Partial discharge control method and device based on active noise reduction and wavelet denoising

Abstract

The invention provides a partial discharge control method based on active noise reduction and wavelet denoising. The method comprises the following steps: collecting an interface sound wave signal generated by breakdown of an internal cavity of grounding resistance complete equipment; collecting a field noise signal of the grounding resistance complete equipment, and carrying out amplitude and phase correction; carrying out discrete processing on the interface sound wave signal, and carrying out discrete processing on the field noise signal after amplitude and phase correction; carrying out subtraction on the discrete signal, and carrying out filtering processing to obtain a filtered sound wave signal; the filtered sound wave signals are collected according to the frequency range generated by partial discharge, and sound wave signals in the set frequency range are obtained; wavelet denoising processing is carried out on the sound wave signals in the set frequency range, the waveform of the denoised sound wave signals is fitted in the power frequency period, and partial discharge is judged. According to the invention, the sound wave opposite to the noise in phase is generated to counteract the noise, so that intelligent operation of the grounding resistor complete equipment is realized.

Description

Technical Field

[0001] The present application relates to the technical field of online monitoring of a complete set of grounding resistors, and in particular to a method and device for controlling partial discharge based on active noise reduction and wavelet denoising. Background Art

[0002] The grounding resistor set is a relatively important device in the power supply and distribution network of the power system. The neutral point is created by the grounding transformer, and a resistor is connected in series between the neutral point of the power grid and the ground. When a single-phase grounding fault occurs in the system, the fault current is limited to ensure the stable operation of the power system. Among them, the primary equipment involved, such as the grounding transformer, primary cable, etc., has insulation defects such as insulation aging and damage during long-term use, resulting in partial discharge, deterioration of the grounding resistor set, and affecting the stable operation of the power system.

[0003] The physical phenomena in the partial discharge process of power equipment are complex and diverse. According to the monitoring method, it can be divided into electrical signal detection and non-electrical signal detection. Ultrasonic detection is a kind of non-electrical signal detection. It collects the sound wave signal in the partial discharge process to determine whether the insulation is deteriorated. It has the advantages of no electrical contact between the detection equipment and the electrical equipment, strong anti-interference ability, convenient detection and easy monitoring.

[0004] However, the acoustic signal is weak during partial discharge, and there is a lot of acoustic interference and background noise at the site where the grounding resistance complete set of equipment is put into operation. The acoustic signal directly picked up by the ultrasonic sensor has a complex time spectrum and frequency spectrum, and the online monitoring equipment cannot directly and effectively capture the characteristic data for fault identification, which brings technical difficulties to the online monitoring of partial discharge ultrasonic waves. Summary of the invention

[0005] In view of the defects in the prior art, the purpose of this application is to provide a local discharge control method and device based on active noise reduction and wavelet denoising, which cancels out noise by generating sound waves with a phase opposite to that of the noise, and obtains a relatively pure local discharge ultrasonic signal through digital filtering and noise reduction processing, so as to identify faults in the fault sound wave signal and facilitate the intelligent operation of the grounding resistance complete set.

[0006] In one aspect of the present application, a partial discharge control method based on active noise reduction and wavelet denoising is provided, comprising:

[0007] Collect the interface acoustic wave signal generated by the breakdown of the internal cavity of the grounding resistance complete set;

[0008] Collect the on-site noise signal of the grounding resistance complete set and make amplitude and phase corrections;

[0009] The collected interface acoustic wave signal is discretely processed, and the amplitude and phase corrected on-site noise signal is discretely processed; the interface acoustic wave signal after discrete processing and the on-site noise signal are subtracted and filtered to obtain a filtered acoustic wave signal;

[0010] The filtered sound wave signal is collected according to the frequency range generated by the partial discharge to obtain a sound wave signal within a set frequency range;

[0011] The acoustic wave signal within the set frequency range is subjected to wavelet denoising, and the waveform of the denoised acoustic wave signal is fitted within the power frequency period to determine partial discharge.

[0012] Furthermore, the method collects the interface acoustic wave signal generated by the breakdown of the internal cavity of the grounding resistor complete set by the first ultrasonic sensor, calculates the phase change, collects the field noise signal of the grounding resistor complete set by the second ultrasonic sensor, and performs amplitude and phase correction, including:

[0013] Acquiring an interface sound wave signal collected by the first ultrasonic sensor and a field noise signal collected by the second ultrasonic sensor;

[0014] According to Snell's law, calculating the interface acoustic wave signal and phase change collected by the first ultrasonic sensor;

[0015] According to the spatial structure of the electrical equipment, the amplitude and phase of the on-site noise signal collected by the second ultrasonic sensor are corrected.

