Discharge detection method, device, equipment, medium and system for high-voltage switch cabinet

Abstract

The invention provides a discharge detection method, device, equipment, medium and system for a high-voltage switch cabinet, and the method comprises the steps: collecting an ultrasonic signal generated by the high-voltage switch cabinet in real time based on a microphone array; preprocessing the ultrasonic signal to obtain a discharge signal; based on a preset decomposition sound field distribution algorithm and a three-dimensional microphone array algorithm, analyzing and processing the discharge signal, and determining discharge position information; generating an acoustic image according to the discharge position information; and the acoustic image is analyzed and processed, fault early warning information is generated, and discharge detection of the high-voltage switch cabinet is completed, so that accurate positioning and quantitative analysis of partial discharge of the high-voltage switch cabinet are realized, the accuracy of discharge detection is improved, and the detection safety is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of detection of electrical equipment, and in particular to a discharge detection method, device, equipment, medium and system for a high-voltage switch cabinet. BACKGROUND

[0002] In today's highly developed modern society, electricity has become the core element driving the normal operation of various fields, and the dependence of society on electricity has reached an unprecedented height. Among them, the switch cabinet of 10kV and above voltage level is a crucial device in the power system, and its stable operation is directly related to the reliability and stability of power supply.

[0003] In the prior art, partial discharge detection of a high-voltage switch cabinet can be achieved by an electric quantity method of directly contacting the device, an optical side method relying on a transparent medium, and a radio frequency method.

[0004] However, the above-mentioned partial discharge detection methods are troublesome to operate and have poor positioning accuracy, which reduces the detection efficiency and safety. SUMMARY

[0005] The present application provides a discharge detection method, device, equipment, medium and system for a high-voltage switch cabinet to solve the technical problem of low accuracy and low safety of the existing discharge detection method for a high-voltage switch cabinet.

[0006] In a first aspect, the present application provides a discharge detection method for a high-voltage switch cabinet, comprising:

[0007] Real-time collection of ultrasonic signals generated by the high-voltage switch cabinet based on a microphone array;

[0008] Pretreatment of the ultrasonic signals to obtain discharge signals, the pretreatment including signal amplification, filtering and digital-to-analog conversion processing;

[0009] Analysis and processing of the discharge signals based on a preset decomposition sound field distribution algorithm and a three-dimensional microphone array algorithm to determine discharge position information;

[0010] Generation of an acoustic image based on the discharge position information, the acoustic image being used to show the discharge position and discharge intensity of the high-voltage switch cabinet;

[0011] Analysis and processing of the acoustic image to generate fault warning information, completing the discharge detection of the high-voltage switch cabinet.

[0012] Further, the analysis and processing of the discharge signals based on the preset decomposition sound field distribution algorithm and the three-dimensional microphone array algorithm to determine the discharge position information comprises:

[0013] performing sound field decomposition processing on the discharge signal based on a preset sound field decomposition algorithm to obtain a plurality of sub-sound fields;

[0014] performing analysis processing on the discharge signal based on a three-dimensional microphone array algorithm to determine signal time difference and signal intensity difference received between different microphones;

[0015] determining discharge position information according to the plurality of sub-sound fields, the signal time difference and the signal intensity difference.

[0016] Further, performing analysis processing on the discharge signal based on a three-dimensional microphone array algorithm to determine signal time difference and signal intensity difference received between different microphones, comprising:

[0017] performing signal acquisition processing on the discharge signal based on a three-dimensional microphone array algorithm to obtain an analysis signal, the signal acquisition processing comprising uniform signal acquisition clock and positioning signal effective segment;

[0018] performing time domain and frequency domain analysis on the analysis signal to obtain waveform characteristics of the discharge signal;

[0019] determining signal time difference and signal intensity difference received between different microphones according to the waveform characteristics of the discharge signal.

[0020] Further, generating an acoustic image according to the discharge position information, comprising:

[0021] determining discharge signal parameters, signal time difference and signal intensity difference according to the discharge position information, the discharge signal parameters comprising discharge point three-dimensional coordinates, signal intensity, frequency and duration;

[0022] associating the discharge signal parameters with the signal time difference and the signal intensity difference to obtain a position and acoustic parameter dataset;

[0023] processing the position and acoustic parameter dataset based on an acoustic imaging algorithm to generate an acoustic image.

[0024] Further, processing the position and acoustic parameter dataset based on an acoustic imaging algorithm to generate an acoustic image, comprising:

[0025] constructing a three-dimensional space grid model, each grid cell in the three-dimensional space grid model corresponding to an actual space position;

[0026] processing the position and acoustic parameter dataset based on an acoustic imaging algorithm to determine coordinates of a sound source in a three-dimensional space;

[0027] Matching the coordinates of the sound source in three-dimensional space with the three-dimensional space grid model to obtain a grid matrix with space and acoustic characteristics;

[0028] Image rendering and effect optimization are performed on the grid matrix to generate an acoustic image.

[0029] Further, the acoustic image is analyzed and processed to generate fault warning information, completing the discharge detection of the high-voltage switch cabinet, including:

[0030] The acoustic image is subjected to spectral analysis processing to obtain spectral feature information;

[0031] The spectral feature information is compared with preset spectral feature information to determine discharge fault information;

[0032] If the discharge fault information indicates that the high-voltage switch cabinet does not meet the preset discharge threshold requirement, a fault warning information is generated to complete the discharge detection of the high-voltage switch cabinet.

