Disc cable fault detection method and system based on traveling wave signal

Abstract

This invention discloses a method for fault detection in coiled cables based on traveling wave signals, comprising: Step 1, connecting both ends of the coiled cable to a signal generator and a MEMS high-frequency voltage sensor respectively; setting the parameters of the signal generator; and starting the signal generator; Step 2, the MEMS high-frequency voltage sensor detects the reflected wave signal, which is then filtered and denoised before being transmitted to a signal processing device; 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 4, identifying the waveform of the reflected wave signal to classify the cable fault. This invention uses a MEMS high-frequency voltage sensor to sense weak traveling wave signals; it utilizes the maximum signal-to-noise ratio function to improve the filter coefficients, obtaining optimal filter coefficients, which makes the frequency response of the filter more suitable for practical applications, thereby reducing distortion and error in the filtering process, and is applicable to weak traveling wave signals.

Description

Technical Field

[0001] This invention belongs to the field of electrical fault testing technology, specifically relating to a method and system for detecting faults in coiled cables based on traveling wave signals. Background Technology

[0002] The idea of ​​using the traveling wave method to measure cable faults was first proposed by Seidu in 1948, and subsequently, various cable fault detection methods emerged, such as the low-voltage pulse method, the pulse voltage method, and the pulse current method. Among them, the low-voltage pulse method is characterized by its simplicity, low cost, and safety, but it cannot detect high-resistance faults. With technological advancements, the traveling wave method for cable fault detection has gradually developed into an important non-destructive testing technique.

[0003] However, the most significant problem encountered in detecting faults in reeled cables using traveling wave signals is the serious consequences of signal attenuation.

[0004] First, signal attenuation reduces the amplitude of the traveling wave signal, causing waveform distortion. When the signal strength decreases to a certain level, it may lead to data transmission errors or loss, affecting the accuracy of fault location. Second, as signal attenuation increases, communication distance becomes limited. When signal attenuation reaches a certain level, stable communication may not be maintained, preventing the traveling wave signal from effectively reaching the detection point and thus hindering fault location. Third, signal attenuation can also cause system instability, leading to communication failures or data loss, thus affecting the normal operation of the entire system. During the detection of cable reel faults using traveling wave signals, system instability may lead to false alarms or missed faults, posing a threat to the safe operation of the power system. Finally, because signal attenuation may result in inaccurate or undetectable fault location, more time and manpower are required for troubleshooting and repair. This not only increases maintenance costs but also affects 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 reel faults. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a fault detection method for coiled cables based on traveling wave signals, which addresses the shortcomings of the prior art. The method uses a MEMS high-frequency voltage sensor to sense weak traveling wave signals; it improves the filter coefficients by using the maximum signal-to-noise ratio function to obtain the optimal filter coefficients, which makes the frequency response of the filter more in line with the needs of practical applications, thereby reducing distortion and error in the filtering process, and is suitable for weak traveling wave signals.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for fault detection of coiled cables based on traveling wave signals, characterized by including the following steps:

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

[0009] Step 2: Connect the other end of the coiled cable 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 filtered for noise reduction.

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

[0011] Step 301: Improve the filter coefficients using the maximum signal-to-noise ratio function to obtain the optimal filter coefficients. ;

[0012] Step 302: Improve the symlet wavelet function: ,in 'b' represents the scaling parameter, which controls the scaling of the wavelet function; 'b' represents the translation parameter, which controls the position of the wavelet function on the time axis. Represents the basic wavelet function. ,in This represents the optimal filter coefficients, where n represents the index of the optimal filter coefficients;

[0013] Step 303: Based on the improved symlet wavelet function, the reflected wave signal is decomposed layer by layer downwards, and the high-frequency components of each layer of wavelet and the low-frequency components of the original highest layer wavelet are extracted to form the reconstructed and denoised local reflected wave signal.

[0014] Step 4: Identify the waveform of the local reflected wave signal to classify the cable fault.

