A cable defect detection apparatus and method

Abstract

The application discloses a kind of cable defect detection device and detection method, detection device includes: high-frequency current sensor, signal acquisition module, algorithm analysis module, high-frequency pulse detection module and fault diagnosis module;High-frequency current sensor is used to detect the current pulse signal on cable ground wire, and conversion into voltage signal transmission to signal acquisition module;Signal acquisition module is used to amplify, filter and detect wave processing to voltage signal;Algorithm analysis module is used to the characteristic extraction and analysis of voltage signal after processing, and the characteristic classification and clustering of voltage signal;High-frequency pulse detection module is used to the noise reduction processing of voltage signal, extracts the voltage signal of the cable partial discharge defect characteristic;Fault diagnosis module is used to determine the type and degree of cable defect according to the voltage signal of the cable partial discharge defect characteristic and historical data.The application quickly judges cable line fault, improves power supply system reliability.

Description

TECHNICAL FIELD

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

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

[0003] The traditional maintenance mode of cables has always been regular power-off maintenance, which requires a large amount of manpower and material resources. This maintenance method is time-consuming and labor-intensive, prone to misoperation accidents, has low maintenance efficiency and high maintenance cost, and can delay power transmission time, resulting in economic losses and social impacts for the power system. SUMMARY

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

[0005] In a first aspect, the present application provides a cable defect detection device, which 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.

[0006] 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 high-frequency current sensor is used to detect the current pulse signal on the cable grounding wire and convert it into a voltage signal transmitted to the signal acquisition module, and 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 classify and cluster the voltage signal.

[0008] 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 to extract the voltage signal representing the cable partial discharge defect.

[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 representing the cable partial discharge defect and historical data.

[0010] Optionally, the detection device further comprises a fault positioning module.

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

[0012] Optionally, the detection device further comprises a signal processing module.

[0013] The signal processing module is connected between the signal acquisition module and the algorithm analysis module, and is configured to mark the voltage signal processed by the signal acquisition module with time.

[0014] Optionally, the detection device further comprises a data processing module.

[0015] The data processing module is connected between the signal acquisition module and the algorithm analysis module, and is configured to identify the polarity of the voltage signal processed by the signal acquisition module.

[0016] Optionally, the data processing module is further configured to calculate the discharge amount of the cable according to the voltage signal processed by the signal acquisition module, determine whether the cable has a partial discharge defect according to the value of the discharge amount, and transmit the voltage signal to the algorithm analysis module after determining that the cable has the partial discharge defect.

[0017] Optionally, the detection device further comprises a fault warning module.

[0018] The fault warning module is connected with the fault diagnosis module and the fault positioning module, and is configured to generate warning information according to the defect type and degree determined by the fault diagnosis module and the position of the defect determined by the fault positioning module, and upload the warning information to a remote terminal.

[0019] Optionally, the signal acquisition module and the high-frequency current sensor are connected through a BNC coaxial cable.

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

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

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

[0023] The algorithm analysis module is configured to extract and analyze features of the processed voltage signal, and classify and cluster the features of the voltage signal.

[0024] The high-frequency pulse detection module is configured to perform noise reduction processing on the voltage signal, and extract a voltage signal representing a partial discharge defect of the cable.

[0025] The fault diagnosis module determines the type and degree of the cable defect according to the voltage signal representing the cable partial discharge defect and historical data.

[0026] Optionally, the detection device further comprises a fault positioning module connected with 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 representing the cable partial discharge defect and historical data, the detection device further comprises:

[0027] The fault positioning module determines the position of the cable defect by using the pulse reflection method.