[0033] Further, after comparing the spectral feature information with the preset spectral feature information to determine the discharge fault information, the method further includes:

[0034] According to the discharge fault information, the fault occurrence time, position and type information are determined;

[0035] The fault occurrence time, position and type information are displayed to the user end through a visual interface, so that the user can maintain the high-voltage switch cabinet according to the fault occurrence time, position and type information.

[0036] In a second aspect, the application provides a discharge detection device for a high-voltage switch cabinet, including:

[0037] A signal acquisition module for real-time acquisition of ultrasonic signals generated by a high-voltage switch cabinet based on a microphone array;

[0038] A discharge signal obtaining module for preprocessing the ultrasonic signals to obtain a discharge signal;

[0039] A discharge position information determining module for analyzing and processing the discharge signal based on a preset decomposition sound field distribution algorithm and a three-dimensional microphone array algorithm to determine discharge position information;

[0040] An acoustic image generating module for generating an acoustic image according to the discharge position information;

[0041] A fault warning information generating module for analyzing and processing the acoustic image in time and frequency domains to generate fault warning information and complete the discharge detection of the high-voltage switch cabinet.

[0042] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory connected with the processor in communication;

[0043] The memory stores computer-executable instructions.

[0044] The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of the first aspect.

[0045] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method according to any one of the first aspect.

[0046] In a fifth aspect, the present application provides a discharge detection system of a high-voltage switch cabinet, comprising a computer program and a hardware module, wherein the computer program is executed by a processor to implement the method according to any one of the first aspect, and the hardware module is used to store data when the computer program is executed by the processor.

[0047] The discharge detection method, device, equipment, medium and system of the high-voltage switch cabinet provided by the present application are based on a microphone array, and ultrasonic signals generated by the high-voltage switch cabinet are collected in real time; the ultrasonic signals are preprocessed to obtain discharge signals; the discharge signals are analyzed and processed based on a preset decomposition sound field distribution algorithm and a three-dimensional microphone array algorithm to determine discharge position information; an acoustic image is generated according to the discharge position information; the acoustic image is analyzed and processed to generate fault early warning information, and the discharge detection of the high-voltage switch cabinet is completed, the precise positioning and quantitative analysis of the partial discharge of the high-voltage switch cabinet are realized, the accuracy of the discharge detection is improved, and the detection safety is improved. BRIEF DESCRIPTION OF DRAWINGS

[0048] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the application.

[0049] Figure 1 A flowchart of a discharge detection method embodiment one of a high-voltage switch cabinet according to the present application is shown in the figure;

[0050] Figure 2 A flowchart of a discharge detection method embodiment two of a high-voltage switch cabinet according to the present application is shown in the figure;

[0051] Figure 3 A flowchart of a discharge detection method embodiment three of a high-voltage switch cabinet according to the present application is shown in the figure;

[0052] Figure 4 A flowchart of a discharge detection method embodiment four of a high-voltage switch cabinet according to the present application is shown in the figure;

[0053] Figure 5 A structure diagram of a discharge detection device of a high-voltage switch cabinet provided in the present application is shown.

[0054] Figure 6 A structure diagram of an electronic device provided in the present application is shown.

[0055] The specific embodiments of the present application have been shown in the above-described drawings, and will be described in more detail hereinafter. These drawings and the written description are not intended to restrict the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0056] The exemplary embodiments will be described in detail herein below with reference to the drawings. In the following description, the same numbers refer to the same or similar elements unless otherwise represented. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0057] The partial discharge detection technology of the existing 10kV and above voltage grade switch cabinet is complex in operation and has the risk of electric shock and short circuit. Secondly, the optical detection method relies on transparent medium and is difficult to apply in the metal enclosed switch cabinet, and has low sensitivity. In addition, the radio frequency detection method is easily affected by environmental electromagnetic interference, has large signal positioning error, and is difficult to accurately determine the discharge point position, which reduces the safety of the power grid and the stability of the equipment operation.

[0058] In view of the above technical problems, the present application realizes the visualization and quantitative detection of partial discharge by using a three-dimensional microphone array to collect discharge ultrasonic signals in real time, using a preset decomposition sound field distribution algorithm and a three-dimensional array calculation method to accurately locate the discharge point position, and combining an acoustic imaging algorithm to generate a three-dimensional acoustic image. The acoustic image is subjected to frequency spectrum analysis and intelligent comparison, the discharge type is automatically identified, and warning information is generated, realizing non-contact safe detection and improving the safety and efficiency of the discharge detection of the high-voltage switch cabinet.

[0059] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described again in some embodiments. It should be noted that in each embodiment of the present application, unless otherwise specified, the execution subject is a discharge detection system of a high-voltage switch cabinet. The embodiments of the present application will be described below with reference to the drawings.

[0060] Figure 1Figure 1 is a flowchart of a first embodiment of a discharge detection method for a high-voltage switch cabinet according to the present application. As shown in Figure 1, the method comprises the following steps: Figure 1

[0061] S101, real-time collection of ultrasonic signals generated by the high-voltage switch cabinet based on a microphone array.

[0062] The microphone array is a signal collection system formed by arranging a plurality of microphones in a specific three-dimensional geometric structure, which is used to receive sound wave information at different positions in space. Its key parameters include array shape, channel number, sampling rate, and phase synchronization accuracy.