[0016] Furthermore, the formula for calculating the interface acoustic wave signal and phase change collected by the first ultrasonic sensor is:

[0017]

[0018] In the formula, c air is the speed of sound in air; c medm is the sound velocity of the insulating medium; θ 1 and θ 2 are the angle of incidence and the angle of refraction respectively.

[0019] Further, the collected interface sound wave signal is discretely processed, the collected field noise signal is amplitude and phase corrected and then discretely processed; the sound wave signal after discrete processing is subtracted and filtered to obtain a filtered sound wave signal, including:

[0020] Discretize the collected interface acoustic wave signal to obtain a first discrete value point;

[0021] Discretize the signal after amplitude and phase correction of the collected on-site noise signal to obtain a second discrete value point;

[0022] According to the first discrete numerical point and the second discrete numerical point, at the same time, the first discrete numerical point and the second discrete numerical point are subtracted point by point to obtain a differenced sound wave signal;

[0023] The acoustic wave signal after the difference is filtered through a bandpass filter to obtain a filtered acoustic wave signal.

[0024] Furthermore, the transfer function of the bandpass filter is a combination of a low pass and a high pass, which is a bandpass Butterworth filter H(s), and the formula is:

[0025]

[0026] Wherein, wc1 is the lower limit angular frequency of the passband edge; wc2 is the upper limit angular frequency of the passband edge; wn1 is the natural frequency; Q is the quality factor; S is the Laplace operator.

[0027] Furthermore, the performing wavelet denoising processing on the sound wave signal within the set frequency range includes:

[0028] Acquire a sound wave signal within a set frequency range, and perform FFT transformation on the sound wave signal within the set frequency range;

[0029] Performing wavelet transform on the signal after the FFT transformation and performing multi-scale decomposition to obtain multi-scale decomposed wavelet coefficients;

[0030] Performing denoising processing on the wavelet coefficients of the multi-scale decomposition to generate denoised wavelet coefficients;

[0031] The denoised wavelet coefficients are reconstructed using wavelet transformation to generate a denoised sound wave signal.

[0032] Furthermore, the signal after the FFT transformation is subjected to wavelet transformation and multi-scale decomposition to obtain multi-scale decomposed wavelet coefficients, including:

[0033] Acquiring a sound wave signal within the set frequency range, performing signal conversion, and forming a digital signal;

[0034] According to the data signal, a discrete wavelet transform is performed according to a set number of decomposition layers to obtain a multi-scale decomposition wavelet coefficient cj;

[0035] The set number of decomposition layers is 5;

[0036] The wavelet coefficients cj:

[0037] cj=∫f(t)ψ * (t-2 j T)dt;

[0038] Where: f(t) is a function with t as a variable; ψ is a wavelet function; j is the number of decomposition levels; t is a time variable; and T is a constant related to the time scale.

[0039] Furthermore, the denoising process is performed on the wavelet coefficients of the multi-scale decomposition to generate denoised wavelet coefficients, including:

[0040] Obtaining the wavelet coefficients of the multi-scale decomposition, and processing the wavelet coefficients using minimax threshold selection;

[0041] Performing an inverse discrete wavelet transform according to the wavelet coefficients processed by the threshold value to obtain a first reconstructed signal f′(t);

[0042] The first reconstructed signal f′(t)=∑cjψ(t-2jT);

[0043] Where j is the number of decomposition levels; ψ is the wavelet function; t is the time variable; T is a constant related to the time scale, and cj is the wavelet coefficient;

[0044] According to the first reconstructed signal f′(t), wavelet packet decomposition is performed to obtain the wavelet packet coefficient d of the set decomposition layer number. k ;

[0045] The wavelet packet coefficient d k =∫f(t)φ * (t-2 k T)dt;

[0046] In the formula, f(t) is a function with t as a variable, φ is a wavelet packet function; t is a time variable; T is a constant related to the time scale; k is the set number of decomposition levels;

[0047] The step of reconstructing the denoised wavelet coefficients by using wavelet transformation to generate a denoised sound wave signal includes:

[0048] According to the wavelet packet coefficients, wavelet packet reconstruction is performed using wavelet transformation to obtain a second reconstructed signal f′(t), thereby generating a denoised acoustic wave signal:

[0049] f'(t)=∑d k φ(t-2 k T)dt;

[0050] Where, d k is the wavelet packet coefficient; φ is the wavelet packet function; t is the time variable; T is a constant related to the time scale; k is the set number of decomposition levels.

