively extracted, so that the cable line fault can be quickly judged without affecting the normal power supply of the cable, the fault repair time can be shortened, and the reliability of the power supply system can be improved. It breaks through the limitation that the operation and maintenance of traditional power cables depends on planned power outages, and the detection can be completed without power outages. It greatly improves the operation and maintenance efficiency and convenience, effectively improves the operation and maintenance level of the distribution network, and reduces the manpower and material consumption, economic losses and social impact caused by power outages. At the same time, the technical solution of the embodiment of the present invention integrates the intelligent cable state discrimination technology, and establishes a database covering various types and degrees of partial discharge through a large number of field experimental simulations. Using this database, the system can extract the spectral characteristic signal, accurately identify the fault type and defect degree, realize the automatic identification and analysis of the cable operation state, and quickly and accurately judge whether the cable has insulation defects and partial discharge problems. This makes the operation and maintenance of the entire system more convenient and efficient, changing the situation where traditional detection technology cannot perform intelligent analysis of cable defect status. The detection device has a series of functions such as real-time detection and data analysis, providing comprehensive, accurate and fast troubleshooting and repair support for operation and maintenance personnel. Operation and maintenance personnel can grasp the operating status of the cable in a timely manner and take corresponding measures at the early stage of the fault or potential fault stage, effectively avoiding further deterioration of the fault and making up for the deficiency of existing traditional cable fault detection technology that cannot detect and analyze in real time.

[0070] Optionally, based on the above embodiments, Figure 4 is a schematic structural diagram of another cable defect detection device provided by an embodiment of the present invention, Figure 5 Schematic diagram of a discharge pulse transmission after partial discharge occurs in a cable provided by an embodiment of the present invention, such as Figure 4 As shown, the detection device also includes: a fault location module 6 and a fault warning module 7. The fault location module 6 is connected to the high-frequency pulse detection module 4, and the fault location module 6 is used to determine the location of the defect of the cable 10 by using a pulse reflection method. The fault warning module 7 is connected to the fault diagnosis module 5 and the fault location module 6, and is used to generate warning information according to the defect type and degree determined by the fault diagnosis module 5 and the defect location determined by the fault location module 6, and upload the warning information to the remote terminal.

[0071] Specifically, the fault location module 6 can use a variety of positioning methods to analyze the traveling wave signal to determine the defect location of the cable 10, and use the single-ended positioning technology to locate multiple partial discharge points. The high-frequency pulse detection module 4 sends the processed and analyzed voltage signal to the fault potential module 6, and the fault location module 6 can use the pulse reflection method to determine the location of the partial discharge defect of the cable 10. Figure 5 As shown, the pulse reflection principle is that after partial discharge occurs in the cable 10, the discharge pulse propagates to both ends of the cable 10 at the same time, one of which is transmitted to the detection device as the incident pulse Q1, and the other discharge pulse is transmitted to the far end of the cable 10 as the reflected pulse Q2, and then transmitted to the detection device through reflection. The fault location module 6 calculates the time difference Δt between the incident pulse Q1 and the reflected pulse Q2, and calculates the location of the partial discharge fault according to the time difference Δt:

[0072]

[0073] Wherein, t1 is the transmission time of the incident pulse Q1, x is the transmission distance of the incident pulse Q1, v is the transmission speed of the incident pulse Q1 or the reflected pulse Q2, l is the length of the cable 10, and the fault location module 6 can obtain the transmission speed v of the incident pulse Q1 or the reflected pulse Q2 according to the wave propagation constant and the impedance characteristic of the cable 10. After receiving the incident pulse Q1 and the reflected pulse Q2, the fault location module 6 can calculate the time difference Δt, and the fault location module 6 can calculate the transmission distance of the incident pulse Q1 according to the transmission speed v of the incident pulse Q1 or the reflected pulse Q2 and the time difference Δt, so as to obtain the location of the partial discharge fault of the cable 10.