[0028] Optionally, the detection device further comprises a fault warning module connected with the fault diagnosis module and the fault positioning module; after the fault positioning module determines the position of the cable defect by using the pulse reflection method, the detection device further comprises:

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

[0030] The technical scheme of the embodiment of the present application can detect the partial discharge when the cable is in operation. Based on big data sample statistical analysis algorithm, the high-frequency pulse detection technology is used to comprehensively collect and analyze the data in the operation of the cable. Through the collection 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, the key information under the live state of the cable is effectively extracted, the cable line fault is quickly judged without affecting the normal power supply of the cable, the fault maintenance time is shortened, and the reliability of the power supply system is improved. The traditional power cable operation and maintenance which relies on planned power-off is broken through, and the detection can be completed without power-off. The operation and maintenance efficiency and convenience are greatly improved, the distribution network operation and maintenance level is effectively improved, the human and material consumption, economic loss and social influence caused by power-off maintenance are reduced. Meanwhile, the technical scheme of the embodiment of the present application integrates the intelligent cable state judgment technology, a database covering various partial discharge types and degrees is established through a large number of field experiment simulations. By using the database, the system can extract the frequency spectrum 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 of the whole system more convenient and efficient, changes the situation that the traditional detection technology cannot intelligently analyze the cable defect state. The detection device has a series of functions such as real-time detection and data analysis, provides comprehensive and accurate, fast fault troubleshooting and repair support for the operation and maintenance personnel. The operation and maintenance personnel can timely master the operation state of the cable, take corresponding measures at the initial stage or potential fault stage of the fault, effectively avoid the further deterioration of the fault, and make up for the deficiency that the existing traditional cable fault detection technology cannot realize real-time detection and analysis.

[0031] It should be understood that the matters described in this detailed description are intended to be illustrative and are not intended to limit or restrict the scope or applicability of the embodiments of the application in any way. This description set in examples is intended to be illustrative, and is not intended to be limiting on the scope of the application. Other features of the present application will become apparent in the course of the following descriptions and / or in the examples, which are given for illustration of the application. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

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

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

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

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

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

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

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

[0040] In order to make the technical personnel in the art better understand the present application scheme, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0041] It should be noted that the terms "first", "second", and the like in the description and in the claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device including a series of steps or units does not necessarily have to be limited to only those steps or units clearly listed, but can include other steps or units 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 application, Figure 2 is a structural schematic diagram of a high-frequency current sensor provided by an embodiment of the present application, as Figure 1 and Figure 2 shown, the detection device comprises 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 line, the signal acquisition module 2 is connected with the high-frequency current sensor 1, the high-frequency current sensor 1 is used for detecting the current pulse signal on the cable grounding line and converting into a voltage signal transmitted to the signal acquisition module 2. The signal acquisition module 2 is used for amplifying, filtering, and detecting the voltage signal. The algorithm analysis module 3 is connected with the signal acquisition module 2, and is used for extracting and analyzing the features of the processed voltage signal, and classifying and clustering the features of the voltage signal. The high-frequency pulse detection module 4 is connected with the algorithm analysis module 3, and is used for noise reduction processing of the voltage signal, and extracting the voltage signal representing the partial discharge defect of the cable 10. The fault diagnosis module 5 is connected with the high-frequency pulse detection module 4, and is used for determining the type and degree of the defect of the cable 10 according to the voltage signal representing the partial discharge defect of the cable 10 and historical data.

[0043] Specifically, in the live state of the cable 10, the cable 10 is detected in real time, and the high-frequency current detection method is adopted. When the cable 10 has partial discharge, a current pulse signal is usually generated on the cable grounding line. The high-frequency current sensor 1 is arranged on the cable grounding line, and the detection of the cable defect is performed by detecting the electromotive force generated in the high-frequency current sensor 1 by the magnetic field.

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

[0045] As shown in the formula (1), the output voltage of the Rogowski coil is proportional to the derivative of the current i, and the output voltage of the self-integrating Rogowski coil is proportional to the current i. Figure 2 As shown, a integrating resistor R1 is connected in parallel with the output terminal 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, R2 is the integrating resistor of the coil, and R2 and the equivalent inductance Ls of the coil form an integrating circuit. Therefore, the sensitivity of the high-frequency current sensor 1 is inversely proportional to the number of turns of the coil and proportional to the integrating resistor R2, and the frequency band response of the high-frequency current sensor 1 is related to the integrating resistor R2, the equivalent inductance Ls and the equivalent resistance Rs. The equivalent inductance Ls is related to the number of turns of the coil after the size and material of the magnetic core are determined, and increasing the number of turns of the coil and reducing the integrating 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, and 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 rules of the partial discharge spectrum based on big data and algorithms, and classifies and clusters the defect information of the cable 10 to feed back to the high-frequency pulse detection module 4. The high-frequency pulse detection module 4 uses high-frequency current detection method combined with wavelet denoising technology to improve the detection accuracy, and extracts the characteristic signal of the partial discharge defect of the live cable 10 under the live state of the cable 10, which further improves 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 of the database.

