Traveling wave signal-based coiled cable fault detection method and system

Abstract

The invention discloses a traveling wave signal-based coiled cable fault detection method. The method comprises the following steps of: 1, respectively connecting two ends of a coiled cable with a signal generator and an MEMS (Micro Electro Mechanical System) high-frequency voltage sensor; setting parameters of the signal generator; starting a signal generator; 2, the MEMS high-frequency voltage sensor detects a reflected wave signal, and the reflected wave signal is denoised by a filter and then transmitted to a signal processing device; 3, the signal processing device carries out discrete wavelet transformation on the reflected wave signal to obtain a reconstructed and denoised local reflected wave signal; and 4, the waveform of the reflected wave signal is identified to obtain the classification of cable faults. According to the invention, the MEMS high-frequency voltage sensor is adopted to sense weak traveling wave signals; the filter coefficient is improved by using the maximum signal-to-noise ratio function to obtain the optimal filter coefficient, so that the frequency response of the filter can better meet the requirements of practical application, distortion and errors in the filtering process are reduced, and the method is suitable for weak traveling wave signals.

Description

Technical Field

[0001] The invention belongs to the technical field of electrical fault testing, and in particular relates to a method and system for detecting cable faults in a coil based on traveling wave signals. Background Art

[0002] The idea of ​​using the traveling wave method to measure cable faults was first proposed by Seidu in 1948, followed by the emergence of a variety of cable fault detection methods such as the low-voltage pulse method, pulse voltage method, and pulse current method. Among them, the low-voltage pulse method is simple to operate, cheap, and safe, but it cannot detect high-resistance faults. With the advancement of technology, the traveling wave method for cable fault detection has gradually developed into an important non-destructive testing technology.

[0003] However, in the process of using traveling wave signals to detect cable faults, the main problem faced is that signal attenuation brings a series of serious consequences.

[0004] First, signal attenuation will cause the amplitude of the traveling wave signal to decrease and the waveform to be distorted. When the signal strength decreases to a certain extent, it may cause data transmission errors or loss, thus affecting the accuracy of fault location. Secondly, as the signal attenuation increases, the communication distance will be limited. When the signal attenuates to a certain extent, it may not be able to maintain stable communication, resulting in the inability to effectively transmit the traveling wave signal to the detection point, and thus the inability to locate the fault. Then, signal attenuation may also cause system instability, leading to communication failure or data loss, thus affecting the normal operation of the entire system. In the process of detecting cable faults with traveling wave signals, if the system is unstable, it may cause false alarms or missed faults, posing a hidden danger to the safe operation of the power system. Finally, since signal attenuation may cause inaccurate or impossible fault location, it will take more time and manpower to troubleshoot and repair the fault. This will not only increase maintenance costs, but also affect the reliability and stability of the power system.

[0005] Therefore, when measuring cable faults using the traveling wave method, effective measures need to be taken to avoid the consequences of signal attenuation, improve the applicability to weak signals, and ensure the accuracy and reliability of traveling wave signal detection of cable faults. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a cable fault detection method based on traveling wave signals in response to the deficiencies in the above-mentioned prior art. The method adopts a MEMS high-frequency voltage sensor to sense weak traveling wave signals. The filter coefficients are improved by using the maximum signal-to-noise ratio function to obtain the optimal filter coefficients, which can make the frequency response of the filter more in line with the needs of practical applications, thereby reducing distortion and errors in the filtering process, and is suitable for weak traveling wave signals.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is: a cable fault detection method based on traveling wave signals, characterized in that it comprises the following steps:

[0008] Step 1: Connect one end of the cable drum to the signal generator; set the parameters of the signal generator; start the signal generator and send an incident wave signal to the cable drum;

[0009] Step 2: The other end of the coiled cable is connected to the MEMS high-frequency voltage sensor; the MEMS high-frequency voltage sensor detects the reflected wave signal, and the reflected wave signal is transmitted to the signal processing device after being de-noised by the filter;

[0010] Step 3: The signal processing device performs discrete wavelet transform on the reflected wave signal to obtain a reconstructed and denoised local reflected wave signal:

[0011] Step 301, using the maximum signal-to-noise ratio function to improve the filter coefficient to obtain the optimal filter coefficient h';