[0063] In the present application, the microphone array is integrated in the front end of a non-contact handheld industrial acoustic imager. The microphones in the array are arranged in an asymmetric spatial distribution, which can non-contactly collect ultrasonic signals generated during the discharge process outside the high-voltage switch cabinet in real time. When the high-voltage switch cabinet is subjected to partial discharge, it will emit high-frequency sound waves in the frequency range of 20-80 kHz. The array captures sound wave energy through built-in high-sensitivity electret sensing elements and ensures consistent phase of data from each channel through a synchronous sampling clock. The operator only needs to point the acoustic imager at the surface of the target switch cabinet to collect the discharge sound signals at a safe distance.

[0064] In another aspect, the handheld industrial acoustic imager according to the present application can simultaneously detect multiple high-voltage switch cabinet terminals within the camera range, improving the efficiency of detection and recognition and reducing monitoring costs.

[0065] This step solves the problem of the need to contact the equipment in the traditional electric quantity method and the medium limitation of the optical measurement method, realizes non-contact and real-time acoustic data collection, and significantly improves the detection safety and sensitivity.

[0066] S102, pre-processing of the ultrasonic signals to obtain discharge signals.

[0067] The signal pre-processing is a key step of amplifying, filtering, and digital-analog conversion of the original analog sound signal to improve the signal-to-noise ratio and remove environmental interference.

[0068] In this step, the signal collected by the microphone array is first amplified by a low-noise amplifier circuit to improve the amplitude, and then enters a multi-stage band-pass filter module to filter out background sound below 20 kHz and electromagnetic noise above 80 kHz. Then, the signal is digitized by an analog-to-digital converter (ADC) at a sampling rate of 192 kHz or higher to ensure signal detail fidelity. This step effectively solves the problems of noise interference and signal distortion through multi-stage filtering and high-precision sampling, ensures the quality of input data for subsequent positioning and imaging algorithms, and realizes a high-stability data collection link.

[0069] ​S103, based on the preset decomposition sound field distribution algorithm and the three-dimensional microphone array algorithm, the discharge signal is analyzed and processed to determine the discharge position information.

[0070] The preset decomposition sound field distribution algorithm is a signal decomposition method based on a sound wave propagation model, which can decompose a complex sound field into multiple sub-sound fields for accurate analysis of the sound source position; the three-dimensional microphone array algorithm calculates the time difference and intensity difference of the signal according to the array geometric relationship to calculate the three-dimensional coordinates of the sound source.

[0071] In this step, first, the expected decomposition algorithm is used to establish a sound field mathematical model, and the signal matrix received from different directions is decomposed into several sub-sound field components. Then, the time difference and amplitude difference of the signals received by each microphone are calculated by the three-dimensional array algorithm, and the coordinates of the discharge point in the three-dimensional space are obtained based on the least squares inversion method. This step overcomes the defects of traditional sound source positioning, such as being easily affected by multi-path interference and low calculation accuracy, and realizes fast and accurate positioning of the discharge source with a positioning error of less than 2 centimeters.

[0072] S104, generating an acoustic image according to the discharge position information.

[0073] The acoustic image is a visual result that presents the intensity distribution of the sound source in space in the form of an image, reflecting the sound pressure level difference through pixel brightness or pseudo-color.

[0074] In this step, the system maps the sound source energy and frequency information to a two-dimensional or three-dimensional coordinate system according to the calculated discharge coordinates, and generates a color image through acoustic imaging algorithm. By visualizing the spatial distribution of acoustic signals, the spatial position and energy size of the discharge point can be intuitively reflected, facilitating the rapid judgment of fault severity by operation and maintenance personnel and improving the inspection efficiency.

[0075] S105, analyzing and processing the acoustic image to generate fault warning information.

[0076] The fault warning information is a discharge risk prompt generated by comprehensive analysis of acoustic data and images, including discharge level, position, frequency, and time.

[0077] After the acoustic image is generated, the system calls the signal feature analysis module to extract the spectral feature and energy peak value parameters, and compares them with the preset threshold model in the background database. When the detection result exceeds the safety threshold, the system automatically marks the abnormal area and pops up a warning message through the graphical interface. At the same time, the detection result is stored and synchronized to the remote management platform, so that the operation and maintenance personnel can view it in real time through the mobile terminal, thereby solving the problems of lagging behind in traditional manual analysis and high misjudgment rate, realizing intelligent and real-time discharge risk prompt, and greatly improving the system safety monitoring capability.

[0078] The embodiment realizes safe and high-sensitivity detection of partial discharge signals of a high-voltage switch cabinet by constructing a non-contact ultrasonic signal acquisition system based on a three-dimensional microphone array. Through multi-stage amplification and band-pass filtering preprocessing, the power frequency noise and environmental interference are significantly suppressed, and the signal-to-noise ratio is effectively improved. The defects of the traditional electric quantity method needing to contact the equipment and the radio frequency method being seriously disturbed are overcome, and the partial discharge detection can be completed without opening the equipment and contacting the high-voltage conductor, thereby improving the detection safety and operation convenience. At the same time, the preprocessing module performs dynamic gain control and high-precision sampling on the signal, ensures the input data quality of the subsequent analysis algorithm, realizes stable and accurate signal acquisition, and provides a reliable foundation for subsequent positioning and imaging.