[0047] In the algorithm analysis module 3 and the fault diagnosis module 5, an artificial neural network (ANNS) is introduced as a core tool, which is composed of a large number of processing units simulating biological neurons connected to each other, forming a highly nonlinear and complex network system. Through an accurate mathematical model, the structure of the biological neural network is simulated, and under the guidance of advanced algorithms, the ANNS simulates the intelligent behavior of biological intelligence to solve the intelligent information processing problems that traditional algorithms cannot handle. As the basis of massive parallel processing and large-scale parallel computing, ANNS is a highly nonlinear dynamic system and a self-organizing and adaptive system, which is 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 make the ANNS fully trained and improve the performance of the ANNS. The BP neural network algorithm has high-speed information processing capability, large knowledge storage capacity, strong uncertainty information processing capability, and strong robustness. These characteristics enable the BP neural network algorithm to exhibit excellent performance when processing complex and large-scale data sets. Figure 3 is a flowchart of a BP neural network algorithm program implementation method provided by an embodiment of the present application, as shown in Figure 3 The BP neural network algorithm program implementation method comprises the following steps.

[0049] S100: initializing the weight matrix V and W, and initializing the counters i and p to 1.

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

[0051] S110: inputting a sample and calculating the output of each layer.

[0052] Specifically, a sample data is inputted. According to the current weight matrix V and W, the output of each layer of the neural network is calculated through forward propagation. This step starts from the input layer, passes through the hidden layer, and finally obtains the result of the output layer.

[0053] S120: calculating the error E.

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

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

[0056] Specifically, it is checked whether the current sample serial number p is less than the total number of samples P. If p < P, p is increased by 1 (i.e., the next sample is processed), and then the step S110 is returned to continue processing the next sample. If p ≥ P, the step S141 is executed.

[0057] S141: calculating the error signal of each layer.

[0058] Specifically, the error signal of each layer of the neural network is calculated through the back propagation 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: increasing p by 1.

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

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

[0062] Specifically, according to the calculated error signal of each layer, the weight matrix V and W are adjusted. This step is to update the weight by gradient descent and other optimization algorithms 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 B is less than the preset minimum error Emin. If E < Emin, execute S171, the algorithm ends, indicating that the neural network has converged to a satisfactory result. If E ≥ Emin, reset the sample number p to 1, and increase the iteration number i by 1, then return to S110 to start processing the sample again and perform the next iteration. The BP neural network algorithm adjusts the weight by continuous iteration, so that the output of the neural network is as close as possible to the actual label, thereby realizing the learning and prediction of data.

[0065] S171: The algorithm ends.

[0066] S172: p is set to 1, and i is increased by 1.

[0067] Specifically, if E ≥ Emin, reset the sample number p to 1, increase the iteration number i by 1, and then return to S110 to start processing the sample again and perform the next iteration.

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

[0069] The technical scheme of the embodiment of the present application can detect partial discharge when the cable is in operation, and comprehensively collects and analyzes data in the cable operation based on big data sample statistical analysis algorithm and high-frequency pulse detection technology. The complete link from the original signal to the analyzable data is constructed through the collection and processing of the high-frequency pulse current signal by the signal collection module, the key information in the cable live state is effectively extracted, the cable line fault is quickly judged without affecting the normal power supply of the cable, the fault maintenance time is shortened, and the reliability of the power supply system is improved. The traditional power cable operation and maintenance repair relying on planned power-off is broken through, and the detection can be completed without power-off. The operation and maintenance efficiency and convenience are greatly improved, the distribution network operation and maintenance level is effectively improved, the human and material consumption, economic loss and social influence caused by power-off repair are reduced. Meanwhile, the technical scheme of the embodiment of the present application integrates intelligent cable state judgment technology, a database covering various partial discharge types and degrees is established through a large number of field experiment simulations. By using the database, the system can extract the frequency spectrum characteristic signal, accurately identify the fault type and defect degree, realize 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 of the whole system more convenient and efficient, changes the situation that the traditional detection technology cannot intelligently analyze the cable defect state. The detection device has a series of functions such as real-time detection and data analysis, and provides comprehensive and accurate, fast fault troubleshooting and repair support for operation and maintenance personnel. The operation and maintenance personnel can timely master the operation state of the cable, take corresponding measures at the initial stage or potential fault stage of the fault, effectively avoid the further deterioration of the fault, and make up for the deficiency that the existing traditional cable fault detection technology cannot realize real-time detection and analysis.