[0051] Furthermore, the waveform of the de-noised acoustic wave signal is fitted within the power frequency period to determine the partial discharge, including:

[0052] Acquiring the denoised sound wave signal, sampling the waveform of the denoised sound wave signal, and fitting the waveform obtained by sampling;

[0053] Based on the exponential decay model of partial discharge of IEC60270, the fitted waveform is analyzed to extract characteristic parameters related to partial discharge;

[0054] The extracted characteristic parameters are compared with the thresholds specified in the IEC60270 standard to determine whether partial discharge exists.

[0055] The second aspect of the present application provides a partial discharge control device based on active noise reduction and wavelet denoising, comprising:

[0056] The first processing module is used to collect the interface acoustic wave signal generated by the breakdown of the internal cavity of the grounding resistance complete set;

[0057] The second processing module is used to collect the on-site noise signal of the grounding resistance complete set and perform amplitude and phase correction;

[0058] A calculation module is used to perform discrete processing on the collected interface acoustic wave signal and discrete processing on the on-site noise signal after amplitude and phase correction; perform subtraction on the discretely processed interface acoustic wave signal and the on-site noise signal, and perform filtering processing to obtain a filtered acoustic wave signal;

[0059] An acquisition module, used for acquiring the filtered sound wave signal according to the frequency range generated by the partial discharge, to obtain the sound wave signal within a set frequency range;

[0060] The judgment module is used to perform wavelet denoising on the sound wave signal within the set frequency range, and to fit the waveform of the denoised sound wave signal within the power frequency period to judge partial discharge.

[0061] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0062] 1. This application effectively removes the noise components in the sound wave signal by performing wavelet denoising on the denoised signal. During the processing, the wavelet transform can decompose the signal at multiple scales and complete the denoising of a large number of sound wave signals in a short time, thereby revealing the characteristics of the signal at different scales in more detail and improving the quality of the signal.

[0063] 2. By collecting and analyzing interface acoustic wave signals and field noise signals, the present application can effectively distinguish the acoustic waves generated by local discharge from the background noise, promptly discover the local discharge phenomenon inside the grounding resistor complete set, and effectively distinguish the interface acoustic wave signals and field noise signals generated by the breakdown of the internal cavity of the grounding resistor complete set. The digital filtering and noise reduction processing can obtain a relatively pure local discharge ultrasonic signal, so as to identify the fault acoustic wave signal, facilitate the intelligent operation of the grounding resistor complete set, realize the status monitoring and fault diagnosis of the grounding resistor complete set, and improve the accuracy of local discharge detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0065] Figure 1 This is a flow chart of a partial discharge control method based on active noise reduction and wavelet denoising in one embodiment of the present application.

[0066] Figure 2 This is a flow chart of wavelet denoising processing in one embodiment of the present application.

[0067] Figure 3 This is a structural diagram of a partial discharge control device based on active noise reduction and wavelet denoising according to an embodiment of the present application.

[0068] Figure 4 This is a discrete control flow chart in one embodiment of the present application.

[0069] Figure 5 This is a sound source waveform diagram collected in one embodiment of the present application.

[0070] Figure 6 This is a waveform diagram of a sound source after noise reduction in one embodiment of the present application.

[0071] Figure 7 This is a waveform diagram of a sound source after denoising in one embodiment of the present application. DETAILED DESCRIPTION

[0072] The present application is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements can also be made without departing from the concept of the present application. These all belong to the protection scope of the present application.

[0073] Reference Figure 1 As shown, a partial discharge control method based on active noise reduction and wavelet denoising in one embodiment of the present application includes: S1, collecting an interface acoustic wave signal generated by the breakdown of the internal cavity of the grounding resistor complete set;

[0074] S2. Collect the on-site noise signal of the grounding resistance complete set and make amplitude and phase corrections.

[0075] S3, discretely process the collected interface sound wave signal, and discretely process the on-site noise signal after amplitude and phase correction; make a difference between the discretely processed interface sound wave signal and the on-site noise signal, and perform filtering to obtain a filtered sound wave signal.

[0076] S4. Collect the filtered sound wave signal according to the frequency range generated by the partial discharge to obtain the sound wave signal within the set frequency range.

[0077] S5. Perform wavelet denoising on the sound wave signal within the set frequency range, and fit the waveform of the denoised sound wave signal within the power frequency period to determine partial discharge.

[0078] By collecting and analyzing interface acoustic wave signals and field noise signals, the present application can effectively distinguish the acoustic waves generated by partial discharge from the background noise, timely discover the partial discharge phenomenon inside the grounding resistor complete set, and effectively distinguish the interface acoustic wave signals and field noise signals generated by the breakdown of the internal cavity of the grounding resistor complete set. The digital filtering and noise reduction processing obtains a relatively pure partial discharge ultrasonic signal, so as to identify the fault acoustic wave signal, facilitate the intelligent operation of the grounding resistor complete set, realize the status monitoring and fault diagnosis of the grounding resistor complete set, and improve the accuracy of partial discharge detection.