[0079] Figure 2 The flowchart of the discharge detection method for the high-voltage switch cabinet according to Embodiment 2 of the present application is shown in FIG. 2. As shown in FIG. 2, based on Embodiment 1, the discharge signal is analyzed and processed based on a preset decomposition sound field distribution algorithm and a three-dimensional microphone array algorithm to determine the discharge position information, including: Figure 2

[0080] S201, based on the preset decomposition sound field distribution algorithm, the discharge signal is subjected to sound field decomposition processing to obtain a plurality of sub-sound fields.

[0081] In this step, the processor establishes a sound field covariance matrix for the collected multi-channel signal, and obtains a sound source characteristic vector and a characteristic value through an expectation decomposition algorithm. The system sorts the sound field according to the characteristic value, extracts the main energy component and defines it as an effective sub-sound field. Each sub-sound field corresponds to a potential sound source position, so that the sound wave signals of multiple discharge points can be distinguished in a strong noise environment. This decomposition process significantly reduces the influence of multi-source interference, realizes high-precision modeling of a complex discharge sound field, and provides a stable input for subsequent three-dimensional positioning.

[0082] S202, based on the three-dimensional microphone array algorithm, the discharge signal is subjected to signal acquisition processing to obtain a signal to be analyzed.

[0083] ​The three-dimensional microphone array arrangement algorithm collects time difference and intensity difference information through spatially asymmetrically distributed microphones to improve positioning accuracy and anti-interference capability. Specifically, the system uses a unified sampling clock in the signal collection stage to ensure that all channels in the array work synchronously. To avoid phase aliasing and spatial ambiguity, the microphone array adopts a three-dimensional asymmetric layout, typically a "pyramid + eccentric point array" combination, so that the microphones are distributed at different heights, different radial distances and different azimuth angles, thereby forming a three-dimensional irregular sampling structure. The asymmetric three-dimensional array arrangement increases the difference in the propagation path of the signals received by different microphones through path difference stretching, and the time difference changes more obviously, thereby showing a clearer peak shift in the cross-correlation function, significantly improving the time difference measurement sensitivity. Secondly, through intensity difference spatial decoupling, the sound pressure intensity difference received by each microphone increases, making it easier to distinguish between multiple sound sources and reflected interference signals. Through the above optimization measures, the system can determine the discharge point coordinates with higher computational stability and accuracy in the subsequent sound source inversion process, with a positioning error of less than 2 centimeters, and the spatial resolution is improved by about 30% compared with the traditional symmetric array structure. Therefore, the asymmetric three-dimensional array structure not only physically enhances the difference in sound signal collection, but also algorithmically improves the robustness of the positioning equation, providing a core support for high-precision sound source positioning.

[0084] S203, time domain and frequency domain analysis is performed on the signal to be analyzed to obtain the waveform characteristics of the discharge signal.

[0085] Among them, the time domain analysis focuses on the amplitude characteristics of the signal changing with time, and the frequency domain analysis focuses on the energy distribution of different frequency components in the signal. Specifically, the processor performs fast Fourier transform on the sampled signal to obtain the frequency spectrum distribution, and calculates the envelope line to analyze the pulse duration. Combined with the short-time Fourier transform to generate a time-frequency feature map, the main frequency peak, energy concentration area and duration parameters of the discharge signal are extracted. Through dual-domain analysis, the discharge characteristic parameters can be accurately extracted to provide a basis for subsequent sound source positioning and type identification, significantly improving the signal recognition accuracy.

[0086] S204, according to the waveform characteristics of the discharge signal, the time difference and signal intensity difference received between different microphones are determined.

[0087] Among them, the time difference refers to the time interval of the same sound source signal reaching different microphones, which is used to calculate the sound source position; the intensity difference refers to the energy difference of the sound wave after propagation attenuation at different receiving points, which is used to assist in positioning accuracy correction.

[0088] In this step, the processor calculates the cross-correlation function peak position between each microphone based on the extracted waveform features, thereby accurately measuring the time difference of arrival of the signal. Since the partial discharge signal is short pulse type, the system uses sub-sample interpolation algorithm to improve the time resolution to the microsecond level. At the same time, through the integral calculation of the energy of each microphone sampling signal, the signal intensity difference matrix is obtained, which solves the time difference error problem caused by insufficient sampling rate or signal overlap in the traditional algorithm, significantly improves the accuracy and stability of spatial positioning, and provides high-resolution input for three-dimensional positioning.

[0089] S205, determining discharge position information according to the plurality of sub-acoustic fields, the signal time difference and the signal intensity difference.

[0090] Among them, the discharge position information refers to the specific coordinates of the partial discharge point in the three-dimensional space obtained by acoustic measurement, including X, Y, Z position parameters and error range.

[0091] In this step, based on the discharge positioning of the sound field distribution decomposition algorithm, the system can first use the beam forming algorithm or the sound intensity vector imaging method to process the signal and generate multiple groups of sub-acoustic field distribution graphs. For each frame of data, the spatial grid is divided to obtain multiple small cubic grids, and then the energy back-projection modeling is performed on each grid, that is, each grid point is assumed to be a possible sound source, the theoretical sound field distribution of each grid point is calculated by inverting its contribution to all array elements. Then calculate the residual between the theoretical sound field distribution of the grid point and the measured sound field. Then select the point with the minimum residual as the suspected discharge point, and re-subdivide the grid in the minimum residual area for secondary or multiple searches to improve the positioning accuracy, and finally control the convergence error within the preset error range.