[0074] When a power cable fails, the traditional manual troubleshooting of the fault point is difficult and has errors. The traditional cable fault location technology can also pressurize the power-off cable and use the high-voltage oscillation wave reflection principle to determine the position, but repeated pressurization will cause the cable insulation to be damaged, and the positioning accuracy is not high. The technical solution of the embodiment of the present invention, the fault location module 6 can realize the defect location of the cable in the energized state, without the need to pressurize the cable 10 as in the traditional way. This non-destructive testing method avoids damage to the structure of the cable 10. By deeply analyzing the propagation time and amplitude changes of the traveling wave signal, the location of the defect of the cable 10 can be accurately found. This function provides precise guidance for maintenance and processing work, helps operation and maintenance personnel quickly lock the fault point, greatly shortens the time for troubleshooting, reduces costs, and overcomes the problem that the traditional cable fault location technology is prone to insulation damage and low positioning accuracy when the power-off cable is pressurized.

[0075] The fault warning module 7 receives the diagnosis and positioning information, monitors and prevents the partial discharge of the cable 10 in real time, and generates warning information and uploads it to the remote terminal. The warning information may include the type, degree and location of the fault. The fault warning module 7 can timely monitor and prevent the zero-sequence grounding and other warning signals caused by the potential cable 10 fault in real time.

[0076] Traditional cable fault detection technology is unable to provide fault warning. The technical solution of the embodiment of the present invention realizes efficient detection, diagnosis, positioning and warning of power cables in a live state through the collaborative work of multiple modules, and quickly determines the location of the faulty cable line and the distance between the fault point and the cable line terminal, greatly shortening the fault detection time, quickly repairing cable faults, and improving the reliability of the power supply system.

[0077] Optionally, based on the above embodiments, Figure 6 is a schematic diagram of the structure of another cable defect detection device provided by an embodiment of the present invention. Figure 6 As shown, the monitoring device also includes: a signal processing module 8 and a data processing module 9. The signal processing module 8 is connected between the signal acquisition module 2 and the algorithm analysis module 3, and is used to time-mark the voltage signal processed by the signal acquisition module 2. The data processing module 9 is connected between the signal acquisition module 2 and the algorithm analysis module 3, and is used to identify the polarity of the voltage signal processed by the signal acquisition module 2.

[0078] Specifically, the signal processing module 8 receives the voltage signal processed by the signal acquisition module 2. The signal processing module 8 adds a unique and accurate time tag to each pulse waveform data, which can accurately record the occurrence time of each pulse signal, so as to facilitate the subsequent time series analysis and processing of the signal. In the process of processing and analyzing multiple groups of data, the signal can be synchronized and connected in parallel according to the time tag of the signal, which is helpful to more accurately analyze the changing law and characteristics of the signal, as well as the time sequence of the fault. The data processing module 9 receives the voltage signal processed by the signal processing module 8. The data processing module 9 judges and distinguishes the polarity of the received voltage signal. The direction and characteristics of the partial discharge pulse can be determined by identifying the polarity of the signal, so as to more accurately analyze and judge the partial discharge situation and improve the accuracy and reliability of the measurement. Polarity identification in the data processing module 9 can remove some interference signals, better identify effective signals, and provide more accurate data for subsequent signal processing and analysis.

[0079] The technical solution of the embodiment of the present invention is to couple the pulse current signal at the joint of the cable 10 through the high-frequency current sensor 1 installed on the cross-connection line, and the coupled pulse signal is transmitted to the signal acquisition unit 2 through the coaxial cable, and the analog signal is amplified and converted into a data signal after analog-to-digital conversion, and then transmitted to the data processing module 9 through the optical cable using the TCP / IP protocol. The data processing module 9 obtains the partial discharge signal at the joint of the cable 10 after performing polarity identification and signal filtering and noise reduction on the signal. The calculated discharge data is written into the database. After multiple acquisitions, the algorithm analysis module 3 extracts data from the database for spectrum analysis and data reports, and displays it on the panel of the virtual detection system. The characteristic quantity of the spectrum of partial discharge is calculated and extracted, and then input into the high-frequency pulse detection module 4 and the fault diagnosis module 5, and the partial discharge type is analyzed and judged to obtain the partial discharge type of the cable 10.

[0080] The technical solution of the embodiment of the present invention adopts broadband detection technology, applies high-frequency clamp current sensor to directionally couple pulse signals and uses broadband differential current pulse polarity identification method to perform online interference suppression to eliminate random pulse interference which is the most difficult to eliminate. In addition, other comprehensive anti-interference measures such as digital filtering and wavelet analysis are set to make the measurement results accurate and reliable.