[0079] Specifically, firstly, the interface acoustic wave signal generated by the breakdown of the internal cavity of the grounding resistor complete set is collected by using a first ultrasonic sensor, and the noise signal at the scene is collected by using a second ultrasonic sensor, and the amplitude and phase of the interface acoustic wave signal and the noise signal are corrected to reduce interference and error; then, the interface acoustic wave signal collected by the first ultrasonic sensor is discretized, and at the same time, the noise signal collected by the second ultrasonic sensor and pre-processed is also discretized; then, the two discretely processed acoustic wave signals are subtracted to eliminate the influence of background noise, and the subtracted signal is filtered to obtain a filtered acoustic wave signal; then, according to the frequency range generated by partial discharge, the filtered acoustic wave signal is collected to obtain an acoustic wave signal within a set frequency range, and the acoustic wave signal within the set frequency range is subjected to wavelet denoising to further reduce noise interference; finally, the denoised acoustic wave waveform is fitted within the power frequency cycle, and according to the characteristics of the fitted acoustic wave waveform, it is judged whether there is a partial discharge phenomenon, so as to realize the intelligent operation of the grounding resistor complete set.

[0080] The interface acoustic wave signal is calculated to change the phase in order to obtain the specific phase characteristics of the acoustic wave signal when the cavity inside the grounding resistor complete set breaks down, so as to identify the partial discharge phenomenon. The purpose of the amplitude and phase correction of the on-site noise signal is to eliminate or weaken the amplitude and phase differences that may exist between the noise signal and the interface acoustic wave signal, to ensure that the two can accurately reflect the characteristics of the actual partial discharge signal when performing the difference processing in the subsequent process, and to improve the accuracy and reliability of partial discharge detection.

[0081] In some possible embodiments, an interface acoustic wave signal generated by the breakdown of the internal cavity of the grounding resistor complete set is collected, the phase change is calculated, the field noise signal of the grounding resistor complete set is collected, and amplitude and phase corrections are performed, including: obtaining the interface acoustic wave signal collected by the first ultrasonic sensor and the field noise signal collected by the second ultrasonic sensor; calculating the interface acoustic wave signal and phase change collected by the first ultrasonic sensor according to Snell's law; and performing amplitude and phase corrections on the field noise signal collected by the second ultrasonic sensor according to the spatial structure of the electrical equipment.

[0082] In the amplitude and phase correction, the time scales of the two sensors' acquisition times are first aligned, and then the signals in the set frequency band are conditioned according to the fixed value setting, and the amplitudes and phases of the first and second sensors are filtered, de-jittered, and interpolated to obtain the corrected data.

[0083] Among them, the signal collected by the first ultrasonic sensor is a specific sound wave signal generated when the internal cavity of the grounding resistor complete set is broken down, such as the location and intensity of the discharge; the phase change of the interface sound wave signal is calculated, and for the subsequent signal synchronization and noise reduction processing, the phase information of the discharge signal is used to accurately adjust the phase of the noise reduction signal so that it is aligned with the original noise signal in phase, thereby achieving more effective noise reduction. The second ultrasonic sensor collects the noise signal at the device site, including environmental noise, device operation noise, etc. Since the on-site noise signal may be affected by many factors (such as propagation path, reflection, attenuation, etc.), the amplitude and phase are corrected according to the spatial structure of the electrical equipment to eliminate the influence of on-site noise on discharge signal detection.

[0084] like Figure 5 As shown, it is the sound source collected on site. In this application, the location where the partial discharge occurs can be regarded as the sound source. As a mechanical wave, the sound wave has different forms in the partial discharge process. First, the sound wave is generated by the breakdown of the cavity inside the grounding resistor complete set. There is an interface between air and insulating material, and refraction and reflection will occur. The interface wave leaves the interface during propagation and enters a single medium. It can propagate again in the form of a spherical wave. The propagation of sound waves in gas and liquid is in the form of spherical waves that radiate and propagate around, and the energy of the sound waves will also be reduced. Based on this principle, active noise reduction technology is adopted.

[0085] In the above principle, the reflection coefficient R is:

[0086] Refractive index Z:

[0087] In the formula, ρ air 、c air , medm 、c medm are the density and speed of sound of air and insulating medium respectively.

[0088] By calculating the reflection coefficient and refraction coefficient, it is used for noise reduction.