[0092] In another embodiment, the application uses a pre-arranged three-dimensional microphone array structure, combines the principle of sound wave propagation, and inversely estimates the position of the partial discharge sound source through two dimensions of signal time difference and signal intensity difference.

[0093] Illustratively, the system first reads the spatial installation position of each microphone in the microphone array, which has been fixed by measurement during the system deployment stage and stored as a geometric model. When a partial discharge event occurs, the sound signal will propagate at the speed of sound and be received by different microphones. The system records the time when each microphone receives the sound wave through a high-precision timing synchronization module, and calculates the time difference between each pair of microphones. The time difference represents the difference in relative distance between the sound source and each microphone. Specifically, first, the collection model of the microphone array is set, assuming that the system contains n microphones located at known spatial coordinates , where i = 1, 2, n. Assuming that the sound source is an unknown coordinate point s, where .

[0094] Then the time difference equation set is constructed, taking the first microphone as the reference, and the formula is:

[0095]

[0096] where c is the speed of sound, is the time difference of i microphones relative to the reference channel.

[0097] At the same time, the intensity of the sound wave received by each microphone is recorded. Since the sound wave attenuates with distance during propagation in the air, the received signal intensity also reflects the approximate distance between the sound source and the receiving point. The system can further determine the approximate direction and distance trend of the sound source by analyzing the intensity difference. The formula for enhancing the intensity difference modeling is:

[0098]

[0099] The energy Pi of each channel is constrained with distance, and the error sensitivity is introduced as a weight adjustment. Finally, all the above time difference and intensity difference constraints are unified into an optimization problem, and the formula is as follows:

[0100]

[0101] where, is the regularization coefficient, is the weight of each pair of time difference equations.

[0102] Finally, the system integrates the above two types of information and matches them with the array geometry model to build a spatial inversion solving framework. In this framework, each possible position is considered as a potential sound source point, and the system simulates the results of the sound wave produced by these points after propagation in the array, and compares the simulation values with the actual collected time difference and intensity difference.

[0103] In order to quickly converge and obtain high-precision positioning results, the present application adopts an iterative optimization strategy: initially search from the area with the maximum sound source energy, gradually narrow the search range, and adjust the estimated point according to the residual feedback in each round. When the error between the estimated position and the measured data is less than a set threshold (for example, within 2 cm), the iteration is stopped. The finally determined sound source position is output as "discharge position information", including three-dimensional coordinate values and corresponding confidence evaluation. At the same time, this information will be stored in the cache of the device for subsequent generation of acoustic images.

[0104] This step realizes the accurate positioning of the partial discharge point in three-dimensional space, effectively solves the industry pain point of "detectable but difficult to locate" in traditional detection, and provides quantifiable spatial basis for power equipment operation and maintenance

[0105] The embodiment adopts a combination of a preset decomposition sound field distribution algorithm and a three-dimensional microphone array algorithm to realize accurate positioning of the partial discharge point. By decomposing the complex sound field into multiple independent sub-sound fields and combining time difference and intensity difference information for spatial inversion, the problems of multi-source interference, large positioning error and serious environmental impact in traditional methods are effectively solved. The three-dimensional asymmetric microphone array structure improves the time difference calculation accuracy and spatial resolution, and the discharge source positioning error is controlled within the centimeter level. The technology can operate stably in a complex electromagnetic environment, providing high robustness and high precision positioning capability for partial discharge detection, significantly improving the accuracy and efficiency of equipment inspection.

[0106] Figure 3 The flowchart of the third embodiment of the discharge detection method of the high-voltage switch cabinet proposed in the present application is shown in FIG. 8. As shown in FIG. 8, on the basis of the first embodiment, an acoustic image is generated according to the discharge position information, including: Figure 3

[0107] S301, according to the discharge position information, the discharge signal parameter, the signal time difference and the signal intensity difference are determined.

[0108] Among them, the discharge signal parameter refers to the key numerical value describing the characteristics of partial discharge, including the three-dimensional coordinates of the discharge point, the signal intensity, the frequency and the duration.

[0109] Specifically, after positioning the discharge position information, the processor matches the discharge point coordinates with the corresponding sound signal characteristics and extracts the physical property data of each sound source. The system automatically records the signal time difference (for positioning accuracy correction), the signal intensity difference (for energy distribution modeling) and the sound pressure peak frequency and other key indicators. Then all parameters are stored in the acoustic parameter library in the form of structured data to ensure that they can be directly called later. This step realizes the quantitative feature extraction of the discharge signal by systematic parameter archiving, and enhances the accuracy and repeatability of subsequent image generation and analysis.

[0110] S302, the discharge signal parameter is associated with the signal time difference and the signal intensity difference, and the position and acoustic parameter data set is obtained.

[0111] Among them, the position and acoustic parameter data set is a multi-dimensional data set formed by combining spatial coordinates and acoustic characteristic information, which is used as the input of the acoustic imaging algorithm.

[0112] ​Specifically, the system associates and fuses the spatial coordinates of each detection point with acoustic characteristics (including signal amplitude, frequency distribution, energy spectrum density) to generate a unified data set. The association process uses a weighted fusion model to reflect the distance accuracy with a time difference weight and the energy contribution with an intensity difference weight. The data set contains complete spatial information of the sound field and is the core input for acoustic image generation. This processing process solves the fusion error problem caused by scattered data dimensions in traditional acoustic imaging, ensures the consistency and integrity of the imaging input, and thus improves the image reconstruction accuracy.