[0012] Step 302: Improve the symlet wavelet function: Where a represents the scale parameter, which controls the expansion and contraction of the wavelet function; b represents the translation parameter, which controls the position of the wavelet function on the time axis. ψ(t) represents the basic wavelet function, where h' n represents the optimal filter coefficient, and n represents the index of the optimal filter coefficient;

[0013] Step 303: decompose the reflected wave signal layer by layer based on the improved symlet wavelet function, extract the high-frequency components of each layer of wavelet and the original low-frequency components of the highest layer of wavelet, and form a reconstructed and denoised local reflected wave signal;

[0014] Step 4: Identify the waveform of the reflected wave signal and obtain the classification of the cable fault.

[0015] The above-mentioned cable coil fault detection method based on traveling wave signal is characterized in that it also includes:

[0016] Step 5: Calculate the reflection coefficient and infer the second probability of cable failure according to the reflection coefficient;

[0017] Step 6: Multiply the first probability and the second probability by their respective confidence levels and then sum them up to obtain the final fault classification result.

[0018] The above-mentioned method for detecting cable faults in a coil based on traveling wave signals is characterized in that the specific method of step 301 is:

[0019] Step 3011: Use the current filter coefficient h n Filter the signal and calculate the SNR value. Where x(z) represents the filter coefficient h n The denoised signal after filtering, y(z) represents the noisy signal, and m represents the time length;

[0020] Step 3012: Calculate the SNR value with respect to the filter coefficient h based on the gradient optimization algorithm. n The gradient of the filter coefficient h is updated in the opposite direction of the gradient. n ;

[0021] Step 3013: until the number of iterations reaches the upper limit, the optimal filter coefficient h' is obtained. n .

[0022] The above-mentioned method for detecting cable faults in a coil based on traveling wave signals is characterized in that: in step 303, it also includes:

[0023] Step 3031: Calculate the total noise energy E in the j-th layer decomposition j , E j =∑d j (k), where d j (k) is the detail coefficient in the j-th level decomposition, k represents time;

[0024] Step 3032, if E j+1 ≥E j , then the optimal number of decomposition layers is j.

[0025] The above-mentioned cable fault detection method based on traveling wave signal is characterized in that: the specific method of "identifying the first probability of cable fault according to the result of wavelet transform" in step 4 is:

[0026] Use support vector machine to build a cable fault classification model;

[0027] Using cable fault signal data of known categories, training the cable fault classification model, adjusting the parameters and structure of the cable fault classification model, and obtaining a trained cable fault classification model;

[0028] The reflected wave signal to be classified is subjected to discrete wavelet transform;

[0029] Extract features from the results of wavelet transform;

[0030] The extracted features are used as input to the cable fault classification model, and the cable fault classification model outputs the type or category of the cable fault.

[0031] The above-mentioned cable coil fault detection method based on traveling wave signals is characterized in that: in step two, the MEMS high-frequency voltage sensor is placed at a position close to the end of the other end of the cable coil, and the gap between the MEMS high-frequency voltage sensor and the other end of the cable is between 0.5mm and 2mm.

[0032] The above-mentioned cable reel fault detection method based on traveling wave signal is characterized in that the incident wave signal sent by the signal generator is a periodically changing sinusoidal pulse signal.

[0033] The cable reel fault detection system based on traveling wave signals of the present invention is characterized in that it includes a signal generator, and a MEMS high-frequency voltage sensor, a filter and a signal processing device connected in sequence, the signal generator is connected to one end of the cable reel through a BNC male connector and a BNC female connector, and the MEMS high-frequency voltage sensor is connected to the other end of the cable reel.

[0034] The above-mentioned traveling wave signal-based cable fault detection system is characterized in that the MEMS high-frequency voltage sensor is placed near the other end of the cable, and the gap between the MEMS high-frequency voltage sensor and the other end of the cable is between 0.5mm and 2mm.

[0035] The above-mentioned cable fault detection system based on traveling wave signals is characterized in that it also includes a BNC connector bracket, the BNC connector bracket includes a base and a U-shaped frame arranged on the base, one side of the U-shaped frame is provided with a first connector for connecting to a BNC male connector, and the other side of the U-shaped frame is provided with a second connector for connecting to a BNC female connector.