[0089] According to Snell's law, the formula for the interface sound wave and phase change collected by the first ultrasonic sensor is:

[0090]

[0091] In the formula, c air is the speed of sound in air; c medm is the sound velocity of the insulating medium; θ 1 and θ 2 are the angle of incidence and the angle of refraction respectively.

[0092] In some possible embodiments, the collected interface sound wave signal is discretely processed, and the collected field noise signal is discretely processed after amplitude and phase correction; the sound wave signal after discrete processing is subtracted and filtered to obtain a filtered sound wave signal, including:

[0093] The collected interface sound wave signal is discretized to obtain the first discrete numerical point; the collected field noise signal is discretized after amplitude and phase correction to obtain the second discrete numerical point.

[0094] According to the first discrete numerical point and the second discrete numerical point, at the same time, the first discrete numerical point and the second discrete numerical point are subtracted point by point to obtain a differenced sound wave signal; according to the differenced sound wave signal, a bandpass filter is used to filter the differenced sound wave signal to obtain a filtered sound wave signal.

[0095] like Figure 6As shown, the collected sound source waveform is subjected to noise reduction processing to reduce the influence of excess noise. Specifically, the interface sound wave signal is directly collected by the first ultrasonic sensor. After the second ultrasonic sensor collects the sound wave signal, the continuous sound wave signal collected by the first ultrasonic sensor is discretized, and the continuous signal is converted into a first discrete numerical point. The signal after amplitude and phase correction of the field noise signal collected by the second ultrasonic sensor is subjected to the same discretization processing to obtain a second discrete numerical point. At the same time, on the basis of time synchronization, the first discrete numerical point and the second discrete numerical point are subtracted point by point, and for each time point, the difference between the first discrete numerical point and the second discrete numerical point is calculated; then, the sound wave signal after the difference is filtered through a bandpass filter to achieve the purpose of active noise reduction.

[0096] Reference Figure 4 As shown, P(z) is the discrete processing of the first ultrasonic sensor, and A(z) is the discrete processing of the second ultrasonic sensor after amplitude and phase correction.

[0097] By taking x(n) as the sound source collected on-site, the sound is discretely processed by the first ultrasonic sensor. At the same time, the signal after amplitude and phase correction is discretely processed by the second ultrasonic sensor, and the signal is subtracted, the component corresponding to the on-site noise is removed from the interface sound wave signal, and a compensation signal with the opposite phase and equal amplitude to the on-site noise is generated, which can offset the on-site noise in real time and achieve the effect of active noise reduction. Then, the signal is filtered by BUTTER to obtain the filtered signal d(n).

[0098] In the above embodiment, specifically, the transfer function of the bandpass filter is a combination of a low-pass filter and a high-pass filter, which is a bandpass Butterworth filter H(s), and the formula is:

[0099]

[0100] Wherein, wc1 is the lower limit angular frequency of the passband edge; wc2 is the upper limit angular frequency of the passband edge; wn1 is the natural frequency; Q is the quality factor; s is the Laplace operator.

[0101] Among them, according to the frequency range of 50kHz to 400kHz generated by partial discharge, the sound wave signal d(x) (ie d(n)) in the frequency range is collected after noise reduction processing (ie the signal filtered by a bandpass Butterworth filter).

[0102] Reference Figure 2As shown, in some possible embodiments, wavelet denoising is performed on a sound wave signal within a set frequency range, including: acquiring a sound wave signal within the set frequency range, and performing FFT transformation on the sound wave signal within the set frequency range; performing wavelet transform on the signal after the FFT transformation, and performing multi-scale decomposition to obtain multi-scale decomposed wavelet coefficients; performing denoising on the multi-scale decomposed wavelet coefficients to generate denoised wavelet coefficients; and reconstructing the denoised wavelet coefficients using wavelet transformation to generate a denoised sound wave signal.

[0103] The present application effectively removes the noise components in the sound wave signal by performing wavelet denoising on the denoised signal. During the processing, the wavelet transform can decompose the signal at multiple scales and complete the denoising of a large number of sound wave signals in a short time, thereby revealing the characteristics of the signal at different scales in more detail and improving the quality of the signal.

[0104] Specifically, first, a bandpass filter is used to filter the collected sound wave signal to remove noise and interference signals that are not outside the set frequency range, and the sound wave signal within the set frequency range is preprocessed (FFT change) to facilitate subsequent wavelet transform; using wavelet transform technology, the preprocessed sound wave signal is converted from the time domain to the wavelet domain, and multi-scale decomposition is performed to decompose the signal into wavelet coefficients at different scales (or frequencies); then, according to the wavelet coefficients obtained by multi-scale decomposition, and an appropriate denoising strategy, such as threshold processing, is used to denoise the coefficients to remove noise components while retaining the main features of the signal. After denoising, the denoised wavelet coefficients are obtained; finally, using the wavelet inverse transform technology, the denoised wavelet coefficients are converted back from the wavelet domain to the time domain, and the denoised sound wave signal is reconstructed.

