A method for detecting partial discharge of cables

Abstract

The present invention discloses a method for detecting partial discharge of a cable in the field of power cable detection, comprising the following steps: S101, installing a plurality of high-frequency current sensors on the grounding wire of the cable, and installing a plurality of ultrasonic sensors and ultra-high frequency sensors on the surface of the outer sheath of the cable. The present invention comprehensively uses high-frequency current sensors, ultrasonic sensors and ultra-high frequency sensors to detect partial discharge of the cable, fully utilizes the sensitivity of different sensors to different types of partial discharge signals, realizes the complementary advantages of multiple detection methods, improves the accuracy and reliability of detection, and can more comprehensively and accurately judge the partial discharge of the cable through comprehensive analysis of the signals of the three sensors.

Description

Technical Field

[0001] The invention relates to the field of power cable detection, in particular to a method for detecting partial discharge of a cable. Background Art

[0002] With the continuous development of the power system, the safe and stable operation of cables as an important carrier of power transmission is of vital importance. In the long-term operation of cables, due to the influence of various factors such as electricity, heat, and mechanics, the insulation performance will gradually decline, thus causing partial discharge. If partial discharge cannot be detected and handled in time, it will accelerate the aging and damage of cable insulation. In severe cases, it may cause cable failure, resulting in power outages and huge losses to social production and life.

[0003] At present, the existing cable partial discharge detection methods have many shortcomings. For example, although the traditional electrical detection method can detect the electrical signal generated by partial discharge, it is easily affected by the electromagnetic interference of other electrical equipment in the power grid, which greatly reduces the accuracy and reliability of the detection results; although the acoustic detection method is more sensitive to the ultrasonic signal generated by partial discharge, in the complex field environment, the noise interference is serious, making it difficult to effectively extract and identify the weak partial discharge ultrasonic signal; the optical detection method has high requirements for the detection equipment and the detection range is limited, making it difficult to achieve comprehensive detection of long-distance cables.

[0004] Therefore, those skilled in the art provide a method for detecting partial discharge of cables to solve the problems raised in the above background technology. Summary of the invention

[0005] The object of the present invention is to provide a method for detecting partial discharge of a cable to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for detecting partial discharge of a cable comprises the following steps: S101. Install multiple high-frequency current sensors on the ground wire of the cable, and install multiple ultrasonic sensors and ultra-high frequency sensors on the outer sheath surface of the cable, wherein the bandwidth range of the high-frequency current sensor is 10kHz-100MHz, the center frequency of the ultrasonic sensor is 20kHz-200kHz, and the operating frequency range of the ultra-high frequency sensor is 300MHz-3GHz; S102, synchronously collecting signals collected by the high-frequency current sensor, the ultrasonic sensor and the ultra-high frequency sensor using a data acquisition card, wherein the sampling frequency of the data acquisition card is above 1 GHz and the resolution is above 12 bits; S103, respectively denoising the collected high-frequency current signal, ultrasonic signal and ultra-high frequency signal, using a wavelet transform algorithm, selecting the Daubechies wavelet basis function, decomposing the number of layers into 3-6 layers, removing the noise component through threshold processing and then reconstructing; S104, normalizing the denoised signal to adjust the amplitude range of the signal to between [0, 1]; S105, extracting characteristic parameters such as pulse amplitude, pulse width, pulse rise time, and pulse repetition frequency from the high-frequency current signal, extracting characteristics such as frequency components, energy distribution, and arrival time of the signal from the ultrasonic signal, and extracting parameters such as spectrum characteristics, phase characteristics, and number of pulses of the signal from the ultra-high frequency signal; S106, using a principal component analysis method to perform dimensionality reduction processing on the extracted multiple feature parameters, and selecting the first few principal components with larger contribution rates; S107, using a support vector machine algorithm to establish a cable partial discharge fault diagnosis model, dividing the data set after feature extraction and dimensionality reduction processing into a training set and a test set, training the support vector machine model through the training set, and adjusting the parameters of the model, including the kernel function type, penalty factor, etc.; S108. For the detected partial discharge fault, a positioning method based on time difference is used to locate the fault. According to the different propagation speeds of high-frequency current signals, ultrasonic signals and ultra-high frequency signals in the cable, the arrival time difference of the signals collected by the sensor is used, combined with the cable length, wave speed and other parameters, and the position of the partial discharge fault point is calculated by establishing a mathematical model.