[0036] Compared with the prior art, the present invention has the following advantages:

[0037] 1. The invention has a simple structure, reasonable design, and is easy to implement and operate.

[0038] 2. The present invention adopts a MEMS high-frequency voltage sensor to realize non-contact measurement of voltage. The MEMS high-frequency voltage sensor has the advantages of high precision and low loss, can accurately sense weak traveling wave signals, and improves the accuracy of voltage measurement. The sensor does not contain iron cores, windings and other structures, thus eliminating problems such as magnetic saturation and ferromagnetic resonance.

[0039] 3. The present invention uses a filter to amplify and filter the collected weak traveling wave signal.

[0040] 4. The present invention applies wavelet transform to perform multi-scale decomposition on the traveling wave signal, so as to gradually refine the signal at multiple scales, thereby focusing on any details of the signal and extracting the characteristics of the signal in different frequency ranges, thus providing a basis for subsequent fault detection.

[0041] 5. The present invention utilizes the maximum signal-to-noise ratio function to improve the filter coefficient and obtain the optimal filter coefficient, which can directly improve the signal-to-noise ratio performance of the filter. By improving the filter coefficient, the frequency response of the filter can be made more in line with the needs of practical applications, thereby reducing distortion and errors in the filtering process, and is suitable for weak traveling wave signals.

[0042] In summary, the present invention has a simple structure and a reasonable design. It uses a MEMS high-frequency voltage sensor to sense weak traveling wave signals; applies wavelet transform to perform multi-scale decomposition on traveling wave signals; uses the maximum signal-to-noise ratio function to improve the filter coefficients to obtain the optimal filter coefficients. This can make the frequency response of the filter more in line with the needs of practical applications, thereby reducing distortion and errors in the filtering process, and is suitable for weak traveling wave signals.

[0043] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of a method according to Embodiment 1 of the present invention.

[0045] Figure 2 The figure is a flow chart of the wavelet transform method of the present invention.

[0046] Figure 3 This is a flow chart of the method of Embodiment 2 of the present invention.

[0047] Figure 4 It is a circuit principle block diagram of the present invention.

[0048] Figure 5 The figure is a schematic diagram of installing the BNC connector bracket of the present invention.

[0049] Description of reference numerals:

[0050] 1—Signal generator; 2—BNC male connector; 3—BNC female connector;

[0051] 4—coiled cable; 5—MEMS high-frequency voltage sensor; 6—filter;

[0052] 7—Oscilloscope; 8—Signal processing device; 9—BNC connector bracket. DETAILED DESCRIPTION

[0053] The method of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments of the present invention.

[0054] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0055] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0056] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations 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.

[0057] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used here to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0058] Embodiment 1

[0059] like Figure 1 and Figure 2 As shown, the cable fault detection method based on traveling wave signal of the present invention comprises the following steps:

[0060] Step 1: Connect one end of the cable reel to the signal generator; set the parameters of the signal generator; start the signal generator and send an incident wave signal to the cable reel.

[0061] Then, the output parameters of the signal generator are set according to the amplitude, frequency, waveform and other parameters of the incident wave signal to be sent. Ensure that the output level of the signal generator matches the input level of the cable to avoid abnormal operation or damage of the circuit caused by too high or too low input signals. In one possible embodiment, for a power cable with a rated voltage of 1kv, the incident wave is a sinusoidal pulse, and the amplitude of the sinusoidal pulse is set to 100V, which is lower than the rated voltage of the cable and provides a sufficient safety margin. The frequency of the sinusoidal pulse signal is set to 10kHz. This value is within the stable transmission characteristics of the cable and meets the limitations of the test equipment. In the power system, the cables used to transmit electric energy and control signals usually require that the signal has a small loss during the transmission process to ensure the integrity and stability of the signal. Therefore, the signal generator outputs a sinusoidal pulse signal, the waveform of the sinusoidal pulse signal is smooth and continuous, and the spectrum is shown as a single spectrum line. The main energy is concentrated on the base frequency, and the loss during transmission in the cable is relatively small.

[0062] The incident wave signal sent by the signal generator is a periodically changing sinusoidal pulse signal.