[0081] Optionally, based on the above embodiments, continue to refer to Figure 6 The data processing module 9 is also used to calculate the discharge amount of the cable 10 according to the voltage signal processed by the signal acquisition module 2, and determine whether a partial discharge defect occurs in the cable 10 according to the value of the discharge amount, and after determining that a partial discharge defect occurs in the cable 10, transmit the voltage signal to the algorithm analysis module 3.

[0082] Specifically, the pulse current method (ERA) is used for partial discharge measurement under DAC, which mainly uses the lower frequency band part of the partial discharge spectrum, generally tens of kHz to hundreds of kHz. When the cable is not charged, a pulse injection with a known charge amount is used to calibrate the quantification, thereby obtaining an accurately quantified partial discharge value in pC, which has a reasonable and effective physical meaning.

[0083] The data processing module 9 processes the voltage signal and calculates the value of the partial discharge. Assuming that the partial discharge occurs at a distance x from the test end, the test end can be the location of the detection device, u pd1 is the discharge pulse voltage, and the voltage collected by the detection impedance at the test end of the discharge pulse current is u pd2 , then the discharge amount q at the local discharge point is:

[0084]

[0085] Among them, Z0 is the characteristic impedance of the cable 10, t0 is the duration of the discharge pulse, and α represents the attenuation coefficient of the discharge pulse when it propagates in the cable 10. It reflects the attenuation degree of the discharge pulse voltage (or current) during the propagation along the cable 10, and the unit is generally m -1 (Attenuation per meter).

[0086] Optionally, based on the above embodiments, continue to refer to Figure 1 , Figure 4 and Figure 6 , the signal acquisition module 2 is connected to the high-frequency current sensor 1 through a BNC coaxial cable.

[0087] Specifically, the BNC coaxial cable can be used to connect the high-frequency current sensor 1 and the signal acquisition module 2. The BNC coaxial cable is widely used in various high-frequency signal transmission scenarios and has good high-frequency characteristics and shielding to ensure signal quality. The BNC coaxial cable is mainly used for signal transmission.

[0088] Optionally, based on the above embodiments, Figure 6 As shown, the signal acquisition module 2, the signal processing module 8, the data processing module 9, the algorithm analysis module 3, the high-frequency pulse detection module 4, the fault diagnosis module 5, the fault location module 6, the fault warning module 7 and the remote terminal are all connected through the Internet. The remote terminal is a web terminal, which is installed on a laptop device and has functions such as real-time fault detection, data analysis, fault warning and fault location.

[0089] Figure 7 is a flow chart of a cable defect detection method provided by an embodiment of the present invention, the detection method is applied to a cable defect detection device, such as Figure 6 As shown, the detection device includes: a high-frequency current sensor 1, a signal acquisition module 2, an algorithm analysis module 3, a high-frequency pulse detection module 4 and a fault diagnosis module 5. The high-frequency current sensor 1 is arranged on the cable grounding wire, the signal acquisition module 2 is connected to the high-frequency current sensor 1; the algorithm analysis module 3 is connected to the signal acquisition module 2; the high-frequency pulse detection module 4 is connected to the algorithm analysis module 3; the fault diagnosis module 5 is connected to the high-frequency pulse detection module 4. The detection device may also include a fault location module 6, a fault warning module 7, a signal processing module 8 and a data processing module 9, the fault location module 6 is connected to the high-frequency pulse detection module 4, the fault warning module 7 is connected to the fault diagnosis module 5 and the fault location module 6, the signal processing module 8 is connected to the signal acquisition module 2, the data processing module 9 is connected to the signal processing module 8, the algorithm analysis module 3 is connected to the data processing module 9, and the algorithm analysis module 3 is connected to the signal acquisition module 2 through the data processing module 9 and the signal processing module 8. The detection method includes:

[0090] S200: The high-frequency current sensor detects the current pulse signal on the cable grounding wire and converts it into a voltage signal.

