A denoising method, device and medium for partial discharge detection

Abstract

The embodiment of the application discloses a denoising method and device for partial discharge detection and a medium, and belongs to the technical field of partial discharge detection. The embodiment solves the problem of the existing partial discharge detection denoising method that destroys the integrity and accuracy of the partial discharge signal. A time sequence energy instantaneous change rate curve is drawn; the time sequence energy instantaneous change rate curve is subjected to window processing; the time sequence energy instantaneous change rate corresponding to the data points in the window is compared with a preset filtering threshold; based on the comparison result, the high-frequency current signal to be processed is subjected to primary denoising; in the time sequence energy instantaneous change rate curve after the primary denoising, data points with a time sequence energy instantaneous change rate greater than zero are determined, so as to extract a pulse waveform from the high-frequency current signal to be processed and construct a pulse array; the corresponding frequency spectrum bandwidth of the pulse array at a preset amplitude proportion threshold is determined; based on the frequency spectrum bandwidth, the high-frequency current signal to be processed is subjected to secondary denoising, so as to obtain a partial discharge signal.

Description

Technical Field

[0001] This application relates to the field of partial discharge detection technology, and in particular to a noise reduction method, device and medium for partial discharge detection. Background Technology

[0002] During operation, converter transformers are subjected to complex stresses from electrical, thermal, and mechanical sources. Insulation defects such as bubbles, cracks, and burrs can easily develop, leading to partial discharge. Partial discharge accelerates the deterioration of insulation materials, potentially causing insulation failures and serious safety accidents. Detecting partial discharge helps to promptly identify potential defects within the converter transformer, preventing insulation failures and effectively ensuring equipment safety and the stable operation of the power grid.

[0003] High-frequency current methods are widely used for partial discharge detection in converter stations, acquiring high-frequency current signals through high-frequency current transformers. However, severe electromagnetic interference at converter stations can overwhelm the acquired high-frequency partial discharge current signals, significantly affecting the accuracy of partial discharge detection. Therefore, to accurately detect partial discharges, it is essential to first eliminate the influence of noise interference.

[0004] Noise interference at converter stations can be mainly categorized into three types: white noise, periodic narrowband interference, and random impulse interference. White noise generally originates from the environment or the equipment itself and is typically considered to follow a Gaussian distribution. Periodic narrowband interference is mainly generated by carrier communication, higher harmonics, and radio communication, and it has a narrowband spectrum centered on the dominant frequency. Random impulse interference is generally a wideband signal caused by the operation of thyristors and other switching equipment. Once the frequencies of these noise interferences overlap with the frequencies of partial discharge signals, it becomes difficult to effectively remove the interference. While frequency domain filtering can remove low-frequency noise interference, it also filters out the low-frequency components of the partial discharge signal, leading to the omission of discharge information, attenuation of the partial discharge signal, and damage to the integrity and accuracy of the partial discharge signal. Summary of the Invention

[0005] This application provides a denoising method, device, and medium for partial discharge detection, which addresses the following technical problem: existing partial discharge detection denoising methods are prone to missing discharge information, causing partial discharge signals to attenuate and compromising the integrity and accuracy of the partial discharge signals.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] This application provides a denoising method for partial discharge detection. The method includes: determining the instantaneous rate of change of time-series energy of the high-frequency current signal to be processed and plotting the instantaneous rate of change curve; windowing the instantaneous rate of change curve, comparing the instantaneous rate of change of time-series energy corresponding to data points within the window with a preset filtering threshold, and performing a first denoising operation on the high-frequency current signal to be processed based on the comparison result; identifying data points with a time-series energy instantaneous rate of change greater than zero in the denoised instantaneous rate of change curve, extracting pulse waveforms from the high-frequency current signal to be processed based on these data points, and constructing a pulse array; determining the spectral bandwidth corresponding to each pulse waveform in the pulse array at a preset amplitude percentage threshold, and performing a second denoising operation on the high-frequency current signal to be processed based on the spectral bandwidth to obtain the partial discharge signal.