[0063] Step 2: The other end of the coiled cable is connected to the MEMS high-frequency voltage sensor; the MEMS high-frequency voltage sensor detects the reflected wave signal, and the reflected wave signal is transmitted to the signal processing device after being de-noised by a filter.

[0064] The incident wave signal will propagate along the cable.

[0065] In step 2, the MEMS high-frequency voltage sensor is placed near the end of the other end of the coiled cable, and the gap between the MEMS high-frequency voltage sensor and the other end of the cable is between 0.5 mm and 2 mm. In one possible embodiment, the MEMS high-frequency voltage sensor uses the MEMS cable voltage non-contact measurement sensor model XDY-W01. Based on the principle of electric field coupling voltage measurement, advanced MEMS technology is used to achieve non-contact measurement of DC voltage and AC voltage, which has the advantages of safe and convenient measurement.

[0066] The MEMS high-frequency voltage sensor is used to detect the voltage waveform of the reflected wave signal reflected from the cable. The voltage waveform is transmitted to the computer of the signal processing device, and the computer processes the reflected wave signal.

[0067] Step 3: The signal processing device performs discrete wavelet transform on the reflected wave signal to obtain a reconstructed and denoised local reflected wave signal:

[0068] Step 301: Improve the filter coefficient using the maximum signal-to-noise ratio function to obtain the optimal filter coefficient h' n ;

[0069] The specific method of step 301 is:

[0070] Step 3011: Use the current filter coefficient h n Filter the signal and calculate the SNR value. Where x(z) represents the filter coefficient h n The denoised signal after filtering, y(z) represents the noisy signal, and m represents the time length;

[0071] Step 3012: Calculate the SNR value with respect to the filter coefficient h based on the gradient optimization algorithm. n The gradient of the filter coefficient h is updated in the opposite direction of the gradient. n ;

[0072] Step 3013: until the number of iterations reaches the upper limit, the optimal filter coefficient h' is obtained. n .

[0073] By using this method, the signal-to-noise ratio performance of the filter can be directly improved. By improving the filter coefficient, the frequency response of the filter can be made more in line with the needs of practical applications, thereby reducing distortion and errors in the filtering process, which is suitable for weak traveling wave signals.

[0074] Step 302: Improve the symlet wavelet function: Where a represents the scale parameter, which controls the expansion and contraction of the wavelet function; b represents the translation parameter, which controls the position of the wavelet function on the time axis. ψ(t) represents the basic wavelet function, where h' n represents the optimal filter coefficient, and n represents the index of the optimal filter coefficient.

[0075] The Symlet wavelet itself has an approximate symmetric property, and based on the optimal filter coefficient h' n The improved Symlet wavelet function maintains this symmetry while optimizing the filter coefficient h' n The phase shift during signal reconstruction is further reduced, which is crucial for traveling wave signal processing that needs to maintain signal phase information. The improved Symlet wavelet function has a stronger ability to approximate non-stationary signals, which enables it to more accurately capture transient characteristics and subtle changes in traveling wave signals, and has good use effect.

[0076] Step 303: Decompose the reflected wave signal layer by layer based on the improved symlet wavelet function, extract the high-frequency components of each layer of wavelet and the original low-frequency components of the highest layer of wavelet, and compose the reconstructed and denoised local reflected wave signal.

[0077] Step 303 also includes:

[0078] Step 3031: Calculate the total noise energy E in the j-th layer decomposition j , E j =∑d j (k), where d j (k) is the detail coefficient in the j-th level decomposition, k represents time;

[0079] Step 3032, if E j+1 ≥E j , then the optimal number of decomposition layers is j.

[0080] Step 4: Identify the waveform of the reflected wave signal and obtain the classification of the cable fault.

[0081] The specific method of "identifying the first probability of cable fault according to the result of wavelet transform" in step 4 is:

[0082] Use support vector machine to build a cable fault classification model;

[0083] Using cable fault signal data of known categories, training the cable fault classification model, adjusting the parameters and structure of the cable fault classification model, and obtaining a trained cable fault classification model;

[0084] The reflected wave signal to be classified is subjected to discrete wavelet transform;

[0085] Extract features from the results of wavelet transform;

[0086] The extracted features are used as input to the cable fault classification model, and the cable fault classification model outputs the type or category of the cable fault.