[0015] The above-mentioned method for fault detection of coiled cables based on traveling wave signals is characterized by further comprising:

[0016] Step 5: Calculate the reflection coefficient and infer the second probability of cable fault based on the reflection coefficient;

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

[0018] The above-mentioned method for fault detection of coiled cables based on traveling wave signals is characterized in that: the specific method of step 301 is as follows:

[0019] Step 3011: Use the current filter coefficients Filter the signal and calculate the SNR value. ,in Represents the coefficients after filtering The filtered and denoised signal This represents a noisy signal, where m represents the time length.

[0020] Step 3012: Based on the gradient optimization algorithm, calculate the SNR value with respect to the filter coefficients. The gradient is used to update the filter coefficients in the opposite direction of the gradient. ;

[0021] Step 3013: Continue until the maximum number of iterations is reached to obtain the optimal filter coefficients. .

[0022] The above-mentioned method for fault detection of coiled cables based on traveling wave signals is characterized in that: step 303 further includes:

[0023] Step 3031: Calculate the total noise energy in the j-th layer decomposition. , ,in Here, k represents the detail coefficients in the j-th level decomposition;

[0024] Step 3032, if If so, the optimal number of decomposition layers is j.

[0025] The above-mentioned method for fault detection of coiled cables based on traveling wave signals is characterized in that: the specific method of step four is as follows:

[0026] A cable fault classification model was built using support vector machines.

[0027] The cable fault classification model is trained using known categories of cable fault signal data. The parameters and structure of the cable fault classification model are then adjusted to obtain a well-trained cable fault classification model.

[0028] The reflected wave signals to be classified are subjected to discrete wavelet transform;

[0029] Extract features from the wavelet transform results;

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

[0031] The above-mentioned method for fault detection of coiled cables based on traveling wave signals is characterized in that: in step two, the MEMS high-frequency voltage sensor is placed near 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 0.5mm and 2mm.

[0032] The above-mentioned method for detecting faults in coiled cables based on traveling wave signals is characterized in that the incident wave signal sent by the signal generator is a periodically changing sinusoidal pulse signal.

[0033] The present invention relates to a fault detection system for coiled cables based on traveling wave signals, 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 coiled cable via a BNC male connector and a BNC female connector, and the MEMS high-frequency voltage sensor is connected to the other end of the coiled cable.

[0034] The aforementioned fault detection system for coiled cables based on traveling wave signals is characterized in that: a MEMS high-frequency voltage sensor is placed near 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.

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

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

[0037] 1. The present invention has a simple structure, reasonable design, and is convenient to implement and use.

[0038] 2. This invention uses a MEMS high-frequency voltage sensor to achieve non-contact voltage measurement. The MEMS high-frequency voltage sensor has the advantages of high precision and low loss, and can accurately sense weak traveling wave signals, thus improving the accuracy of voltage measurement. The sensor does not contain iron cores, windings or 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 acquired weak traveling wave signal.

[0040] 4. This invention applies wavelet transform to decompose traveling wave signals at multiple scales, thereby gradually refining the signal at multiple scales, focusing on arbitrary details of the signal, and extracting the features of the signal in different frequency ranges, providing a basis for subsequent fault detection.

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

[0042] In summary, this invention has a simple structure and reasonable design. It uses a MEMS high-frequency voltage sensor to sense weak traveling wave signals; it applies wavelet transform to decompose the traveling wave signals into multiple scales; and it uses the maximum signal-to-noise ratio function to improve the filter coefficients and obtain the optimal filter coefficients. This makes the frequency response of the filter more in line with the needs of practical applications, thereby reducing distortion and error in the filtering process. It is suitable for weak traveling wave signals.

[0043] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

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

[0045] Figure 2 This is a flowchart of the wavelet transform method of the present invention.

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

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

[0048] Figure 5 This is a schematic diagram of the installation of the BNC connector bracket of the present invention.