[0008] This application uses the instantaneous rate of change of the signal's temporal energy as the identification basis, effectively distinguishing white noise, periodic narrowband interference, and pulse waveforms, thereby adaptively extracting the pulse waveform. Secondly, based on the spectral differences between the partial discharge signal and the interference pulse, this application proposes using the spectral bandwidth corresponding to the percentage of each pulse waveform in the pulse array at a preset amplitude threshold as an indicator to distinguish between the partial discharge signal and the interference signal. This enables adaptive identification of the partial discharge signal with higher accuracy. This application can denoise various types of noise interference present in high-frequency current method detection without attenuating the partial discharge signal, and requires minimal computation, meeting the needs of rapid partial discharge detection in converter stations.

[0009] In one implementation of this application, determining the instantaneous change rate of the time-series energy of the high-frequency current signal to be processed specifically includes: determining the time-series energy accumulation rate corresponding to each data point in the high-frequency signal to be processed based on a preset time-series energy accumulation rate function; and obtaining the instantaneous change rate of the time-series energy based on the difference between the time-series energy accumulation rate corresponding to the (k+1)th data point and the time-series energy accumulation rate corresponding to the kth data point.

[0010] In one implementation of this application, the instantaneous rate of change of time-series energy corresponding to data points within a window is compared with a preset filtering threshold. Based on the comparison result, a denoising step is performed on the high-frequency current signal to be processed. Specifically, this includes: determining the number of data points within the window whose instantaneous rate of change of time-series energy is greater than the preset filtering threshold; if the number of data points is greater than the data volume ratio threshold corresponding to the window, determining that a pulse waveform exists in the window and retaining the curve corresponding to the window; otherwise, setting the amplitude of the curve corresponding to the window to zero to perform a denoising step on the high-frequency current signal to be processed; wherein, the denoising step includes at least removing white noise and periodic narrowband interference from the high-frequency current signal to be processed.

[0011] In one implementation of this application, before comparing the instantaneous change rate of temporal energy corresponding to the data points within the window with a preset filtering threshold, the method further includes: sorting the instantaneous change rates of temporal energy corresponding to all data points within the window from smallest to largest; determining the instantaneous change rate of temporal energy corresponding to the data points at a preset percentage position among the sorted data points, and using the instantaneous change rate of temporal energy corresponding to the data points at the preset percentage position as the preset filtering threshold.

[0012] In one implementation of this application, data points with a time-series energy instantaneous change rate greater than zero are identified in the time-series energy instantaneous change rate curve after one denoising step. Based on these data points, pulse waveforms are extracted from the high-frequency current signal to be processed, and a pulse array is constructed. Specifically, this includes: identifying data points with a time-series energy instantaneous change rate greater than zero; determining a first time period corresponding to the data points with a time-series energy instantaneous change rate of zero based on a preset judgment function, and setting the high-frequency current signal to be processed corresponding to the first time period to zero; and determining a second time period corresponding to the data points with a time-series energy instantaneous change rate greater than zero based on a preset judgment function, and retaining the high-frequency current signal to be processed corresponding to the second time period to obtain the pulse array.

[0013] In one implementation of this application, the preset judgment function is:

[0014]

[0015] Where P(i) represents the amplitude corresponding to the i-th data point of the pulse array, x(i) represents the amplitude corresponding to the i-th data point of the high-frequency current signal to be processed, and ΔE(i) represents the instantaneous rate of change of the time-series energy corresponding to the i-th data point.

[0016] In one implementation of this application, the spectral bandwidth corresponding to each pulse waveform in the pulse array at a preset amplitude percentage threshold is determined. Based on this spectral bandwidth, a secondary denoising process is performed on the high-frequency current signal to be processed to obtain a partial discharge signal. Specifically, this includes: determining the spectrum corresponding to each pulse waveform in the pulse array and determining the maximum spectral amplitude; determining the reference spectral amplitude corresponding to the preset amplitude percentage threshold of the maximum spectral amplitude; and determining the first frequency and the second frequency corresponding to the reference spectral amplitude; and performing a secondary denoising process on the high-frequency current signal to be processed based on the spectral bandwidth between the first frequency and the second frequency to obtain the partial discharge signal.