[0087] Embodiment 2

[0088] like Figure 3 As shown, different from the first embodiment, this embodiment further includes the following steps:

[0089] Step 5: Calculate the reflection coefficient and infer the second probability of cable failure according to the reflection coefficient;

[0090] Step 6: Express the classification of the cable fault in step 4 by probability. The highest probability is the first probability. Multiply the first probability and the second probability by their respective confidence levels and then sum them to obtain the final fault classification result.

[0091] According to the results of wavelet transform, the first probability of cable fault is identified, focusing on the characteristics of sudden change of reflected wave waveform caused by different impedance of fault point during cable transmission of traveling wave signal. The cable fault point can be accurately located by judging the sudden change point on the waveform and the transmission speed of the electric wave in the cable.

[0092] The reflection coefficient is calculated and the second probability of cable fault is inferred based on the reflection coefficient. The reflection coefficient is related to the characteristic impedance of the cable and the load impedance of the fault point, so it can provide additional information about the nature of the fault.

[0093] By integrating the results of these two methods based on confidence, we can make full use of their respective technical advantages, verify and complement each other, reduce the possibility of misjudgment, improve the anti-interference ability of the detection system, make it more adaptable to complex environments, and analyze cable faults more comprehensively.

[0094] Example explanation of the fault status:

[0095] 1. When the cable is in good condition, the waveform of the reflected wave is usually a smooth curve, indicating that there is no obvious reflection from the sending end to the receiving end and the internal structure of the cable is uniform.

[0096] 2. Break: The waveform of the reflected wave has an obvious positive spike, followed by a long echo, indicating a break in the cable. By calculating the time delay of the spike, the distance between the fault and the test point can be accurately located. The reflection coefficient is close to 1, and the incident wave is set to be positive.

[0097] 3. Short circuit: The reflected wave waveform has a large negative pulse, which may be followed by some smaller fluctuations, indicating that two or more conductors are in direct contact or the insulation layer fails, resulting in a short circuit. The reflection coefficient is close to -1.

[0098] 4. High resistance: The waveform of the reflected wave rises slightly and then slowly falls, indicating that the line is not completely broken, but there is a high resistance in a certain section of the cable. The reflection coefficient is less than 1 but greater than 0.

[0099] 5. Low impedance: The waveform of the reflected wave is a small negative pulse, indicating that the line is not completely disconnected, and there is a certain resistance between the two conductors, forming a low impedance fault. The reflection coefficient is less than -1.

[0100] Embodiment 3

[0101] like Figure 4 and Figure 5 As shown, the cable reel fault detection system based on traveling wave signals of this embodiment includes a signal generator 1, and a MEMS high-frequency voltage sensor 5, a filter 6 and a signal processing device 8 connected in sequence. The signal generator 1 is connected to one end of the cable reel 4 through a BNC male connector 2 and a BNC female connector 3, and the MEMS high-frequency voltage sensor 5 is connected to the other end of the cable reel 4.

[0102] The MEMS high-frequency voltage sensor 5 captures the high-frequency voltage signal and its reflected wave signal. The reflected wave signal is denoised by the filter 6. The processed reflected wave signal is transmitted to the signal processing device 8 for wavelet transformation, signal reconstruction, waveform recognition and other operations.

[0103] In this embodiment, the MEMS high-frequency voltage sensor 5 is placed near the other end of the coiled cable, and the gap between the MEMS high-frequency voltage sensor 5 and the other end of the coiled cable 4 is between 0.5 mm and 2 mm.

[0104] In a possible embodiment, the MEMS high-frequency voltage sensor 5 is a MEMS cable voltage non-contact measurement sensor of model XDY-W01. Based on the principle of electric field coupling voltage measurement, it adopts advanced MEMS technology to achieve non-contact measurement of DC voltage and AC voltage, which has the advantages of safe and convenient measurement.

[0105] In this embodiment, a BNC connector bracket 9 is also included. The BNC connector bracket 9 includes a base and a U-shaped frame arranged on the base. A first connecting piece for connecting to the BNC male connector 2 is arranged on one side of the U-shaped frame, and a second connecting piece for connecting to the BNC female connector 3 is arranged on the other side of the U-shaped frame.