[0070] Optionally, on the basis of each of the above embodiments, Figure 4 is a structural schematic diagram of another cable defect detection device provided by the embodiment of the present application, Figure 5 is a schematic diagram of discharge pulse transmission after partial discharge of a cable provided by the embodiment of the present application, as Figure 4 shown, the detection device further includes a fault positioning module 6 and a fault early warning module 7. The fault positioning module 6 is connected with the high-frequency pulse detection module 4, and the fault positioning module 6 is used to determine the position of the cable 10 defect by using the pulse reflection method. The fault early warning module 7 is connected with the fault diagnosis module 5 and the fault positioning module 6, and is used to generate early warning information according to the defect type and degree determined by the fault diagnosis module 5 and the position of the defect determined by the fault positioning module 6, and upload the early warning information to a remote terminal.

[0071] Specifically, 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 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 in the cable 10. Figure 5 As shown, the pulse reflection principle is that after a partial discharge occurs in the cable 10, the discharge pulse propagates to both ends of the cable 10 simultaneously. One of the discharge pulses, as the incident pulse Q1, is first transmitted to the detection device. The other discharge pulse, as the reflected pulse Q2, propagates to the far end of the cable 10 and is 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 based on the time difference Δt:

[0072]

[0073] Where 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, and l is the length of the cable 10. The fault location module 6 can obtain the transmission speed v of the incident pulse Q1 or the reflected pulse Q2 based on the wave propagation constant and impedance characteristics 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. The fault location module 6 can then calculate the transmission distance of the incident pulse Q1 based on the transmission speed v of the incident pulse Q1 or the reflected pulse Q2 and the time difference Δt, thereby determining the location of the partial discharge fault in the cable 10.

[0074] When a power cable currently fails, traditional manual troubleshooting of the fault point is difficult and prone to errors. 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 damage to the cable insulation, and the positioning accuracy is not high. According to the technical solution of the embodiment of the present invention, the fault location module 6 can locate defects in the cable in the energized state without the need to perform a pressure test on the cable 10 as in the traditional method. 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 in the cable 10 can be accurately found. This function provides precise guidance for maintenance and processing work, helps operation and maintenance personnel quickly locate the fault point, greatly shortens the time for troubleshooting, reduces costs, and overcomes the problem that traditional cable fault location technology is prone to insulation damage and low positioning accuracy when pressurizing the power-off cable.

[0075] The fault early warning module 7 receives the diagnosis and positioning information, monitors and prevents in real time for the partial discharge of the cable 10, and generates early warning information uploaded to the remote terminal, which can include the fault type, degree and location. The fault early warning module 7 can timely monitor and prevent the warning signal such as zero sequence grounding caused by potential cable 10 faults.

[0076] The traditional cable fault detection technology cannot perform fault early warning. The technical scheme of the embodiment of the present application realizes efficient detection, diagnosis, positioning and early warning of the power cable under the live state through the cooperative work of multiple modules, quickly judges the fault cable line and the position of the fault point from the cable line terminal, greatly shortens the fault detection time, quickly repairs the cable fault, and improves the reliability of the power supply system.