[0017] In one implementation of this application, a secondary denoising process is performed on the high-frequency current signal to be processed based on the spectral bandwidth between the first frequency and the second frequency to obtain a partial discharge signal. Specifically, this includes: if the spectral bandwidth between the first frequency and the second frequency is greater than a preset spectral bandwidth threshold, determining that the pulse waveform is a partial discharge signal and retaining it; if the spectral bandwidth between the first frequency and the second frequency is not greater than the preset spectral bandwidth threshold, determining that the pulse waveform is an interference pulse and setting the amplitude of the interference pulse to zero.

[0018] This application provides a denoising device for partial discharge detection, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: determine the instantaneous rate of change of the time-series energy of a high-frequency current signal to be processed, and plot a curve of the instantaneous rate of change of time-series energy; perform windowing processing on the curve of the instantaneous rate of change of time-series energy, compare the instantaneous rate of change of time-series energy corresponding to data points within the window with a preset filtering threshold, and perform a first denoising operation on the high-frequency current signal to be processed based on the comparison result; determine data points with a time-series energy instantaneous rate of change greater than zero in the curve of the instantaneous rate of change of time-series energy, and extract pulse waveforms from the high-frequency current signal to be processed based on the data points with a time-series energy instantaneous rate of change greater than zero, and construct a pulse array; determine the spectral bandwidth corresponding to each pulse waveform in the pulse array at a preset amplitude percentage threshold, and perform a second denoising operation on the high-frequency current signal to be processed based on the spectral bandwidth to obtain a partial discharge signal.

[0019] This application provides a non-volatile computer storage medium storing computer-executable instructions. The computer-executable instructions are configured to: determine the instantaneous rate of change of the time-series energy of the high-frequency current signal to be processed, and plot a curve of the instantaneous rate of change of time-series energy; perform windowing processing on the instantaneous rate of change of time-series energy curve, compare the instantaneous rate of change of time-series energy corresponding to data points within the window with a preset filtering threshold, and perform a first denoising operation on the high-frequency current signal to be processed based on the comparison result; determine data points with a time-series energy instantaneous rate of change greater than zero in the denoised instantaneous rate of change of time-series energy curve, extract pulse waveforms from the high-frequency current signal to be processed based on these data points, and construct a pulse array; determine the spectral bandwidth corresponding to each pulse waveform in the pulse array at a preset amplitude percentage threshold, and perform a second denoising operation on the high-frequency current signal to be processed based on the spectral bandwidth to obtain a partial discharge signal.

[0020] The at least one technical solution adopted in this application embodiment can achieve the following beneficial effects: This application embodiment uses the instantaneous change rate of the signal's temporal energy as the identification basis, which can effectively distinguish white noise, periodic narrowband interference, and pulse waveforms, thereby adaptively extracting the pulse waveform. Secondly, based on the spectral difference between the partial discharge signal and the interference pulse, this application embodiment proposes using the spectral bandwidth corresponding to the preset amplitude ratio threshold of each pulse waveform in the pulse array as an indicator to distinguish between the partial discharge signal and the interference signal, enabling adaptive identification of the partial discharge signal with higher accuracy. This application embodiment can denoise different types of noise interference present in high-frequency current method detection without attenuating the partial discharge signal, and has a small computational load, meeting the needs of rapid partial discharge detection in converter stations. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0022] Figure 1 This is a flowchart of a noise reduction method for partial discharge detection provided in an embodiment of this application;

[0023] Figure 2 A schematic diagram of a high-frequency current signal to be processed provided in an embodiment of this application;

[0024] Figure 3 A schematic diagram of the instantaneous rate of change of the time-series energy of a signal provided in an embodiment of this application;

[0025] Figure 4 A schematic diagram of a pulse array provided in an embodiment of this application;

[0026] Figure 5 A schematic diagram of the -20dB spectral bandwidth of various pulse waveforms provided in the embodiments of this application;

[0027] Figure 6 This is a schematic diagram of an overall denoising result provided in an embodiment of this application;

[0028] Figure 7 This is a schematic diagram of a locally magnified denoising result provided in an embodiment of this application;

[0029] Figure 8 This is a schematic diagram of the structure of a noise reduction device for partial discharge detection provided in an embodiment of this application. Detailed Implementation

[0030] This application provides a noise reduction method, device, and medium for partial discharge detection.