[0007] As a further solution of the present invention: the high-frequency current sensor in step S101 is a Rogowski coil current sensor, which adopts a high-sensitivity, wide-band Rogowski coil high-frequency current sensor with a bandwidth range of 10kHz-100MHz. It can accurately collect high-frequency current pulse signals generated by partial discharge of the cable. The parameters such as the number of coil turns and magnetic permeability of the sensor are optimized to improve its response capability to weak current signals and anti-interference performance.

[0008] As a further solution of the present invention: the ultrasonic sensor in step S101 is a piezoelectric ultrasonic sensor with a center frequency of 20kHz-200kHz, which is used to detect ultrasonic signals generated by local discharge. The surface of the sensor is processed with special acoustic matching materials to enhance its ultrasonic receiving efficiency and reduce the influence of external environmental noise.

[0009] As a further solution of the present invention: the UHF sensor in step S101 is a UHF sensor designed based on the microstrip antenna principle, with an operating frequency range of 300MHz-3GHz, and is used to capture the UHF electromagnetic wave signal generated by local discharge. The antenna shape, size and feeding method of the sensor are carefully designed to achieve efficient reception of UHF signals and good frequency selectivity.

[0010] As a further solution of the present invention: in the step S102, the signal collected by the high-frequency current sensor is amplified by a preamplifier with an amplification factor of 10-100 times, and is filtered by a bandpass filter composed of a low-pass filter and a high-pass filter, and the passband frequency range of the bandpass filter is 50kHz-50MHz.

[0011] As a further solution of the present invention: in step S102, for the signal of the ultrasonic sensor, the weak charge signal is first converted into a voltage signal and amplified by a charge amplifier with an amplification factor of 50-200 times, and then a bandpass filter is used to remove low-frequency noise and high-frequency interference, and the passband frequency range of the bandpass filter is 30kHz-150kHz.

[0012] As a further solution of the present invention: in step S102, the signal collected by the UHF sensor is amplified by a radio frequency amplifier with an amplification factor of 20-80 times, and filtered by a bandpass filter with a passband frequency range of 500MHz-2.5GHz.

[0013] As a further solution of the present invention: in step S107, the kernel function types of the support vector machine algorithm include radial basis function, linear function, etc., and the appropriate kernel function and penalty factor are selected through multiple training and optimization to improve the accuracy of fault diagnosis.

[0014] As a further solution of the present invention: in the positioning method based on time difference in step S108, for the ultrasonic signal and the high-frequency current signal, it is assumed that their propagation speeds in the cable are and , the distance between the two sensors is d, and the signal arrival time difference is , then the distance between the fault point and one of the sensors is given by the formula It is calculated that for the combined positioning of UHF signals and other signals, a similar mathematical model is established according to the corresponding wave speed and time difference for calculation.

[0015] As a further solution of the present invention: the cable partial discharge fault diagnosis model can classify and diagnose the partial discharge state of the cable, including a normal state, a slight discharge state, a moderate discharge state and a severe discharge state.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses a combination of high-frequency current sensors, ultrasonic sensors and ultra-high frequency sensors to detect partial discharge of cables, making full use of the sensitivity of different sensors to different types of partial discharge signals, realizing the complementary advantages of multiple detection methods, and improving the accuracy and reliability of detection. For example, high-frequency current sensors can accurately detect high-frequency current pulses generated by partial discharge, ultrasonic sensors can detect ultrasonic signals generated by mechanical vibrations caused by partial discharge, and ultra-high frequency sensors have good response characteristics to ultra-high frequency electromagnetic waves generated by partial discharge. Through comprehensive analysis of the three sensor signals, the partial discharge of cables can be judged more comprehensively and accurately.

[0017] 2. The present invention uses wavelet transform to perform signal denoising and principal component analysis to perform feature extraction and dimensionality reduction, effectively removing noise interference in the signal, extracting key feature information, and improving the quality of the signal and the representativeness of the features. At the same time, the support vector machine is used for fault diagnosis and the positioning method based on time difference has high accuracy and stability, can quickly and accurately judge the partial discharge fault state of the cable and locate the fault point, and provides strong support for the maintenance and repair of the cable.