[0113] S303, a three-dimensional space grid model is constructed.

[0114] The three-dimensional space grid model is to divide the detection area into a plurality of voxel units, each unit corresponding to an actual space position, for representing sound pressure values or energy density distribution.

[0115] Specifically, the system automatically constructs a three-dimensional grid corresponding to the actual space according to the geometric size of the microphone array and the measured high-voltage switch cabinet. The typical grid density is one unit per cubic centimeter, which can balance the resolution and computational load. Each grid unit corresponds to a sound pressure value, and the unsampled area is filled by an interpolation compensation algorithm. This step solves the problem that the sound source positioning result cannot be intuitively displayed, provides a spatial carrier for subsequent acoustic image rendering, and realizes the basic construction of discharge source visualization.

[0116] S304, based on an acoustic imaging algorithm, the position and acoustic parameter data set is processed to determine the coordinates of the sound source in the three-dimensional space.

[0117] The acoustic imaging algorithm is a calculation method for mapping sound pressure level and spatial coordinates, which is often combined with beamforming or delay-and-sum technology to enhance sound source positioning.

[0118] In this step, the system uses an improved delay-and-sum algorithm to perform weighted superposition after delay correction of the signals received by different microphones to enhance the energy of the real sound source and suppress noise. Energy peak detection of the superposition result can determine the sound source coordinates and mark the energy center point. Through the above acoustic imaging algorithm, the abstract signal data is converted into quantifiable spatial results, realizing high-precision sound source reconstruction and significantly improving the discharge visualization effect.

[0119] S305, matching the sound source coordinates with the three-dimensional space grid model to obtain a grid matrix with spatial and acoustic characteristics.

[0120] The grid matrix is a three-dimensional space data structure containing the spatial coordinates and corresponding acoustic parameters of each grid unit, which is used for image rendering.

[0121] This step maps the sound source coordinates obtained in S304 to the three-dimensional grid generated in S303, assigns values to adjacent grid cells according to the sound source energy size, and generates a continuous energy distribution field using a trilinear interpolation algorithm. Then generate the sound pressure level matrix and store it in the GPU memory in matrix form for the image rendering module to call. Through matching and interpolation, the conversion of sound field data from discrete points to continuous field is realized, forming a complete acoustic energy distribution model, laying the foundation for image rendering.

[0122] S306, image rendering and effect optimization are performed on the grid matrix to generate an acoustic image.

[0123] Among them, image rendering is the process of converting data matrix into visual image through computer graphics algorithm, and effect optimization includes color mapping, light simulation and edge sharpening.

[0124] In this step, the system calls the graphics engine to perform pseudo-color mapping on the sound pressure matrix, with different sound pressure levels corresponding to different color scales (such as blue→green→red). To enhance the visual contrast, the system uses Gaussian smoothing and histogram equalization algorithm to optimize the image effect. The final generated acoustic image can not only reflect the sound source intensity distribution, but also has strong spatial hierarchy, which can be directly displayed on the device screen.

[0125] This step realizes the three-dimensional visualization of partial discharge, which facilitates the rapid identification of discharge points and energy distribution by maintenance personnel, and improves the detection intuitiveness and practicality.

[0126] This embodiment realizes the visualization of discharge position information, sound pressure intensity and frequency characteristics by constructing a three-dimensional space grid model and combining acoustic imaging algorithm to generate intuitive acoustic images. This method not only realizes the conversion of sound field data from abstract signal to graphical image, but also makes the spatial distribution and energy difference of partial discharge obvious through image rendering and effect optimization. Compared with the traditional detection method of only outputting numerical data, the acoustic image generation technology of this scheme greatly improves the detection intuitiveness and diagnosis efficiency, and the maintenance personnel can directly see the discharge point position and intensity without complex analysis, reducing the dependence on professionals, and providing a visual basis for subsequent fault analysis and risk assessment.

[0127] Figure 4 The flowchart of the discharge detection method for high-voltage switchgear according to the fourth embodiment of the present application is shown in Figure Figure 4 As shown in Figure 1, on the basis of the first embodiment, the acoustic image is analyzed and processed to generate fault warning information, and the discharge detection of the high-voltage switchgear is completed, including:

[0128] S401, performing frequency spectrum analysis on the acoustic image to obtain frequency spectrum feature information.

[0129] Among them, the spectrum analysis is the frequency domain analysis of the acoustic image corresponding signal, which is used to identify the discharge type and intensity. In this step, the system extracts the original signal data corresponding to the energy peak area from the generated acoustic image, performs fast Fourier transform to obtain the spectrum curve, and automatically identifies the frequency main peak, harmonic structure and energy density distribution. For different discharge types (such as floating discharge, creeping discharge, and sharp discharge), the spectrum patterns are significantly different, so these features are stored in the feature vector group.

[0130] This step realizes the linkage of acoustic image and spectrum analysis, provides basic data support for discharge mode recognition, and improves the accuracy of discharge recognition.

[0131] S402, compare the spectrum feature information with the preset spectrum feature information, and determine the discharge fault information.