[0031] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0032] Currently, the main methods for partial discharge detection denoising include wavelet packet denoising, fast Fourier transform thresholding, variational mode decomposition, and singular value decomposition. The main principle of these methods is to decompose the noisy signal according to different criteria, remove the noise components, and then reconstruct the signal to achieve denoising. However, these methods often only remove white noise or periodic narrowband interference, making it difficult to meet the denoising requirements of complex noise interference in the field. Furthermore, these methods have limitations in practical applications due to the need to manually set the number of decomposition layers, the attenuation of the partial discharge signal, and the large computational load. In conclusion, denoising algorithms that can effectively remove multiple types of noise interference without attenuating the partial discharge signal still require further research.

[0033] This application proposes a denoising method for partial discharge detection, which can effectively remove noise interference at the converter station site without attenuating the partial discharge signal. First, the time-series energy accumulation rate of the signal is calculated, and then the instantaneous change rate of the time-series energy is determined. Next, the corresponding waveform is extracted from the original signal based on the instantaneous change rate to obtain a pulse array. Finally, each pulse waveform is analyzed to calculate the bandwidth corresponding to a 20dB attenuation of the spectral amplitude from its maximum value. Based on the difference in bandwidth between the partial discharge pulse and the interference pulse, the partial discharge signal is identified, and pulse interference is eliminated, thereby achieving denoising for partial discharge detection.

[0034] The technical solutions proposed in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0035] Figure 1 A flowchart of a noise reduction method for partial discharge detection provided in an embodiment of this application is shown below. Figure 1 As shown, the noise reduction method for partial discharge detection includes the following steps:

[0036] S101. Determine the instantaneous rate of change of the time-series energy of the high-frequency current signal to be processed, and plot the instantaneous rate of change curve of the time-series energy.

[0037] In one embodiment of this application, the time-series energy accumulation rate corresponding to each data point in the high-frequency signal to be processed is determined based on a preset time-series energy accumulation rate function. The instantaneous change rate of the time-series energy is obtained based on the difference between the time-series energy accumulation rate corresponding to the (k+1)th data point and the time-series energy accumulation rate corresponding to the kth data point.

[0038] Specifically, the temporal energy accumulation rate describes the distribution of signal energy over time. The definition of the temporal energy accumulation rate of a signal at the k-th data point is as follows:

[0039]

[0040] Where N is the number of sampling points of the signal, and x(i) represents the amplitude corresponding to the i-th data point of the signal. The denominator represents the total energy of the signal, and normalization based on the total energy of the signal can eliminate the influence of the signal amplitude.

[0041] The instantaneous rate of change ΔE of the time series energy reflects the instantaneous growth of the signal's time series energy, and is calculated according to the following formula:

[0042] ΔE=E(k+1)-E(k) (2)

[0043] Where E(k) represents the time energy accumulation rate corresponding to the k-th data point of the signal; E(k+1) represents the time energy accumulation rate corresponding to the (k+1)-th data point of the signal; and ΔE is the instantaneous rate of change of the time energy.

[0044] S102. Window the instantaneous change rate curve of time-series energy, compare the instantaneous change rate of time-series energy corresponding to the data points in the window with the preset filtering threshold, and perform noise reduction on the high-frequency current signal to be processed based on the comparison result.

[0045] In one embodiment of this application, the instantaneous change rate of temporal energy corresponding to all data points within the window is sorted from smallest to largest. Among the sorted data points, the instantaneous change rate of temporal energy corresponding to the data points at a preset percentage position is determined, and the instantaneous change rate of temporal energy corresponding to the data points at the preset percentage position is used as a preset filtering threshold.