[0018] 3. By optimizing the design and performance parameters of the sensor and adopting effective signal conditioning and filtering technology, the detection method of the present invention has high sensitivity and can detect weak partial discharge signals. At the same time, in the process of signal acquisition and processing, through a variety of anti-interference measures, such as bandpass filtering, shielding technology, etc., the influence of external electromagnetic interference, noise interference, etc. on the detection results is effectively reduced, ensuring the reliability and stability of the detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the method of the present invention; DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 In an embodiment of the present invention, a method for detecting partial discharge of a cable includes the following steps: S101. Install multiple high-frequency current sensors on the ground wire of the cable, and install multiple ultrasonic sensors and ultra-high frequency sensors on the outer sheath surface of the cable, wherein the bandwidth range of the high-frequency current sensor is 10kHz-100MHz, the center frequency of the ultrasonic sensor is 20kHz-200kHz, and the operating frequency range of the ultra-high frequency sensor is 300MHz-3GHz; S102, synchronously collecting signals collected by the high-frequency current sensor, the ultrasonic sensor and the ultra-high frequency sensor using a data acquisition card, wherein the sampling frequency of the data acquisition card is above 1 GHz and the resolution is above 12 bits; S103, respectively denoising the collected high-frequency current signal, ultrasonic signal and ultra-high frequency signal, using a wavelet transform algorithm, selecting the Daubechies wavelet basis function, decomposing the number of layers into 3-6 layers, removing the noise component through threshold processing and then reconstructing; S104, normalizing the denoised signal to adjust the amplitude range of the signal to between [0, 1]; S105, extracting characteristic parameters such as pulse amplitude, pulse width, pulse rise time, and pulse repetition frequency from the high-frequency current signal, extracting characteristics such as frequency components, energy distribution, and arrival time of the signal from the ultrasonic signal, and extracting parameters such as spectrum characteristics, phase characteristics, and number of pulses of the signal from the ultra-high frequency signal; S106, using a principal component analysis method to perform dimensionality reduction processing on the extracted multiple feature parameters, and selecting the first few principal components with larger contribution rates; S107, using a support vector machine algorithm to establish a cable partial discharge fault diagnosis model, dividing the data set after feature extraction and dimensionality reduction processing into a training set and a test set, training the support vector machine model through the training set, and adjusting the parameters of the model, including the kernel function type, penalty factor, etc.; S108. For the detected partial discharge fault, a positioning method based on time difference is used to locate the fault. According to the different propagation speeds of high-frequency current signals, ultrasonic signals and ultra-high frequency signals in the cable, the arrival time difference of the signals collected by the sensor is used, combined with the cable length, wave speed and other parameters, and the position of the partial discharge fault point is calculated by establishing a mathematical model.

[0022] Among them, the high-frequency current sensor in step S101 is a Rogowski coil current sensor, which adopts a high-sensitivity, wide-band Rogowski coil high-frequency current sensor with a bandwidth range of 10kHz-100MHz. It can accurately collect high-frequency current pulse signals generated by partial discharge of cables. The number of coil turns, magnetic permeability and other parameters of the sensor are optimized to improve its response capability to weak current signals and anti-interference performance.

[0023] Among them, the ultrasonic sensor in step S101 is a piezoelectric ultrasonic sensor with a center frequency of 20kHz-200kHz, which is used to detect ultrasonic signals generated by partial discharge. The surface of the sensor is processed with special acoustic matching materials to enhance its ultrasonic reception efficiency and reduce the impact of external environmental noise.

[0024] Among them, the UHF sensor in step S101 is a UHF sensor designed based on the microstrip antenna principle, with an operating frequency range of 300MHz-3GHz, and is used to capture the UHF electromagnetic wave signal generated by partial discharge. The antenna shape, size and feeding method of the sensor are carefully designed to achieve efficient reception of UHF signals and good frequency selectivity.

[0025] Among them, in the step S102, the signal collected by the high-frequency current sensor is amplified by a preamplifier with an amplification factor of 10-100 times, and is filtered by a bandpass filter composed of a low-pass filter and a high-pass filter, and the passband frequency range of the bandpass filter is 50kHz-50MHz.

[0026] Among them, in the step S102, for the signal of the ultrasonic sensor, the weak charge signal is first converted into a voltage signal and amplified by a charge amplifier with an amplification factor of 50-200 times, and then a bandpass filter is used to remove low-frequency noise and high-frequency interference, and the passband frequency range of the bandpass filter is 30kHz-150kHz.

[0027] Wherein, in step S102, the signal collected by the UHF sensor is amplified by a radio frequency amplifier with an amplification factor of 20-80 times, and filtered by a bandpass filter with a passband frequency range of 500MHz-2.5GHz.