[0106] The BNC connector bracket 9 is used to limit the distance between the BNC male connector 2 and the BNC female connector 3, fix the flange on the BNC male connector 2 to the first connecting piece, insert the BNC female connector 3 into the BNC male connector 2, and then connect the flange of the BNC female connector 3 to the second connecting piece to avoid the accidental detachment of the BNC connector during the detection process.

[0107] In a possible embodiment, the BNC connector on the signal generator is a BNC female connector 3 .

[0108] First, connect one end of the reeled cable to the BNC male connector 2 of the BNC connector. The specific method is as follows: use wire strippers to strip a certain length of one end of the reeled cable to expose the internal copper core and shielding layer; cut off the plastic wrapping the copper core, and appropriately cut off part of the shielding layer to facilitate the insertion of the negative pole of the BNC male connector 2; twist the negative pole of the BNC male connector 2 and the shielding layer of the cable together, and then use an electric soldering iron to melt the solder and apply it on the copper core and shielding layer to solder them together; use a multimeter to test the insulation and conductivity to ensure that the BNC male connector 2 is connected correctly. Insert the needle-shaped center conductor of the BNC male connector 2 into the center hole of the BNC female connector 3 and make contact with the center conductor inside the BNC female connector 3.

[0109] The BNC connector has the characteristics of quick connection and removal, which is easy to install and maintain; it can ensure the stability and reliability of data transmission.

[0110] Then, the output parameters of the signal generator 1 are set according to the parameters such as the amplitude, frequency, and waveform of the incident wave signal to be sent. Ensure that the output level of the signal generator 1 matches the input level of the cable 4 to avoid abnormal operation or damage of the circuit caused by too high or too low input signals. In one possible embodiment, for a power cable with a rated voltage of 1kv, the incident wave is a sinusoidal pulse, and the amplitude of the sinusoidal pulse is set to 100V, which is lower than the rated voltage of the cable and provides a sufficient safety margin. The frequency of the sinusoidal pulse signal is set to 10kHz. This value is within the stable transmission characteristic range of the cable and meets the limitations of the test equipment. In the power system, the cables used to transmit electric energy and control signals usually require that the signal has a small loss during the transmission process to ensure the integrity and stability of the signal. Therefore, the signal generator outputs a sinusoidal pulse signal, the waveform of the sinusoidal pulse signal is smooth and continuous, and the spectrum is shown as a single spectrum line. The main energy is concentrated on the base frequency, and the loss during transmission in the cable is relatively small.

[0111] Next, place the MEMS high-frequency voltage sensor 5 near the end of the cable to ensure that the gap between the MEMS high-frequency voltage sensor 5 and the cable is moderate. In specific implementation, the gap is 1 mm to form effective capacitive coupling and avoid electrical interference and safety problems that may be caused by direct connection between the MEMS high-frequency voltage sensor and the cable.

[0112] Then, the signal generator 1 is started to send an incident wave signal into the cable. The incident wave signal is monitored using an oscilloscope 7 or other measuring equipment to ensure that the amplitude, frequency and waveform of the incident wave signal are consistent with expectations. The incident wave signal will propagate along the cable.

[0113] Next, the MEMS high-frequency voltage sensor 5 is used to detect the voltage waveform of the reflected wave signal reflected from the cable. The voltage waveform is transmitted to the signal processing device 8, and the fault type is analyzed by processing the reflected wave signal.

[0114] The above description is only an embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A cable fault detection method based on traveling wave signals, characterized in that: The following steps are involved: Step 1: Connect one end of the cable drum to the signal generator; set the parameters of the signal generator; start the signal generator and send an incident wave signal to the cable drum; Step 2: The other end of the coiled cable is connected to the MEMS high-frequency voltage sensor; the MEMS high-frequency voltage sensor detects the reflected wave signal, and the reflected wave signal is transmitted to the signal processing device after being de-noised by the filter; Step 3: The signal processing device performs discrete wavelet transform on the reflected wave signal to obtain a reconstructed and denoised local reflected wave signal: Step 301: Improve the filter coefficient using the maximum signal-to-noise ratio function to obtain the optimal filter coefficient h' n ; Step 302: Improve the symlet wavelet function: Where a represents the scale parameter, which controls the expansion and contraction of the wavelet function; b represents the translation parameter, which controls the position of the wavelet function on the time axis. ψ(t) represents the basic wavelet function, where h' n represents the optimal filter coefficient, and n represents the index of the optimal filter coefficient; Step 303: decompose the reflected wave signal layer by layer based on the improved symlet wavelet function, extract the high-frequency components of each layer of wavelet and the original low-frequency components of the highest layer of wavelet, and form a reconstructed and denoised local reflected wave signal; Step 4: Identify the waveform of the reflected wave signal and obtain the classification of the cable fault.