[0046] Specifically, for white noise and periodic narrowband interference, the instantaneous rate of change of the signal's temporal energy is very small. To remove this noise interference, the filtering threshold is set as follows in this embodiment:

[0047] ΔE T =ΔE 80％ (3)

[0048] Where, ΔE T The preset filter threshold; ΔE 80％This indicates that the instantaneous change rate of the time-series energy of all data points in the signal is sorted in ascending order, and the instantaneous change rate of the time-series energy corresponding to exactly 80% of the data points is greater than the instantaneous change rate of the time-series energy.

[0049] It should be noted that, in this embodiment of the application, the instantaneous change rate of time-series energy corresponding to more than 80% of the data points is preferably used as the preset filtering threshold. In application, it can be adjusted according to the actual situation, and this embodiment of the application does not limit it.

[0050] In one embodiment of this application, within a window, the number of data points whose instantaneous rate of change of time-series energy is greater than a preset filtering threshold is determined. If the number of data points is greater than the data volume percentage threshold corresponding to the window, it is determined that a pulse waveform exists in the window, and the curve corresponding to the window is retained. Otherwise, the amplitude of the curve corresponding to the window is set to zero to perform a denoising operation on the high-frequency current signal to be processed. This denoising operation includes at least removing white noise and periodic narrowband interference from the high-frequency current signal to be processed.

[0051] Specifically, the instantaneous rate of change curve of time-series energy is windowed. The window size can be 150 data points, or it can be adjusted according to the actual application. If more than 20% of the data points within the window correspond to an instantaneous rate of change of time-series energy greater than a preset filtering threshold ΔE, the window is used to filter the energy. T If the waveform is positive, it is considered that a pulse waveform exists within the window and is retained; otherwise, the amplitude of the curve within the window is set to zero.

[0052] The 20% threshold is a preferred data volume percentage set in this application embodiment. It can be adjusted according to the actual situation in application, and this application embodiment does not impose any restrictions on it.

[0053] S103. In the instantaneous change rate curve of time-series energy after one denoising, determine the data points where the instantaneous change rate of time-series energy is greater than zero. Based on the data points where the instantaneous change rate of time-series energy is greater than zero, extract the pulse waveform from the high-frequency current signal to be processed and construct a pulse array.

[0054] In one embodiment of this application, data points with a time-series energy instantaneous change rate greater than zero are identified; based on a preset judgment function, a first time period corresponding to the data points with a time-series energy instantaneous change rate of zero is identified, and the high-frequency current signal to be processed corresponding to the first time period is set to zero; based on the preset judgment function, a second time period corresponding to the data points with a time-series energy instantaneous change rate greater than zero is identified, and the high-frequency current signal to be processed corresponding to the second time period is retained to obtain a pulse array.

[0055] Specifically, after removing white noise and periodic narrowband interference, the signal is traversed, and the high-frequency current signal to be processed in the time period corresponding to the data point with zero instantaneous change rate of time energy is directly set to zero; the high-frequency current signal to be processed in the time period corresponding to the data point with a greater than zero instantaneous change rate of time energy is retained, and finally a pulse array is obtained.

[0056] Specifically, based on preset conditions, such as the absolute value of the rate of change being less than a certain threshold, the identified data points with near-zero rates of change are divided into one or more time periods. For each identified first time period, the corresponding high-frequency current signal value to be processed is set to zero. Using the same preset judgment function, time periods with significantly increased energy are identified, i.e., second time periods. For each second time period, the corresponding high-frequency current signal to be processed remains unchanged. After these steps, the original high-frequency current signal to be processed is transformed into a new signal, retaining only the portion with significantly increased energy, while the portion with stable or minimal energy changes is set to zero, thus obtaining a pulse array.

[0057] In one embodiment of this application, the preset judgment function is:

[0058]

[0059] Where P(i) represents the amplitude corresponding to the i-th data point of the pulse array, x(i) represents the amplitude corresponding to the i-th data point of the high-frequency current signal to be processed, and ΔE(i) represents the instantaneous rate of change of the time-series energy corresponding to the i-th data point.