[0077] Optionally, on the basis of each of the above embodiments, Figure 6 is another structural schematic diagram of a cable defect detection device provided by the embodiment of the present application, as Figure 6 indicated, the monitoring device further 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 for time labeling 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 for polarity discrimination 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 label to each pulse waveform data, which can accurately record the occurrence time of each pulse signal, and facilitates subsequent time sequence analysis and processing of the signal. In the process of multiple data processing and analysis, the signal can be synchronized and connected in parallel according to the time label of the signal, which helps to more accurately analyze the change rule and characteristics of the signal, as well as the time sequence of the fault occurrence. The data processing module 9 receives the voltage signal processed by the signal processing module 8, and the data processing module 9 judges and distinguishes the polarity of the received voltage signal. The polarity of the signal can be distinguished to determine the direction and characteristics of the partial discharge pulse, so as to more accurately analyze and judge the partial discharge condition and improve the accuracy and reliability of the measurement. In the polarity discrimination of the data processing module 9, some interference signals can be removed, and effective signals can be better identified, providing more accurate data for subsequent signal processing and analysis.

[0079] The technical scheme of the embodiment of the application is that the high-frequency current sensor 1 installed on the cross interconnection line is used to couple the pulse current signal at the joint of the cable 10, the coupled pulse signal is transmitted to the signal acquisition unit 2 through the coaxial cable, the analog signal is converted into a data signal after amplification and analog-digital conversion, and then the data signal is transmitted to the data processing module 9 through the optical cable by using the TCP / IP protocol. The data processing module 9 obtains the partial discharge signal at the joint of the cable 10 after polarity discrimination and signal filtering and noise reduction of the signal. The calculated discharge data is written into the database, after multiple acquisitions, the algorithm analysis module 3 extracts the data from the database for spectrum analysis and data report, and displays on the panel of the virtual detection system, the characteristic quantity of the spectrum of the partial discharge is calculated and extracted, and then input into the high-frequency pulse detection module 4 and the fault diagnosis module 5, to analyze and distinguish the type of the partial discharge of the cable 10.

[0080] The technical scheme of the embodiment of the application adopts a wideband detection technology, uses a high-frequency clamp current sensor to directionally couple a pulse signal, and uses a wideband differential current pulse polarity discrimination method to perform online interference suppression, so as to eliminate the most difficult-to-eliminate random pulse-type interference. In addition, other comprehensive anti-interference measures such as digital filtering and wavelet analysis are set, so that the measurement result is accurate and reliable.

[0081] Optionally, on the basis of each of the above embodiments, with reference to Figure 6 , the data processing module 9 is further 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 the cable 10 has a partial discharge defect according to the value of the discharge amount, and transmit the voltage signal to the algorithm analysis module 3 after determining that the cable 10 has a partial discharge defect.

[0082] Specifically, the partial discharge measurement under the DAC uses a pulse current method (ERA), mainly uses a lower frequency band part in the partial discharge spectrum, generally tens of kHz to hundreds of kHz. In the case that the cable is not electrified, a pulse with a known charge amount is injected for correction quantification, so as to obtain an accurate quantified partial discharge amount value in pC, which has reasonable and effective physical meaning.

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

[0084]

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

[0086] Optionally, on the basis of the above embodiments, with reference to Figure 1 、 Figure 4 and Figure 6 , the signal acquisition module 2 is connected with 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 properties to ensure signal quality. The BNC coaxial cable is mainly used for signal transmission.

[0088] Optionally, on the basis of the above embodiments, as shown in Figure 6 , 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 positioning 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 notebook computer device and has functions of real-time fault detection, data analysis, fault warning, and fault positioning.

[0089] Figure 7 is a flowchart of a cable defect detection method provided by an embodiment of the present application. The detection method is applied to a cable defect detection device, as shown in Figure 6 , 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 ground wire, and the signal acquisition module 2 is connected with the high-frequency current sensor 1; the algorithm analysis module 3 is connected with the signal acquisition module 2; the high-frequency pulse detection module 4 is connected with the algorithm analysis module 3; and the fault diagnosis module 5 is connected with the high-frequency pulse detection module 4. The detection device can also include a fault positioning module 6, a fault warning module 7, a signal processing module 8, and a data processing module 9. The fault positioning module 6 is connected with the high-frequency pulse detection module 4, the fault warning module 7 is connected with the fault diagnosis module 5 and the fault positioning module 6, the signal processing module 8 is connected with the signal acquisition module 2, the data processing module 9 is connected with the signal processing module 8, the algorithm analysis module 3 is connected with the data processing module 9, and the algorithm analysis module 3 is connected with 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 ground wire and converts it into a voltage signal.