[0049] Explanation of reference numerals in the attached figures:

[0050] Detailed Implementation

[0051] The method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0052] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0053] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0054] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0055] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0056] Example 1

[0057] like Figure 1 and Figure 2 As shown, the present invention provides a method for fault detection of coiled cables based on traveling wave signals, comprising the following steps:

[0058] Step 1: Connect one end of the coiled cable to the signal generator; set the parameters of the signal generator; start the signal generator and send the incident wave signal into the coiled cable.

[0059] Then, based on the amplitude, frequency, waveform, and other parameters of the incident wave signal to be sent, set the output parameters of the signal generator. Ensure that the output level of the signal generator matches the input level of the cable to avoid abnormal circuit operation or damage caused by excessively high or low input signals. In one possible embodiment, for a power cable with a rated voltage of 1kV, the incident wave is a sine pulse, and the amplitude of the sine pulse is set to 100V. This value is lower than the rated voltage of the cable but provides a sufficient safety margin. The frequency of the sine pulse signal is set to 10kHz. This value is within the stable transmission characteristics range of the cable and meets the limitations of the test equipment. In power systems, cables used to transmit electrical energy and control signals typically require low signal loss during transmission to ensure signal integrity and stability. Therefore, the signal generator outputs a sine pulse signal. The waveform of the sine pulse signal is smooth and continuous, and its spectrum is represented by a single spectral line. The main energy is concentrated on the fundamental frequency, and the loss during transmission in the cable is relatively small.

[0060] The incident wave signal sent by the signal generator is a periodically changing sinusoidal pulse signal.

[0061] Step 2: Connect the other end of the coiled cable 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 filtered for noise reduction.

[0062] The incident wave signal will propagate along the cable.

[0063] In step two, the MEMS high-frequency voltage sensor is placed near the other end of the coiled cable, with a gap of 0.5mm to 2mm between the MEMS high-frequency voltage sensor and the other end of the cable. In one possible embodiment, the MEMS high-frequency voltage sensor is a model XDY-W01 MEMS cable voltage non-contact measurement sensor. Based on the principle of electric field coupling voltage measurement, and employing advanced MEMS technology, it achieves non-contact measurement of DC and AC voltage, offering advantages of safety and convenience.

[0064] MEMS high-frequency voltage sensors are used to detect the voltage waveform of reflected wave signals from cables. The voltage waveform is transmitted to the computer of the signal processing device, and the computer processes the reflected wave signal.

[0065] Step 3: The signal processing device performs discrete wavelet transform on the reflected wave signal to obtain the reconstructed and denoised local reflected wave signal.

[0066] Step 301: Improve the filter coefficients using the maximum signal-to-noise ratio function to obtain the optimal filter coefficients. ;

[0067] The specific method for step 301 is as follows:

[0068] Step 3011: Use the current filter coefficients Filter the signal and calculate the SNR value. ,in Represents the coefficients after filtering The filtered and denoised signal This represents a noisy signal, where m represents the time length.

[0069] Step 3012: Based on the gradient optimization algorithm, calculate the SNR value with respect to the filter coefficients. The gradient is used to update the filter coefficients in the opposite direction of the gradient. ;

[0070] Step 3013: Continue until the maximum number of iterations is reached to obtain the optimal filter coefficients. .

[0071] This method can directly improve the signal-to-noise ratio performance of the filter. By improving the filter coefficients, the frequency response of the filter can be made to better meet the needs of practical applications, thereby reducing distortion and error in the filtering process. It is suitable for weak traveling wave signals.

[0072] Step 302: Improve the symlet wavelet function: ,in 'b' represents the scaling parameter, which controls the scaling of the wavelet function; 'b' represents the translation parameter, which controls the position of the wavelet function on the time axis. Represents the basic wavelet function. ,in represents the optimal filter coefficients, and n represents the index of the optimal filter coefficients.