[0060] S104. Determine the spectral bandwidth corresponding to each pulse waveform in the pulse array at the preset amplitude ratio threshold, and perform secondary denoising on the high-frequency current signal to be processed based on the spectral bandwidth to obtain the partial discharge signal.

[0061] In one embodiment of this application, the spectrum corresponding to each pulse waveform in the pulse array is determined, and the maximum spectral amplitude is determined. A reference spectral amplitude corresponding to a preset amplitude percentage threshold of the maximum spectral amplitude is determined; and a first frequency and a second frequency corresponding to the reference spectral amplitude are determined. Based on the spectral bandwidth between the first frequency and the second frequency, secondary denoising is performed on the high-frequency current signal to be processed to obtain a partial discharge signal.

[0062] Specifically, if the spectral bandwidth between the first and second frequencies is greater than a preset spectral bandwidth threshold, the pulse waveform is determined to be a partial discharge signal and is preserved. If the spectral bandwidth between the first and second frequencies is not greater than the preset spectral bandwidth threshold, the pulse waveform is determined to be an interference pulse, and the amplitude of the interference pulse is set to zero.

[0063] Furthermore, the spectrum X(f) of each pulse waveform in the pulse array is calculated separately to obtain the maximum value X(f) of the spectrum amplitude. max The bandwidth corresponding to a 10-fold decrease in spectral amplitude (-20dB) from its maximum value is the -20dB spectral bandwidth, calculated using the following formula:

[0064]

[0065] Among them, B W The -20dB spectral bandwidth is represented by f1 and f2, which represent the frequencies corresponding to one-tenth of the maximum spectral amplitude. The -20dB spectral bandwidth of each pulse waveform is calculated. If the bandwidth is greater than 10MHz, it is a partial discharge signal and is retained; if the bandwidth is less than 10MHz, it is an interference pulse and its amplitude is set to zero. This completes the adaptive identification of partial discharge signals.

[0066] The following is a specific experimental verification:

[0067] (1) Figure 2 This is a schematic diagram of a high-frequency current signal to be processed, provided as an embodiment of this application. The diagram shows a high-frequency current signal collected at the grounding point of the core of a converter transformer in a converter station.

[0068] (2) Figure 3 This application provides a schematic diagram of the instantaneous rate of change of the time-series energy of a signal, as shown in the embodiment of the present application. Figure 3 As shown, the horizontal axis represents the signal time, and the vertical axis represents the instantaneous rate of change of time-series energy corresponding to each data point. Figure 3 The acquisition process is as follows: first determine Figure 2 The time-series energy accumulation rate corresponding to each data point is used to calculate the instantaneous change rate of the signal's time-series energy based on this time-series energy accumulation rate.

[0069] (3) Figure 4 This application provides a schematic diagram of a pulse array. The process of obtaining this schematic diagram is as follows: [The following text appears to be a separate, unrelated section:] ...to... Figure 3 Windowing is applied, and within the window, the number of data points whose instantaneous rate of change of time-series energy exceeds a preset filtering threshold is determined to remove white noise and periodic narrowband interference from the high-frequency current signal to be processed. Next, based on a preset judgment function, pulse waveforms for the corresponding time periods are extracted from the high-frequency current signal to be processed to construct a pulse array.

[0070] (4) Figure 5 A schematic diagram of the -20dB spectral bandwidth of various pulse waveforms provided in the embodiments of this application is shown below. Figure 5 As shown, the horizontal axis represents signal time, and the vertical axis represents the -20dB spectral bandwidth corresponding to each data point. Figure 6This is a schematic diagram of an overall denoising result provided in an embodiment of this application. Figure 7 This is a schematic diagram of a locally magnified denoising result provided in an embodiment of this application. Figure 6 and Figure 7 It is by Figure 5 The result of denoising pulse waveforms with a bandwidth greater than 10MHz is obtained, and the partial discharge signal is obtained from this.