[0132] Among them, the preset spectrum feature information refers to the typical discharge mode spectrum template saved in the system database, which is used to identify the type of detection signal.

[0133] In this step, the system uses machine learning classification algorithm to match the detection spectrum and template, and calculates the similarity. When the similarity is higher than the set threshold (for example, 0.9), the fault type can be determined. If the spectrum feature and the template do not match, it is automatically recorded as "unknown discharge" and stored for subsequent update of the feature library.

[0134] This step realizes the automatic recognition of discharge type through intelligent comparison, reduces the error of manual analysis, and improves the degree of detection automation.

[0135] S403, if the discharge fault information does not meet the preset discharge threshold requirement, generate fault warning information.

[0136] Among them, the preset discharge threshold is determined according to different voltage levels of switch cabinet models, operating environment and discharge type. Its setting principles include sound pressure peak limit, energy integral threshold, spectrum feature deviation and statistical baseline model. The system collects a large amount of acoustic sample data through standard switch cabinet partial discharge experiment when initially deployed, and establishes a discharge feature parameter database.

[0137] Exemplarily, the algorithm takes the peak sound pressure, discharge duration, dominant frequency distribution, and sound energy density as core indicators, determines the upper limit of normal operation by statistical average and three standard deviations, and uses the same as the initial threshold. During operation, the system monitors the acoustic characteristics drift caused by background noise and equipment aging in real time, dynamically updates the noise baseline using the sliding window average algorithm, and realizes adaptive adjustment of the threshold. When the detection result meets any of the following conditions, the system automatically triggers an early warning: ① the peak sound pressure exceeds 120% of the noise baseline threshold; ② the discharge energy integral exceeds 150% of the baseline threshold; ③ the dominant frequency drift exceeds 2 kHz and the duration exceeds 100 μs. According to the degree of overrun, the warning is divided into three levels: slight warning, serious warning, and critical warning, and different colors or sound and light signals are used to prompt in real time on the device end.

[0138] This step realizes a quantitative and verifiable early warning standard through an experimental model and a dynamically corrected threshold setting method, solves the false alarm and missed alarm problems that often occur in traditional fixed threshold methods, and enables the system to have adaptive and reproducible real-time judgment capabilities.

[0139] S404, according to the discharge fault information, determine the fault occurrence time, location and type information.

[0140] Among them, the fault occurrence time, location and type information respectively refer to the specific timestamp of the fault occurrence, the discharge point spatial coordinates and the discharge mode category.

[0141] In this step, the system time-stamps the acoustic signal stream in the detection period, and combines the discharge spatial coordinates obtained in S205 to associate the three-dimensional positioning information with the spectrum analysis results, forming a multi-dimensional data structure containing "time-space-feature". The discharge type is automatically identified by the aforementioned spectrum comparison module and can be classified into types such as floating discharge, creeping discharge, sharp-point discharge, and composite discharge. The processor integrates these data into a structured fault record table, including fault number, occurrence time, sound source coordinates (X, Y, Z), energy level, spectral peak value, and type label, and stores them in the local database and the background cloud system.

[0142] This step enables each discharge event to have complete spatio-temporal and energy characteristic description, forming a traceable historical data chain, providing a data basis for trend analysis, degradation prediction and equipment state evaluation, and improving the equipment operation state management capability.

[0143] S405, display the fault time, location and type information to the user end through a visual interface.

[0144] Among them, the visual interface is an interactive display system for users, used to display real-time detection data and fault status.

[0145] In this step, the fault time, location and type information are displayed to the user through the visual interface. The background software system provides a multi-dimensional data visualization interface, and the operation and maintenance personnel can remotely view the real-time detection results and early warning status through a tablet, computer or mobile terminal. In the interface, a pseudo-color acoustic image is superimposed on a schematic diagram of the switch cabinet structure, with a red highlight area corresponding to a high-risk discharge point, and a time axis and discharge waveform curve are also displayed. The system also supports fault list, historical curve, spectrum graph comparison and trend prediction functions, and the operation and maintenance personnel can adjust the threshold value, select the region and perform historical backtracking operations through the interface.

[0146] This step significantly improves the operation and maintenance efficiency and response speed through the information visualization and remote access function of the human-computer interaction interface, changes the detection system from "offline analysis" to "real-time monitoring", realizes the goal of intelligent operation and maintenance management of power equipment, and greatly improves the operation and maintenance efficiency and decision-making accuracy.

[0147] This embodiment realizes discharge type recognition and automatic early warning by intelligently analyzing and comparing the spectral features of acoustic images, combined with preset threshold values and feature templates. It can generate early warning information in real time according to the energy level, frequency characteristics and spatial position of the detection signal and push it to the user end, thereby realizing intelligent, real-time and visual management of discharge detection. It effectively solves the problems of traditional manual inspection, such as dependence on experience, analysis lag and high misjudgment rate, has self-learning and dynamic updating capabilities, can continuously optimize the recognition accuracy, improves the safety monitoring capability and operation automation level of the power system, and has good engineering practical value and promotion prospects.

[0148] Figure 5 The structure diagram of the discharge detection device for the high-voltage switch cabinet proposed in this application is shown in FIG. 1. Figure 5 As shown in FIG. 1, the discharge detection device 500 for the high-voltage switch cabinet includes a signal acquisition module 501, a discharge signal obtaining module 502, a discharge position information determining module 503, an acoustic image generating module 504 and a fault early warning information generating module 505. Among them,

[0149] The signal acquisition module 501 is configured to acquire ultrasonic signals generated by the high-voltage switch cabinet in real time based on a microphone array.