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

[0092] S210: The signal acquisition module is used for amplifying, filtering and detecting the voltage signal.

[0093] Specifically, as shown in Figure 6 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, and adds a unique and accurate time label to each pulse waveform data. This can accurately record the occurrence time of each pulse signal, facilitating subsequent time series analysis and processing of the signal. During the processing and analysis of multiple groups of data, the signal can be synchronized and connected according to the time label of the signal, which helps to more accurately analyze the change rule and characteristics of the signal, as well as the time sequence of the fault occurrence. The data processing module 9 receives the voltage signal processed by the signal processing module 8, and judges and distinguishes the polarity of the received voltage signal. By identifying the polarity of the signal, the direction and characteristics of the partial discharge pulse can be determined, so as to more accurately analyze and judge the partial discharge situation and improve the accuracy and reliability of the measurement. During the polarity discrimination of the data processing module 9, some interference signals can be removed, and effective signals can be better identified, providing more accurate data for subsequent signal processing and analysis.

[0094] S220: The algorithm analysis module extracts and analyzes the features of the processed voltage signal, and classifies and clusters the features of the voltage signal.

[0095] Specifically, as shown in Figure 6 The algorithm analysis module 3 uses statistical and machine learning methods to analyze the features and rules of the partial discharge spectrum based on big data and algorithms, and classifies and clusters the defect information of the cable 10 to feed 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 cable partial discharge defect.

[0097] Specifically, as shown in Figure 6As shown, the high-frequency pulse detection module 4 uses high-frequency current detection method combined with wavelet denoising technology to improve the detection accuracy of the processed and analyzed voltage signal, extracts the characteristic signal that can effectively distinguish the partial discharge defect of the live cable 10 under the live state of the cable 10, and further improves the accuracy and reliability of the defect detection of the cable 10.

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

[0099] Specifically, as shown in the figure, Figure 6 The fault diagnosis module 5 receives the analysis result and the detection data, judges the fault type and degree of the cable 10 in combination with the historical data in the database.

[0100] S250: The fault location module determines the position of the cable defect by using the pulse reflection method.

[0101] Specifically, as shown in the figure, Figure 6 The fault location module 6 can use various positioning methods to analyze the traveling wave signal to determine the defect position of the cable 10, and use single-end 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 location module 6, and the fault location module 6 can determine the position of the partial discharge defect of the cable 10 by using the pulse reflection method.

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

[0103] Specifically, as shown in the figure, Figure 6 The fault warning module 7 receives the diagnosis and positioning information, monitors and prevents the partial discharge of the cable 10 in real time, generates warning information and uploads it to the remote terminal. The warning information can include the fault type, degree and position. The fault warning module 7 can timely and real-time monitor and prevent the warning signal such as zero sequence grounding caused by potential cable 10 faults.

[0104] The cable defect detection method provided by the embodiment of the application is applied to any cable defect detection device provided by any embodiment of the application described above, and has the beneficial effects of any cable defect detection device provided by any embodiment of the application described above.

[0105] It should be understood that various forms of processes shown above can be reordered, added or deleted. For example, each step described in the present application can be executed in parallel, sequentially or in different order, as long as the desired results of the technical solutions of the present application can be achieved, which is not limited herein.

[0106] The above detailed description does not limit the scope of the application. Various modifications, combinations, sub-combinations and alternatives can be made to the detailed description. Any modification, equivalent replacement and improvement etc. made within the spirit and principle of the application shall be included in the scope of the application.