[0071] Based on the waveform characteristics of white noise and periodic narrowband interference, this application proposes using the instantaneous rate of change of the signal's temporal energy as the identification criterion. This effectively distinguishes white noise, periodic narrowband interference, and pulse waveforms, thereby adaptively extracting the pulse waveform. Secondly, based on the spectral differences between partial discharge signals and interference pulses, this application proposes using the -20dB spectral bandwidth of the pulse waveform as an indicator to distinguish between partial discharge signals and interference signals. This enables adaptive identification of partial discharge signals with high accuracy. Furthermore, the method proposed in this application employs a time-frequency joint approach for denoising, demonstrating good denoising effects for different types of noise interference present in high-frequency current method detection, without attenuating the partial discharge signal. This application has low computational complexity and can meet the needs of rapid partial discharge detection in converter stations.

[0072] Figure 8 This is a schematic diagram of a noise reduction device for partial discharge detection provided in an embodiment of this application. Figure 8 As shown, a denoising device for partial discharge detection includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to: determine the instantaneous rate of change of the time-series energy of the high-frequency current signal to be processed, and plot the instantaneous rate of change curve of the time-series energy; perform windowing processing on the instantaneous rate of change curve of the time-series energy, compare the instantaneous rate of change of the time-series energy corresponding to the data points within the window with a preset filtering threshold, and perform a first denoising on the high-frequency current signal to be processed based on the comparison result; determine the data points with a time-series energy instantaneous rate of change greater than zero in the instantaneous rate of change curve of the time-series energy, and extract pulse waveforms from the high-frequency current signal to be processed based on the data points with a time-series energy instantaneous rate of change greater than zero, and construct a pulse array; determine the spectral bandwidth corresponding to each pulse waveform in the pulse array at a preset amplitude percentage threshold, and perform a second denoising on the high-frequency current signal to be processed based on the spectral bandwidth to obtain the partial discharge signal.

[0073] This application provides a non-volatile computer storage medium storing computer-executable instructions. The computer-executable instructions are configured to: determine the instantaneous rate of change of the time-series energy of the high-frequency current signal to be processed, and plot a curve of the instantaneous rate of change of time-series energy; perform windowing processing on the instantaneous rate of change of time-series energy curve, compare the instantaneous rate of change of time-series energy corresponding to data points within the window with a preset filtering threshold, and perform a first denoising operation on the high-frequency current signal to be processed based on the comparison result; determine data points with a time-series energy instantaneous rate of change greater than zero in the denoised instantaneous rate of change of time-series energy curve, extract pulse waveforms from the high-frequency current signal to be processed based on these data points, and construct a pulse array; determine the spectral bandwidth corresponding to each pulse waveform in the pulse array at a preset amplitude percentage threshold, and perform a second denoising operation on the high-frequency current signal to be processed based on the spectral bandwidth to obtain a partial discharge signal.

[0074] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0075] The above descriptions are merely embodiments of this application and are not intended to limit the scope of this application. For those skilled in the art, various modifications and variations can be made to the embodiments of this application. These modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions in the embodiments of this application.