[0105] Specifically, the sound wave signal within the set frequency range is subjected to FFT transformation; the signal after FFT transformation is subjected to wavelet transformation and multi-scale decomposition to obtain the wavelet coefficients of the multi-scale decomposition, including: obtaining the sound wave signal within the set frequency range, performing signal conversion to form a data signal; according to the data signal, a discrete wavelet transform is performed according to a set number of decomposition layers to obtain the wavelet coefficients cj of the multi-scale decomposition.

[0106] The sound wave signal d(x) processed by active noise cancellation is read to form a data sequence, and the signal is transformed by FFT; then, multi-scale decomposition is performed through wavelet transform, the number of decomposition layers of wavelet transform is determined, and discrete wavelet transform is performed on f(t)f(t), where f(t)f(t) represents a function with t as a variable, and a series of wavelet coefficients cj are obtained. The wavelet coefficients represent the components of the signal at different scales (or frequencies).

[0107] Among them, the number of decomposition layers is set to 5.

[0108] Wavelet coefficient cj: cj = ∫f(t)ψ * (t-2 j T)dt.

[0109] Where: f(t) is a function with t as a variable; ψ is a wavelet function; j is the number of decomposition levels; t is a time variable; and T is a constant related to the time scale.

[0110] First, through precise digital signal conversion, the sound wave signal can be efficiently converted into a processable data sequence. Then, the Fourier transform is used to identify the key frequency components in the signal, and the signal is discretely transformed through a set number of decomposition layers to decompose the signal into multi-scale wavelet coefficients, thereby improving the accuracy and efficiency of sound wave signal processing.

[0111] Specifically, according to the wavelet coefficients of multi-scale decomposition, denoising is performed on the wavelet coefficients of multi-scale decomposition to generate denoised wavelet coefficients; according to the denoised wavelet coefficients, the denoised wavelet coefficients are reconstructed using wavelet transformation to generate denoised sound wave signals, including: obtaining the wavelet coefficients of multi-scale decomposition, and processing the wavelet coefficients using minimaxi threshold selection; according to the wavelet coefficients processed by threshold, inverse discrete wavelet transform is performed to obtain a first reconstructed signal f′(t), the first reconstructed signal f′(t)=∑cjψ(t-2jT).

[0112] Where j is the number of decomposition levels; ψ is the wavelet function; t is the time variable; T is a constant related to the time scale, and cj is the wavelet coefficient.

[0113] According to the first reconstructed signal f′(t), wavelet packet decomposition is performed to obtain the wavelet packet coefficient d of the set decomposition layer k ; Wavelet packet coefficient d k =∫f(t)φ * (t-2 k T)dt.

[0114] Where f(t) is a function with t as a variable, φ is a wavelet packet function, t is a time variable, T is a constant related to the time scale, and k is the set number of decomposition levels.

[0115] According to the wavelet packet coefficients, wavelet packet reconstruction is performed using wavelet transformation to obtain the second reconstructed signal f′(t), generating the denoised sound wave signal: f′(t)=∑d k φ(t-2 k T)dt.

[0116] Where, d k is the wavelet packet coefficient; φ is the wavelet packet function; t is the time variable; T is a constant related to the time scale; k is the set number of decomposition levels.

[0117] By using the minimaxi threshold selection strategy to finely process the wavelet coefficients, the noise components can be intelligently identified and suppressed, while the effective information in the signal is retained to the maximum extent, generating the denoised wavelet coefficients; then, using the wavelet transform, the thresholded wavelet coefficients are subjected to an inverse discrete wavelet transform to reconstruct the denoised signal f′(t) and restore the original form of the signal, such as Figure 7 shown.

[0118] In some specific embodiments, the waveform of the denoised sound wave signal is fitted within the power frequency cycle to determine partial discharge, including: obtaining the denoised sound wave signal, sampling the waveform of the denoised sound wave signal, and fitting the sampled waveform; based on the exponential decay model of partial discharge of IEC60270, the fitted waveform is analyzed to extract characteristic parameters related to partial discharge; the extracted characteristic parameters are compared with the threshold value specified in the IEC60270 standard to determine whether partial discharge exists.