[0073] Symlet wavelets inherently possess approximately symmetric properties, and based on optimal filter coefficients... The improved Symlet wavelet function, while maintaining this symmetry, optimizes the filter coefficients. The improved Symlet wavelet function further reduces the phase shift during signal reconstruction, which is crucial for traveling wave signal processing that requires preserving signal phase information. It has a stronger ability to approximate non-stationary signals, enabling it to more accurately capture transient features and subtle changes in traveling wave signals, resulting in good performance.

[0074] Step 303: Based on the improved symbol wavelet function, the reflected wave signal is decomposed layer by layer downwards, and the high-frequency components of each layer of wavelet and the low-frequency components of the original highest layer wavelet are extracted to form the reconstructed and denoised local reflected wave signal.

[0075] Step 303 also includes:

[0076] Step 3031: Calculate the total noise energy in the j-th layer decomposition. , ,in Here, k represents the detail coefficients in the j-th level decomposition;

[0077] Step 3032, if If so, the optimal number of decomposition layers is j.

[0078] Step 4: Identify the waveform of the local reflected wave signal to classify the cable fault.

[0079] The specific method for step four is as follows:

[0080] A cable fault classification model was built using support vector machines.

[0081] The cable fault classification model is trained using known categories of cable fault signal data. The parameters and structure of the cable fault classification model are then adjusted to obtain a well-trained cable fault classification model.

[0082] The reflected wave signals to be classified are subjected to discrete wavelet transform;

[0083] Extract features from the wavelet transform results;

[0084] The extracted features are used as input to the cable fault classification model, which outputs the type or category of the cable fault.

[0085] Example 2

[0086] like Figure 3 As shown, unlike Embodiment 1, this embodiment also includes the following steps:

[0087] Step 5: Calculate the reflection coefficient and infer the second probability of cable fault based on the reflection coefficient;

[0088] Step 6: Represent the cable fault classification in Step 4 using probabilities. 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.

[0089] Based on the results of wavelet transform, the first probability of identifying cable faults focuses on utilizing the characteristic that the reflected wave waveform changes abruptly due to the different impedances at the fault point during the transmission of the traveling wave signal in the cable. The fault point in the cable is accurately located by judging the abrupt change point on the waveform diagram and the transmission speed of the electromagnetic wave in the cable.

[0090] The reflection coefficient is calculated, and a second probability of cable fault is inferred from the reflection coefficient. The reflection coefficient is related to the characteristic impedance of the cable and the load impedance at the fault point, so it can provide additional information about the nature of the fault.

[0091] By fusing the results of these two methods based on confidence levels, their respective technical advantages can be fully utilized. They can be mutually verified and complemented, reducing the possibility of misjudgment, improving the anti-interference capability of the detection system, making it more adaptable to complex environments, and enabling a more comprehensive analysis of cable faults.

[0092] Example explanation of fault states:

[0093] 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 transmitting end to the receiving end, and the internal structure of the cable is uniform.

[0094] 2. Open Circuit: The reflected wave waveform has a distinct positive spike, followed by a long echo, indicating an open circuit fault in the cable. By calculating the time delay of the spike's appearance, the distance of the fault from the test point can be accurately located. The reflection coefficient is close to 1, and the incident wave is set to positive.

[0095] 3. Short circuit: The reflected wave waveform shows a large negative pulse, possibly followed by some smaller fluctuations, indicating that two or more conductors are in direct contact or that the insulation layer has failed, causing a short circuit. The reflection coefficient will be close to -1.

[0096] 4. High resistance: The reflected wave waveform rises slightly and then slowly declines, indicating that the line is not completely broken, but there is high resistance in a certain section of the cable. The reflection coefficient is less than 1 but greater than 0.

[0097] 5. Low impedance: The reflected wave waveform appears as a small negative pulse, indicating that the line is not completely broken and there is a certain resistance between the two conductors, forming a low impedance fault. The reflection coefficient is less than -1.

[0098] Example 3

[0099] like Figure 4 and Figure 5 As shown, the traveling wave signal-based cable reel fault detection system 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 reel 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 reel cable 4.