Claims

1. A method of denoising partial discharge detection, characterized in that, The method comprises: determining the time sequence energy instantaneous change rate of the high-frequency current signal to be processed, and drawing a time sequence energy instantaneous change rate curve; performing window processing on the time sequence energy instantaneous change rate curve, comparing the time sequence energy instantaneous change rate of the data points in the window with a preset filtering threshold, and performing primary denoising on the high-frequency current signal to be processed based on the comparison result, including: determining the number of data points in the window whose time sequence energy instantaneous change rate is greater than the preset filtering threshold, and determining that the window has a pulse waveform if the number of data points is greater than the data amount proportion threshold corresponding to the window, and retaining the curve corresponding to the window, otherwise, setting the amplitude of the curve corresponding to the window to zero to perform primary denoising on the high-frequency current signal to be processed; wherein the primary denoising at least includes removing white noise and periodic narrowband interference in the high-frequency current signal to be processed; in the time sequence energy instantaneous change rate curve after the primary denoising, determining the data points whose time sequence energy instantaneous change rate is greater than zero, and extracting a pulse waveform from the high-frequency current signal to be processed based on the data points whose time sequence energy instantaneous change rate is greater than zero, and constructing a pulse array; determining the frequency spectrum bandwidth corresponding to each pulse waveform in the pulse array at a preset amplitude proportion threshold, and performing secondary denoising on the high-frequency current signal to be processed based on the frequency spectrum bandwidth to obtain a partial discharge signal, including: determining the frequency spectrum corresponding to each pulse waveform in the pulse array, determining the maximum frequency spectrum amplitude, determining the reference frequency spectrum amplitude at the preset amplitude proportion threshold of the maximum frequency spectrum amplitude, and determining the first frequency and the second frequency corresponding to the reference frequency spectrum amplitude; performing secondary denoising on the high-frequency current signal to be processed based on the frequency spectrum bandwidth between the first frequency and the second frequency to obtain a partial discharge signal, specifically: if the frequency spectrum bandwidth between the first frequency and the second frequency is greater than a preset frequency spectrum bandwidth threshold, determining that the pulse waveform is a partial discharge signal and performing retention processing, and if the frequency spectrum bandwidth between the first frequency and the second frequency is not greater than the preset frequency spectrum bandwidth threshold, determining that the pulse waveform is an interference pulse and performing amplitude zero processing on the interference pulse.

2. The denoising method for partial discharge detection according to claim 1, characterized in that, The determination of the time sequence energy instantaneous change rate of the high-frequency current signal to be processed specifically comprises: determining the time sequence energy accumulation rate corresponding to each data point in the high-frequency current signal to be processed based on a preset time sequence energy accumulation rate function. The time sequence energy accumulation rate corresponding to the first data point is obtained based on the first time sequence energy accumulation rate corresponding to the first data point and the second time sequence energy accumulation rate corresponding to the second data point. k The time sequence energy accumulation rate corresponding to the first data point is obtained based on the first time sequence energy accumulation rate corresponding to the first data point and the second time sequence energy accumulation rate corresponding to the second data point. k The time sequence energy accumulation rate corresponding to the first data point is obtained based on the first time sequence energy accumulation rate corresponding to the 3. The denoising method for partial discharge detection according to claim 1, characterized in that, Before the comparison of the time sequence energy instantaneous change rate of the data points in the window with the preset filtering threshold, the method further comprises: sorting the time sequence energy instantaneous change rates of all data points in the window from small to large; determining the time sequence energy instantaneous change rate of the data point at the preset proportion position in the sorted data points, and taking the time sequence energy instantaneous change rate of the data point at the preset proportion position as the preset filtering threshold.

4. The denoising method of partial discharge detection according to claim 1, characterized in that, The time series energy instantaneous change rate curve after one-time denoising is determined, and data points with time series energy instantaneous change rate greater than zero are determined, so as to extract a pulse waveform from the to-be-processed high-frequency current signal based on the data points with time series energy instantaneous change rate greater than zero, construct a pulse array, and specifically include the following steps: Determine the data points with time series energy instantaneous change rate greater than zero. Based on a preset judgment function, a first time period corresponding to the data points with time series energy instantaneous change rate of zero is determined, and the to-be-processed high-frequency current signal corresponding to the first time period is set to zero. Based on the preset judgment function, a second time period corresponding to the data points with time series energy instantaneous change rate greater than zero is determined, and the to-be-processed high-frequency current signal corresponding to the second time period is retained to obtain the pulse array.

5. The denoising method of partial discharge detection according to claim 4, characterized in that, The preset judgment function is: ； in, P ( i ) represents the pulse array i The amplitude corresponding to each data point x ( i ) represents the high-frequency current signal to be processed. i The amplitude corresponding to each data point; Δ E ( i ) indicates the first i The instantaneous rate of change of time-series energy corresponding to each data point.

6. A denoising device for partial discharge detection, characterized in that, The device includes a memory for storing computer program instructions and a processor for executing program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute the method of any one of claims 1-5.

7. A non-transitory computer storage medium storing computer-executable instructions that, when executed, cause a computer to perform: The computer executable instructions can execute the method of any one of claims 1-5.

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