[0028] Wherein, in step S107, the kernel function types of the support vector machine algorithm include radial basis function, linear function, etc., and the appropriate kernel function and penalty factor are selected through multiple training and optimization to improve the accuracy of fault diagnosis.

[0029] In the positioning method based on time difference in step S108, for the ultrasonic signal and the high-frequency current signal, their propagation speeds in the cable are respectively and , the distance between the two sensors is d, and the signal arrival time difference is , then the distance between the fault point and one of the sensors is given by the formula It is calculated that for the combined positioning of UHF signals and other signals, a similar mathematical model is established according to the corresponding wave speed and time difference for calculation.

[0030] The cable partial discharge fault diagnosis model can classify and diagnose the partial discharge state of the cable, including normal state, slight discharge state, moderate discharge state and severe discharge state.

[0031] Example 1 Partial discharge detection was carried out on a 110kV power cable line in a certain city. The cable line is 5km long and uses cross-linked polyethylene insulated cables. First, according to the requirements of the detection system, a high-frequency current sensor was installed every 30 meters on the grounding wire of the cable, with a total of 167 sensors installed; an ultrasonic sensor and a UHF sensor were installed every 40 meters on the surface of the cable outer sheath, with 125 sensors installed respectively.

[0032] The data acquisition system was started to synchronously collect the signals collected by the three sensors for 24 hours, and a large amount of raw signal data was collected. The collected data was transmitted to the computer processing unit, and the developed detection and analysis software was used for signal preprocessing. The signal was denoised by wavelet transform algorithm, and the Daubechies4 wavelet basis function was selected. The number of decomposition layers was 4. After threshold processing and reconstruction, the denoised signal was obtained. Then the denoised signal was normalized so that its amplitude range was between [0,1].

[0033] Then, feature extraction was performed. Ten feature parameters such as pulse amplitude, pulse width, and pulse rise time were extracted from high-frequency current signals, eight feature parameters such as frequency component and energy distribution were extracted from ultrasonic signals, and 12 feature parameters such as spectrum characteristics and phase characteristics were extracted from UHF signals. The principal component analysis method was used to reduce the dimension of these feature parameters, and the first five principal components were selected as the basis for subsequent fault diagnosis.

[0034] The fault diagnosis model was established using the support vector machine algorithm. The data set was divided into a training set and a test set at a ratio of 70% and 30%. The SVM model was trained using the training set, and the radial basis function was selected as the kernel function with a penalty factor of 10. After multiple training and optimization, a fault diagnosis model with high accuracy was obtained. The model was used to diagnose the faults of the collected signals, and it was found that a slight partial discharge phenomenon existed in one section of the cable.

[0035] For the detected partial discharge fault, the positioning method based on time difference is used for fault positioning. According to the propagation speed of ultrasonic signals and high-frequency current signals in the cable (1500m / s and 2000m / s respectively) and the distance between the two sensors (40 meters), it is calculated that the distance between the fault point and one of the sensors is about 120 meters. After on-site excavation verification, the actual fault point location is basically consistent with the detection and positioning results, and the error is within an acceptable range.

[0036] Embodiment 2:

[0037] Partial discharge detection was carried out on a 35kV cable line in a factory. The cable line was 3km long and was a polyvinyl chloride insulated cable. Sensors were also installed and signals were collected according to the requirements of the detection system. The acquisition time was 12 hours. In the signal processing process, Daubechies6 wavelet basis function was selected for denoising, and the number of decomposition layers was 5. After feature extraction, principal component analysis was used to select the first 6 principal components, and a fault diagnosis model was established using a support vector machine (the kernel function was a linear function and the penalty factor was 8). After detection and diagnosis, no obvious partial discharge fault was found, and the cable was in normal operation, which was consistent with the actual operation of the cable, verifying the reliability and accuracy of the detection method of the present invention.

[0038] Through the verification of the above multiple embodiments, the cable partial discharge detection method of the present invention can effectively detect the partial discharge of the cable and accurately locate the fault point, providing an important technical guarantee for the safe and stable operation of the power system.