[0100] MEMS high-frequency voltage sensor 5 captures high-frequency voltage signals and their reflected wave signals. The reflected wave signals are denoised by filter 6. The processed reflected wave signals are transmitted to signal processing device 8 for wavelet transform, signal reconstruction, waveform recognition and other operations.

[0101] In this embodiment, the MEMS high-frequency voltage sensor 5 is placed near the other end of the coiled cable 4, 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.

[0102] In one possible embodiment, the MEMS high-frequency voltage sensor 5 is a model XDY-W01 MEMS cable voltage non-contact measurement sensor. Based on the principle of electric field coupling voltage measurement, it uses advanced MEMS technology to realize non-contact measurement of DC and AC voltage, which has the advantages of safe and convenient measurement.

[0103] In this embodiment, a BNC connector bracket 9 is also included. The BNC connector bracket 9 includes a base and a U-shaped frame disposed on the base. A first connector for connecting the BNC male connector 2 is disposed on one side of the U-shaped frame, and a second connector for connecting the BNC female connector 3 is disposed on the other side of the U-shaped frame.

[0104] BNC connector bracket 9 is used to limit the distance between BNC male connector 2 and BNC female connector 3, fix the flange on BNC male connector 2 to the first connector, insert BNC female connector 3 into BNC male connector 2, and then connect the flange of BNC female connector 3 to the second connector to avoid accidental detachment of BNC connector during testing.

[0105] In one possible embodiment, the BNC connector on the signal generator is a BNC female connector 3.

[0106] First, connect one end of the coiled cable to the BNC male connector 2 of the BNC connector. Specifically: use wire strippers to strip a certain length of the coiled cable to expose the internal copper core and shielding layer; remove the plastic covering the copper core and trim a portion of the shielding layer to facilitate insertion of the negative terminal of the BNC male connector 2; twist the negative terminal of the BNC male connector 2 to the cable's shielding layer; then use a soldering iron to melt solder and apply it to the copper core and shielding layer, soldering them together; use a multimeter to test the insulation and conductivity to ensure the BNC male connector 2 is connected correctly. Insert the pin-shaped center conductor of the BNC male connector 2 into the center hole of the BNC female connector 3, making contact with the center conductor inside the BNC female connector 3.

[0107] BNC connectors feature quick connection and disconnection, facilitating installation and maintenance; they also ensure the stability and reliability of data transmission.

[0108] Then, based on the amplitude, frequency, waveform, and other parameters of the incident wave signal to be sent, the output parameters of signal generator 1 are set. Ensure that the output level of signal generator 1 matches the input level of the coiled cable 4 to avoid abnormal circuit operation or damage caused by excessively high or low input signals. In one possible embodiment, for a power cable with a rated voltage of 1kV, the incident wave is a sine pulse, and the amplitude of the sine pulse is set to 100V. This value is lower than the rated voltage of the cable while providing sufficient safety margin. The frequency of the sine pulse signal is set to 10kHz. This value is within the stable transmission characteristics range of the cable and meets the limitations of the testing equipment. In power systems, cables used for transmitting electrical energy and control signals typically require low signal loss during transmission to ensure signal integrity and stability. Therefore, the signal generator outputs a sine pulse signal. The waveform of the sine pulse signal is smooth and continuous, exhibiting a single spectral line in the spectrum, with the main energy concentrated at the fundamental frequency, resulting in relatively low loss during transmission in the cable.

[0109] Next, place the MEMS high-frequency voltage sensor 5 near the end of the cable, ensuring that the gap between the MEMS high-frequency voltage sensor 5 and the cable is appropriate. In practice, the gap is 1mm to form effective capacitive coupling and avoid electrical interference and safety issues that may be caused by direct connection between the MEMS high-frequency voltage sensor and the cable.