[0091] Specifically, Figure 6 As shown, when partial discharge occurs in the cable 10, a current pulse signal is usually generated on the cable grounding wire. The high-frequency current sensor 1 is arranged on the cable grounding wire, and the cable defect is detected by detecting the magnitude of the electromotive force induced in the high-frequency current sensor 1 by detecting the magnetic field. The high-frequency current sensor 1 detects the current pulse signal on the cable grounding wire and converts it into a voltage signal.

[0092] S210: The signal acquisition module is used to amplify, filter and detect the voltage signal.

[0093] Specifically, Figure 6 As shown, the signal acquisition module 2 amplifies, filters and detects the voltage signal. The signal processing module 8 receives the voltage signal processed by the signal acquisition module 2. The signal processing module 8 adds a unique and accurate time tag to each pulse waveform data, which is used to accurately record the occurrence time of each pulse signal, so as to facilitate the subsequent time series analysis and processing of the signal. In the process of processing and analyzing multiple groups of data, the signal can be synchronized and connected in parallel according to the time tag of the signal, which helps to more accurately analyze the change law and characteristics of the signal, as well as the time sequence of the fault. The data processing module 9 receives the voltage signal processed by the signal processing module 8. The data processing module 9 judges and distinguishes the polarity of the received voltage signal. The direction and characteristics of the partial discharge pulse can be determined by identifying the polarity of the signal, so as to more accurately analyze and judge the partial discharge situation and improve the accuracy and reliability of the measurement. Polarity identification in the data processing module 9 can remove some interference signals, better identify effective signals, and provide more accurate data for subsequent signal processing and analysis.

[0094] S220: The algorithm analysis module extracts and analyzes features of the processed voltage signal, and performs feature classification and clustering on the voltage signal.

[0095] Specifically, Figure 6 As shown, the algorithm analysis module 3 uses statistical and machine learning methods to analyze the characteristics and laws of the partial discharge spectrum based on big data and algorithms, classifies and clusters the defect information of the cable 10 and feeds it back to the high-frequency pulse detection module 4.

[0096] S230: The high-frequency pulse detection module performs noise reduction processing on the voltage signal to extract the voltage signal representing the partial discharge defect of the cable.

[0097] Specifically, Figure 6As shown, the high-frequency pulse detection module 4 uses a high-frequency current detection method combined with wavelet noise reduction technology to improve the detection accuracy of the processed and analyzed voltage signal, and extracts characteristic signals that can effectively identify partial discharge defects of the live cable 10 when the cable 10 is energized, thereby further improving the accuracy and reliability of defect detection of the cable 10.

[0098] S240: The fault diagnosis module determines the type and extent of the cable defect based on the voltage signal representing the cable partial discharge defect and historical data.

[0099] Specifically, Figure 6 As shown, the fault diagnosis module 5 receives the analysis results and the detection data, and determines the fault type and degree of the cable 10 in combination with the historical data in the database.

[0100] S250: The fault location module uses the pulse reflection method to determine the location of the cable defect.

[0101] Specifically, Figure 6 As shown, the fault location module 6 can use multiple positioning methods to analyze the traveling wave signal to determine the defect location of the cable 10, and use the single-ended positioning technology to locate multiple partial discharge points. The high-frequency pulse detection module 4 sends the processed and analyzed voltage signal to the fault potential module 6, and the fault location module 6 can use the pulse reflection method to determine the location of the partial discharge defect of the cable 10.

[0102] S260: The fault warning module generates warning information according to the defect type and degree determined by the fault diagnosis module and the defect location determined by the fault location module, and uploads the warning information to the remote terminal.

[0103] Specifically, Figure 6 As shown, the fault warning module 7 receives the diagnosis and positioning information, monitors and prevents the partial discharge of the cable 10 in real time, and generates warning information to upload to the remote terminal. The warning information may include the type, degree and location of the fault. The fault warning module 7 can timely monitor and prevent the zero-sequence grounding and other warning signals caused by the potential cable 10 fault in real time.

[0104] The cable defect detection method provided in the embodiment of the present invention is applied to any cable defect detection device provided in any of the above embodiments of the present invention, and has the beneficial effects of any cable defect detection device provided in any of the above embodiments of the present invention.