[0039] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for detecting partial discharge of a cable, characterized in that: The steps include: S101. Install multiple high-frequency current sensors on the ground wire of the cable, and install multiple ultrasonic sensors and ultra-high frequency sensors on the surface of the outer sheath of the cable; S102, synchronously collecting signals collected by the high-frequency current sensor, the ultrasonic sensor and the ultra-high frequency sensor using a data acquisition card; S103, respectively denoising the collected high-frequency current signal, ultrasonic signal and ultra-high frequency signal, using a wavelet transform algorithm, selecting the Daubechies wavelet basis function, decomposing the number of layers into 3-6 layers, removing the noise component through threshold processing and then reconstructing; S104, normalizing the denoised signal to adjust the amplitude range of the signal to between [0, 1]; S105, extracting pulse amplitude, pulse width, pulse rise time, and pulse repetition frequency characteristic parameters from the high-frequency current signal, extracting the frequency component, energy distribution, and arrival time characteristics of the signal from the ultrasonic signal, and extracting the spectrum characteristics, phase characteristics, and pulse number parameters of the signal from the ultra-high frequency signal; S106, using a principal component analysis method to perform dimensionality reduction processing on the extracted multiple feature parameters; S107, using a support vector machine algorithm to establish a cable partial discharge fault diagnosis model, dividing the data set after feature extraction and dimensionality reduction processing into a training set and a test set, training the support vector machine model through the training set, and adjusting the parameters of the model, including the kernel function type and the penalty factor; S108. For the detected partial discharge fault, a positioning method based on time difference is used to locate the fault. According to the different propagation speeds of high-frequency current signals, ultrasonic signals and ultra-high frequency signals in the cable, the arrival time difference of the signals collected by the sensor is used, combined with the length and wave speed parameters of the cable, and the position of the partial discharge fault point is calculated by establishing a mathematical model.

2. A method for detecting partial discharge of a cable according to claim 1, characterized in that: The high-frequency current sensor in step S101 is a Rogowski coil current sensor, which is a high-sensitivity, wide-band Rogowski coil high-frequency current sensor.

3. A method for detecting partial discharge of a cable according to claim 1, characterized in that: The ultrasonic sensor in step S101 is a piezoelectric ultrasonic sensor with a center frequency of 20kHz-200kHz, which is used to detect ultrasonic signals generated by partial discharge.

4. A method for detecting partial discharge of a cable according to claim 1, characterized in that: The UHF sensor in step S101 is a UHF sensor designed based on the principle of microstrip antenna, with an operating frequency range of 300 MHz-3 GHz, and is used to capture UHF electromagnetic wave signals generated by partial discharge.

5. A method for detecting partial discharge of a cable according to claim 1, characterized in that: In step S102, the signal collected by the high-frequency current sensor is amplified by a preamplifier with an amplification factor of 10-100 times, and filtered by a bandpass filter composed of a low-pass filter and a high-pass filter, and the passband frequency range of the bandpass filter is 50kHz-50MHz.

6. A method for detecting partial discharge of a cable according to claim 1, characterized in that: In step S102, for the signal of the ultrasonic sensor, the weak charge signal is first converted into a voltage signal by a charge amplifier and amplified by 50-200 times, and then a bandpass filter is used to remove low-frequency noise and high-frequency interference, and the passband frequency range of the bandpass filter is 30kHz-150kHz.

7. A method for detecting partial discharge of a cable according to claim 1, characterized in that: In step S102, the signal collected by the UHF sensor is amplified by a radio frequency amplifier with an amplification factor of 20-80 times, and filtered by a bandpass filter with a passband frequency range of 500MHz-2.5GHz.

8. A method for detecting partial discharge of a cable according to claim 1, characterized in that: In step S107, the kernel function types of the support vector machine algorithm include radial basis function and linear function. The appropriate kernel function and penalty factor are selected through multiple training and optimization to improve the accuracy of fault diagnosis.

9. A method for detecting partial discharge of a cable according to claim 1, characterized in that: In the positioning method based on time difference in step S108, for the ultrasonic signal and the high-frequency current signal, their propagation speeds in the cable are respectively and , the distance between the two sensors is d, and the signal arrival time difference is , then the distance between the fault point and one of the sensors is given by the formula It is calculated that for the combined positioning of UHF signals and other signals, a similar mathematical model is established according to the corresponding wave speed and time difference for calculation.

10. A method for detecting partial discharge of a cable according to claim 1, characterized in that: The cable partial discharge fault diagnosis model can classify and diagnose the partial discharge state of the cable, including a normal state, a slight discharge state, a moderate discharge state and a severe discharge state.

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