[0119] The second aspect of the present application provides a local discharge control device based on active noise reduction and wavelet denoising, comprising: a first processing module for collecting interface acoustic wave signals generated by the breakdown of the internal cavity of the grounding resistor complete set; a second processing module for collecting the field noise signal of the grounding resistor complete set, and performing amplitude and phase correction; a calculation module for discretely processing the collected interface acoustic wave signals, and discretely processing the field noise signals after amplitude and phase correction; subtracting the discretely processed interface acoustic wave signals and the field noise signals, and filtering them to obtain filtered acoustic wave signals; an acquisition module for collecting the filtered acoustic wave signals according to the frequency range generated by the local discharge, and obtaining acoustic wave signals within a set frequency range; a judgment module for performing wavelet denoising on the acoustic wave signals within the set frequency range, and fitting the waveform of the denoised acoustic wave signals within the power frequency period to judge the local discharge.

[0120] like Figure 3As shown, active noise reduction performs signal filtering on the disturbance open loop that compensates for the environmental noise. The hardware system mainly includes using a first ultrasonic sensor to collect signals, using a second ultrasonic sensor to collect the disturbance quantity (environmental noise), and local discharge generates a sound source. The first ultrasonic sensor is placed on the outer wall of the grounding resistor complete device structure and is tightly combined with the outer wall. The local discharge sound signal can be more accurately collected by conduction. The second ultrasonic sensor is placed outside the grounding resistor complete device to collect environmental noise. The ultrasonic signal is converted into an electrical signal according to the first acoustic-to-electric conversion module and the second acoustic-to-electric conversion module; then, the signal conditioning circuit performs hardware filtering and operational amplification. The on-site noise is complex and diverse, and then it passes through the AD conversion module and is sent to the processor for signal processing.

[0121] The above describes the specific embodiments of the present application. It should be understood that the present application is not limited to the above specific embodiments, and those skilled in the art can make various modifications or variations within the scope of the claims, which does not affect the substantive content of the present application. The above preferred features can be used in any combination without conflicting with each other.

Claims

1. A partial discharge control method based on active noise reduction and wavelet denoising, characterized in that: include: Collect the interface acoustic wave signal generated by the breakdown of the internal cavity of the grounding resistance complete set; Collect the on-site noise signal of the grounding resistance complete set and make amplitude and phase corrections; The collected interface acoustic wave signal is discretely processed, the amplitude and phase corrected on-site noise signal is discretely processed, the interface acoustic wave signal and the on-site noise signal after discrete processing are subtracted, and filtered to obtain a filtered acoustic wave signal; The filtered sound wave signal is collected according to the frequency range generated by the partial discharge to obtain a sound wave signal within a set frequency range; The acoustic wave signal within the set frequency range is subjected to wavelet denoising, and the waveform of the denoised acoustic wave signal is fitted within the power frequency period to determine partial discharge.

2. The partial discharge control method based on active noise reduction and wavelet denoising according to claim 1 is characterized in that: The method of collecting the interface acoustic wave signal generated by the breakdown of the internal cavity of the grounding resistor complete set and collecting the on-site noise signal of the grounding resistor complete set to perform amplitude and phase correction includes: Acquire the interface sound wave signal collected by the first ultrasonic sensor and the field noise signal collected by the second ultrasonic sensor; According to Snell's law, calculating the interface acoustic wave signal and phase change collected by the first ultrasonic sensor; According to the spatial structure of the electrical equipment, the amplitude and phase of the on-site noise signal collected by the second ultrasonic sensor are corrected.

3. The partial discharge control method based on active noise reduction and wavelet denoising according to claim 2 is characterized in that: The formula for calculating the interface acoustic wave signal and phase change collected by the first ultrasonic sensor is: In the formula, c air is the speed of sound in air; c medm is the sound velocity of the insulating medium; θ1 and θ2 are the angle of incidence and the angle of refraction, respectively.

4. The partial discharge control method based on active noise reduction and wavelet denoising according to claim 1 is characterized in that: The collected interface acoustic wave signal is discretely processed, the amplitude and phase corrected on-site noise signal is discretely processed, the interface acoustic wave signal and the on-site noise signal after discrete processing are subtracted, and filtering is performed to obtain a filtered acoustic wave signal, including: Discretize the collected interface acoustic wave signal to obtain a first discrete value point; Discretize the on-site noise signal after the amplitude and phase correction to obtain a second discrete value point; According to the first discrete numerical point and the second discrete numerical point, at the same time, the first discrete numerical point and the second discrete numerical point are subtracted point by point to obtain a differenced sound wave signal; The acoustic wave signal after the difference is filtered through a bandpass filter to obtain a filtered acoustic wave signal.

5. The partial discharge control method based on active noise reduction and wavelet denoising according to claim 4 is characterized in that: The transfer function of the bandpass filter is a combination of a low-pass filter and a high-pass filter, which is a bandpass Butterworth filter H(s), and the formula is: Wherein, wc1 is the lower limit angular frequency of the passband edge; wc2 is the upper limit angular frequency of the passband edge; wn1 is the natural frequency; Q is the quality factor; s is the Laplace operator.