[0105] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0106] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A cable defect detection device, characterized in that: include: High-frequency current sensor, signal acquisition module, algorithm analysis module, high-frequency pulse detection module and fault diagnosis module; The high-frequency current sensor is arranged on the cable grounding wire, the signal acquisition module is connected to the high-frequency current sensor, and the high-frequency current sensor is used to detect the current pulse signal on the cable grounding wire and convert it into a voltage signal to be transmitted to the signal acquisition module; The signal acquisition module is used to amplify, filter and detect the voltage signal; The algorithm analysis module is connected to the signal acquisition module, and is used to extract and analyze the features of the processed voltage signal, and to perform feature classification and clustering on the voltage signal; The high-frequency pulse detection module is connected to the algorithm analysis module, and is used to perform noise reduction processing on the voltage signal and extract the voltage signal representing the cable partial discharge defect; The fault diagnosis module is connected to the high-frequency pulse detection module and is used to determine the type and degree of the cable defect based on the voltage signal characterizing the cable partial discharge defect and historical data.

2. The cable defect detection device according to claim 1, characterized in that: Also includes: Fault location module; The fault location module is connected to the high-frequency pulse detection module, and the fault location module is used to determine the location of the cable defect by using a pulse reflection method.

3. The cable defect detection device according to claim 1, characterized in that: Also includes: Signal processing module; The signal processing module is connected between the signal acquisition module and the algorithm analysis module, and is used to time-mark the voltage signal processed by the signal acquisition module.

4. The cable defect detection device according to claim 1, characterized in that: Also includes: Data processing module; The data processing module is connected between the signal acquisition module and the algorithm analysis module, and is used to identify the polarity of the voltage signal processed by the signal acquisition module.

5. The cable defect detection device according to claim 4, characterized in that: The data processing module is also used to calculate the discharge amount of the cable based on the voltage signal processed by the signal acquisition module, and determine whether a partial discharge defect occurs in the cable based on the value of the discharge amount, and after determining that a partial discharge defect occurs in the cable, transmit the voltage signal to the algorithm analysis module.

6. The cable defect detection device according to claim 2, characterized in that: Also includes: Fault warning module; The fault warning module is connected to the fault diagnosis module and the fault location module, and is used to generate warning information according to the defect type and degree determined by the fault diagnosis module and the defect location determined by the fault location module, and upload the warning information to the remote terminal.

7. The cable defect detection device according to claim 1, characterized in that: The signal acquisition module is connected to the high-frequency current sensor via a BNC coaxial cable.

8. A cable defect detection method, characterized in that: The detection method is applied to a cable defect detection device, which includes: a high-frequency current sensor, a signal acquisition module, an algorithm analysis module, a high-frequency pulse detection module and a fault diagnosis module; the high-frequency current sensor is arranged on the cable grounding wire, the signal acquisition module is connected to the high-frequency current sensor; the algorithm analysis module is connected to the signal acquisition module; the high-frequency pulse detection module is connected to the algorithm analysis module; the fault diagnosis module is connected to the high-frequency pulse detection module; the detection method includes: The high-frequency current sensor detects the current pulse signal on the cable grounding wire and converts it into a voltage signal; The signal acquisition module is used to amplify, filter and detect the voltage signal; The algorithm analysis module extracts and analyzes the processed voltage signal, and performs feature classification and clustering on the voltage signal; The high-frequency pulse detection module performs noise reduction processing on the voltage signal to extract the voltage signal representing the cable partial discharge defect; The fault diagnosis module determines the type and extent of the cable defect based on the voltage signal characterizing the cable partial discharge defect and historical data.

9. The cable defect detection method according to claim 8, characterized in that: The detection device further includes a fault location module, which is connected to the high-frequency pulse detection module; after the fault diagnosis module determines the type and degree of the cable defect according to the voltage signal characterizing the cable partial discharge defect and the historical data, it also includes: The fault location module uses a pulse reflection method to determine the location of the cable defect.

10. The cable defect detection method according to claim 9, characterized in that: The detection device also includes a fault warning module, which is connected to the fault diagnosis module and the fault location module; After the fault location module determines the location of the cable defect using the pulse reflection method, it also includes: The fault warning module generates warning information according to the defect type and degree determined by the fault diagnosis module and the defect location determined by the fault location module, and uploads the warning information to the remote terminal.

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