[0110] Next, start signal generator 1 to send an incident wave signal into the cable. Use oscilloscope 7 or other measuring equipment to monitor the incident wave signal, ensuring that its amplitude, frequency, and waveform are consistent with expectations. The incident wave signal will propagate along the cable.

[0111] Next, the MEMS high-frequency voltage sensor 5 is used to detect the voltage waveform of the reflected wave signal reflected back from the cable. The voltage waveform is transmitted to the signal processing device 8, which analyzes the fault type by processing the reflected wave signal.

[0112] The above description is merely an embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for fault detection in coiled cables based on traveling wave signals, characterized in that: Includes the following steps: Step 1: Connect one end of the coiled cable to the signal generator; set the parameters of the signal generator; start the signal generator and send the incident wave signal into the coiled cable; Step 2: Connect the other end of the coiled cable 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 filtered for noise reduction. Step 3: The signal processing device performs discrete wavelet transform on the reflected wave signal to obtain the reconstructed and denoised local reflected wave signal. Step 301: Improve the filter coefficients using the maximum signal-to-noise ratio function to obtain the optimal filter coefficients. ; Step 302: Improve the symlet wavelet function: ,in 'b' represents the scaling parameter, controlling the scaling of the wavelet function; 'b' represents the translation parameter, controlling the position of the wavelet function on the time axis. Represents the basic wavelet function. ,in This represents the optimal filter coefficients, where n represents the index of the optimal filter coefficients; Step 303: Based on the improved symlet wavelet function, the reflected wave signal is decomposed layer by layer downwards, and the high-frequency components of each layer of wavelet and the low-frequency components of the original highest layer wavelet are extracted to form the reconstructed and denoised local reflected wave signal. Step 4: Identify the waveform of the local reflected wave signal to classify the cable fault.

2. The method for detecting faults in coiled cables 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 fault based on the reflection coefficient; Step 6: Represent the cable fault classification in Step 4 using probabilities. 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 faults in coiled cables based on traveling wave signals according to claim 1, characterized in that: The specific method for step 301 is as follows: Step 3011: Use the current filter coefficients Filter the signal and calculate the SNR value. ,in Represents the coefficients after filtering The filtered and denoised signal This represents a noisy signal, where m represents the time length. Step 3012: Based on the gradient optimization algorithm, calculate the SNR value with respect to the filter coefficients. The gradient is used to update the filter coefficients in the opposite direction of the gradient. ; Step 3013: Continue until the maximum number of iterations is reached to obtain the optimal filter coefficients. .

4. The method for detecting faults in coiled cables based on traveling wave signals according to claim 1, characterized in that: Step 303 also includes: Step 3031: Calculate the total noise energy in the j-th layer decomposition. , ,in Here, k represents the detail coefficients in the j-th level decomposition; Step 3032, if If so, the optimal number of decomposition layers is j.

5. The method for detecting faults in coiled cables based on traveling wave signals according to claim 1, characterized in that: The specific method for step four is as follows: A cable fault classification model was built using support vector machines. The cable fault classification model is trained using known categories of cable fault signal data. The parameters and structure of the cable fault classification model are then adjusted to obtain a well-trained cable fault classification model. The reflected wave signals to be classified are subjected to discrete wavelet transform; Extract features from the wavelet transform results; The extracted features are used as input to the cable fault classification model, which outputs the type or category of the cable fault.

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

7. The method for fault detection of coiled cables 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 fault detection system for coiled cables based on traveling wave signals, characterized in that: The method for detecting faults in a coiled cable based on traveling wave signals as described in any one of claims 1-7 includes a signal generator (1), 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 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 reel fault detection system based on traveling wave signals 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 reel fault detection system based on traveling wave signals according to claim 8, characterized in that: It also includes a BNC connector bracket (9), which includes a base and a U-shaped frame set on the base. One side of the U-shaped frame is provided with a first connector for connecting the BNC male connector (2), and the other side of the U-shaped frame is provided with a second connector for connecting the BNC female connector (3).

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