2. The method for detecting cable faults in a coil based on traveling wave signals according to claim 1, characterized in that: Also includes: Step 5: Calculate the reflection coefficient and infer the second probability of cable failure based on the reflection coefficient; Step 6: Express the classification of the cable fault in step 4 by probability. The highest probability is the first probability. Multiply the first probability and the second probability by their respective confidence levels and then sum them to obtain the final fault classification result.

3. The method for detecting cable faults in a coil based on traveling wave signals according to claim 1, characterized in that: The specific method of step 301 is: Step 3011: Use the current filter coefficient h n Filter the signal and calculate the SNR value. Where x(z) represents the filter coefficient h n The denoised signal after filtering, y(z) represents the noisy signal, and m represents the time length; Step 3012: Calculate the SNR value with respect to the filter coefficient h based on the gradient optimization algorithm. n The gradient of the filter is updated along the reverse direction of the gradient. n ; Step 3013: until the number of iterations reaches the upper limit, the optimal filter coefficient h' is obtained. n .

4. The method for detecting cable faults in a coil based on traveling wave signals according to claim 1, characterized in that: Step 303 also includes: Step 3031: Calculate the total noise energy E in the j-th layer decomposition j , E j =∑d j (k), where d j (k) is the detail coefficient in the j-th level decomposition, k represents time; Step 3032, if E j+1 ≥E j , then the optimal number of decomposition layers is j.

5. The method for detecting cable faults in a coil based on traveling wave signals according to claim 1, characterized in that: The specific method of "identifying the first probability of cable fault according to the result of wavelet transform" in step 4 is: Use support vector machine to build a cable fault classification model; Using cable fault signal data of known categories, training the cable fault classification model, adjusting the parameters and structure of the cable fault classification model, and obtaining a trained cable fault classification model; The reflected wave signal to be classified is subjected to discrete wavelet transform; Extract features from the results of wavelet transform; The extracted features are used as input to the cable fault classification model, and the cable fault classification model outputs the type or category of the cable fault.

6. The method for detecting cable faults in a coil based on traveling wave signals according to claim 1, characterized in that: In step 2, the MEMS high-frequency voltage sensor is placed near the end of the other end of the coiled cable, and the gap between the MEMS high-frequency voltage sensor and the other end of the cable is between 0.5 mm and 2 mm.

7. The method for detecting cable faults in a coil based on traveling wave signals according to claim 1, characterized in that: The incident wave signal sent by the signal generator is a periodically changing sinusoidal pulse signal.

8. A cable fault detection system based on traveling wave signals, characterized in that: The invention comprises a signal generator (1), and a MEMS high-frequency voltage sensor (5), a filter (6) and a signal processing device (8) which are connected in sequence. The signal generator (1) is connected to one end of a coiled cable (4) through a BNC male connector (2) and a BNC female connector (3), and the MEMS high-frequency voltage sensor (5) is connected to the other end of the coiled cable (4).

9. The cable coil fault detection system based on traveling wave signal according to claim 8, characterized in that: The MEMS high-frequency voltage sensor (5) is placed near the other end of the coiled cable, and the gap between the MEMS high-frequency voltage sensor (5) and the other end of the coiled cable (4) is between 0.5 mm and 2 mm.

10. The cable coil fault detection system based on traveling wave signal according to claim 8, characterized in that: It also includes a BNC connector bracket (9), which includes a base and a U-shaped frame arranged on the base, a first connector for connecting to a BNC male connector (2) is arranged on one side of the U-shaped frame, and a second connector for connecting to a BNC female connector (3) is arranged on the other side of the U-shaped frame.

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