Claims

1. A cable defect detection device, characterized in that: The 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 arranged on a cable grounding wire, and the signal acquisition module is connected with the high-frequency current sensor. The signal acquisition module is used for amplifying, filtering and detecting the voltage signal. The algorithm analysis module is connected with the signal acquisition module, and is used for extracting and analyzing the features of the processed voltage signal, and classifying and clustering the features of the voltage signal. The high-frequency pulse detection module is connected with the algorithm analysis module, and is used for performing noise reduction processing on the voltage signal classified and clustered, and extracting the voltage signal representing the cable partial discharge defect. The fault diagnosis module is connected with the high-frequency pulse detection module, and is used for determining the type and degree of the cable defect according to the voltage signal representing the cable partial discharge defect and historical data. The device further comprises a data processing module. The data processing module is connected between the signal acquisition module and the algorithm analysis module, and is used for identifying the polarity of the voltage signal processed by the signal acquisition module. The data processing module is further used for calculating the discharge amount of the cable according to the voltage signal processed by the signal acquisition module, determining whether the cable has a partial discharge defect according to the value of the discharge amount, and transmitting the voltage signal to the algorithm analysis module when it is determined that the cable has a partial discharge defect. The device further comprises a fault positioning module.

2. The cable defect detection apparatus according to claim 1, characterized by The fault positioning module is connected with the high-frequency pulse detection module, and is used for determining the position of the cable defect by using the pulse reflection method. The device further comprises a signal processing module. The signal processing module is connected between the signal acquisition module and the algorithm analysis module, and is used for time labeling the voltage signal processed by the signal acquisition module.

3. The cable defect detection apparatus of claim 1, wherein The device further comprises a fault warning module. The fault warning module is connected with the fault diagnosis module and the fault positioning module, and is used for generating warning information according to the type and degree of the defect determined by the fault diagnosis module and the position of the defect determined by the fault positioning module, and uploading the warning information to a remote terminal. The signal acquisition module and the high-frequency current sensor are connected through a BNC coaxial cable.

4. The cable defect detection apparatus according to claim 2, characterized by The detection method is applied to a cable defect detection device, and the detection device comprises a high-frequency current sensor, a signal acquisition module, an algorithm analysis module, a high-frequency pulse detection module, a fault diagnosis module and a data processing module. The high-frequency current sensor is arranged on a cable grounding wire, and the signal acquisition module is connected with the high-frequency current sensor. The data processing module is connected between the signal acquisition module and the algorithm analysis module. The high-frequency pulse detection module is connected with the algorithm analysis module. The fault diagnosis module is connected with the high-frequency pulse detection module. The detection method comprises the following steps: ​ ​ 5. The cable defect detection apparatus of claim 1, wherein ​ 6. A method of detecting defects in a cable, characterized by, ​ The high-frequency current sensor detects the current pulse signal on the cable ground wire and converts it into a voltage signal; The signal acquisition module is used for amplifying, filtering and detecting the voltage signal; The algorithm analysis module extracts and analyzes the processed voltage signal, and classifies and clusters the voltage signal; The high-frequency pulse detection module performs noise reduction processing on the voltage signal classified and clustered, and extracts the voltage signal representing the cable partial discharge defect; The fault diagnosis module determines the type and degree of cable defect according to the voltage signal representing the cable partial discharge defect and historical data; The data processing module is used for polarity discrimination of the voltage signal processed by the signal acquisition module; The data processing module is also used for calculating the discharge amount of the cable according to the voltage signal processed by the signal acquisition module, and determining whether the cable has a partial discharge defect according to the value of the discharge amount, and transmitting the voltage signal to the algorithm analysis module after determining that the cable has a partial discharge defect.

7. The method of claim 6, wherein, The detection device further comprises a fault positioning module connected with the high-frequency pulse detection module; After the fault diagnosis module determines the type and degree of cable defect according to the voltage signal representing the cable partial discharge defect and historical data, it further comprises: The fault positioning module determines the position of the cable defect by pulse reflection method.

8. The method of claim 7, wherein, The detection device further comprises a fault warning module connected with the fault diagnosis module and the fault positioning module; After the fault positioning module determines the position of the cable defect by pulse reflection method, it further comprises: The fault warning module generates warning information according to the type and degree of defect determined by the fault diagnosis module and the position of the defect determined by the fault positioning module, and uploads the warning information to a remote terminal.

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