6. The partial discharge control method based on active noise reduction and wavelet denoising according to claim 1 is characterized in that: The performing wavelet denoising processing on the sound wave signal within the set frequency range comprises: Acquire a sound wave signal within a set frequency range, and perform FFT transformation on the sound wave signal within the set frequency range; Performing wavelet transform on the signal after the FFT transformation and performing multi-scale decomposition to obtain multi-scale decomposed wavelet coefficients; Performing denoising processing on the wavelet coefficients of the multi-scale decomposition to generate denoised wavelet coefficients; The denoised wavelet coefficients are reconstructed using wavelet transformation to generate a denoised sound wave signal.

7. The partial discharge control method based on active noise reduction and wavelet denoising according to claim 6 is characterized in that: The signal after the FFT transformation is subjected to wavelet transformation and multi-scale decomposition to obtain multi-scale decomposed wavelet coefficients, including: Acquiring a sound wave signal within the set frequency range, performing signal conversion, and forming a digital signal; According to the data signal, a discrete wavelet transform is performed according to a set number of decomposition layers to obtain a multi-scale decomposition wavelet coefficient cj; The set number of decomposition layers is 5; The wavelet coefficients cj: cj=∫f(t)ψ * (t-2jT)dt; Where: f(t) is a function with t as a variable; ψ is a wavelet function; j is the number of decomposition levels; t is a time variable; and T is a constant related to the time scale.

8. The partial discharge control method based on active noise reduction and wavelet denoising according to claim 6 is characterized in that: The step of performing denoising on the multi-scale decomposed wavelet coefficients to generate denoised wavelet coefficients includes: Obtaining the wavelet coefficients of the multi-scale decomposition, and processing the wavelet coefficients using minimax threshold selection; Performing an inverse discrete wavelet transform according to the wavelet coefficients processed by the threshold value to obtain a first reconstructed signal f′(t); The first reconstructed signal f′(t)=∑cjψ(t-2jT); Where j is the number of decomposition levels; ψ is the wavelet function; t is the time variable; T is a constant related to the time scale, and cj is the wavelet coefficient; According to the first reconstructed signal f′(t), wavelet packet decomposition is performed to obtain the wavelet packet coefficient d of the set decomposition layer number. k ; The wavelet packet coefficient d k =∫f(t)φ * (t-2 k T)dt; In the formula, f(t) is a function with t as a variable, φ is a wavelet packet function; t is a time variable; T is a constant related to the time scale; k is the set number of decomposition levels; The step of reconstructing the denoised wavelet coefficients by using wavelet transformation to generate a denoised sound wave signal includes: According to the wavelet packet coefficients, wavelet packet reconstruction is performed using wavelet transformation to obtain a second reconstructed signal f′(t), thereby generating a denoised acoustic wave signal: f’(t)=∑d k φ(t-2 k T)dt; Where, d k is the wavelet packet coefficient; φ is the wavelet packet function; t is the time variable; T is a constant related to the time scale; k is the set number of decomposition levels.

9. The partial discharge control method based on active noise reduction and wavelet denoising according to claim 6, characterized in that: The waveform of the de-noised acoustic wave signal is fitted within the power frequency period to determine the partial discharge, including: Acquiring the denoised sound wave signal, sampling the waveform of the denoised sound wave signal, and fitting the waveform obtained by sampling; Based on the exponential decay model of partial discharge of IEC60270, the fitted waveform is analyzed to extract characteristic parameters related to partial discharge; The extracted characteristic parameters are compared with the thresholds specified in the IEC60270 standard to determine whether partial discharge exists.

10. A partial discharge control device based on active noise reduction and wavelet denoising, characterized in that: include: The first processing module is used to collect the interface acoustic wave signal generated by the breakdown of the internal cavity of the grounding resistance complete set; The second processing module is used to collect the on-site noise signal of the grounding resistance complete set and perform amplitude and phase correction; A calculation module is used to perform discrete processing on the collected interface acoustic wave signal and discrete processing on the on-site noise signal after amplitude and phase correction; perform subtraction on the discretely processed interface acoustic wave signal and the on-site noise signal, and perform filtering processing to obtain a filtered acoustic wave signal; An acquisition module, used for acquiring the filtered sound wave signal according to the frequency range generated by the partial discharge, to obtain the sound wave signal within a set frequency range; The judgment module is used to perform wavelet denoising on the sound wave signal within the set frequency range, and to fit the waveform of the denoised sound wave signal within the power frequency period to judge partial discharge.

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