[0150] The discharge signal obtaining module 502 is configured to preprocess the ultrasonic signals to obtain discharge signals.

[0151] The discharge position information determining module 503 is configured to analyze and process the discharge signals based on a preset decomposition sound field distribution algorithm and a three-dimensional microphone array algorithm to determine discharge position information.

[0152] The acoustic image generating module 504 is configured to generate an acoustic image according to the discharge position information.

[0153] The fault early warning information generation module 505 is configured to perform time domain and frequency domain analysis on the acoustic image, and generate the fault early warning information, thereby completing discharge detection of the high-voltage switch cabinet.

[0154] Further, the signal acquisition module 501 is configured to receive and store data, that is, receive detection data and acoustic images transmitted by the handheld industrial acoustic imager, and store the detection data and acoustic images in a database, thereby facilitating subsequent query and analysis.

[0155] Further, the discharge signal obtaining module 502 is further configured to:

[0156] perform acoustic field decomposition processing on the discharge signal based on a preset decomposition acoustic field distribution algorithm to obtain a plurality of sub-acoustic fields;

[0157] perform analysis and processing on the discharge signal based on a three-dimensional microphone array algorithm to determine signal time difference and signal intensity difference received between different microphones;

[0158] determine discharge position information according to the plurality of sub-acoustic fields, the signal time difference and the signal intensity difference.

[0159] Further, the discharge signal obtaining module 502 is further configured to:

[0160] perform signal acquisition processing on the discharge signal based on the three-dimensional microphone array algorithm to obtain an analysis signal, and the signal acquisition processing includes uniform signal acquisition clock and positioning of a signal effective segment;

[0161] perform time domain and frequency domain analysis on the analysis signal to obtain waveform characteristics of the discharge signal;

[0162] determine signal time difference and signal intensity difference received between different microphones according to the waveform characteristics of the discharge signal.

[0163] Further, the acoustic image generation module 504 is further configured to:

[0164] determine discharge signal parameters, signal time difference and signal intensity difference according to the discharge position information, and the discharge signal parameters include discharge point three-dimensional coordinates, signal intensity, frequency and duration;

[0165] perform association processing on the discharge signal parameters, the signal time difference and the signal intensity difference to obtain a position and acoustic parameter dataset;

[0166] perform processing on the position and acoustic parameter dataset based on an acoustic imaging algorithm to generate an acoustic image.

[0167] Further, the acoustic image generation module 504 is further configured to:

[0168] A three-dimensional space grid model is constructed, and each grid cell in the three-dimensional space grid model corresponds to an actual space position;

[0169] Based on the acoustic imaging algorithm, the position and acoustic parameter data set is processed to determine the coordinates of the sound source in the three-dimensional space;

[0170] The coordinates of the sound source in the three-dimensional space are matched with the three-dimensional space grid model to obtain a grid matrix with spatial and acoustic characteristics;

[0171] The grid matrix is subjected to image rendering and effect optimization to generate an acoustic image.

[0172] Specifically, the discharge signal obtaining module 502, the discharge position information determining module 503, and the acoustic image generating module 504 mainly analyze and process data, that is, deeply analyze the stored discharge data of the high-voltage switch cabinet, including waveform spectrum analysis, historical data comparison, etc. By analyzing the waveform spectrum graph, the characteristics of partial discharge are further identified, and the fault type and severity are judged.

[0173] Further, the fault warning information generating module 505 is further used for:

[0174] Performing spectrum analysis processing on the acoustic image to obtain spectrum feature information;

[0175] Comparing the spectrum feature information with preset spectrum feature information to determine discharge fault information;

[0176] If the discharge fault information indicates that the high-voltage switch cabinet does not meet the preset discharge threshold requirement, the fault warning information is generated, and the discharge detection of the high-voltage switch cabinet is completed.

[0177] Further, the fault warning information generating module 505 is further used for:

[0178] According to the discharge fault information, determine the fault occurrence time, position and type information;

[0179] Through the visual interface, the fault occurrence time, position and type information are displayed to the user end, so that the user can maintain the high-voltage switch cabinet according to the fault occurrence time, position and type information.

[0180] Specifically, the fault warning information generating module 505 mainly performs fault warning and management, that is, according to the acoustic image result, a reasonable threshold is set, when the detection data exceeds the threshold, the fault warning information is timely sent to remind the operation and maintenance personnel to process. At the same time, the fault information is managed, and the time, position, type and other information of the fault occurrence are recorded to provide basis for equipment maintenance.

[0181] Figure 6A structural schematic diagram of an electronic device is provided for the embodiments of the present application. As shown in the figure, the electronic device 60 comprises: Figure 6

[0182] The electronic device 60 can comprise a processor 601 with one or more processing cores, a memory 602 with one or more computer readable storage media, a communication module 603, and an ultrasonic pickup 604, etc. Among them, the processor 601, the memory 602, the communication module 603, and the ultrasonic pickup 604 are connected through a bus 605.

[0183] In the specific implementation process, the at least one processor 601 executes the computer execution instructions stored in the memory 602, so that the at least one processor 601 executes the discharge detection method of the high-voltage switch cabinet as above.

[0184] The specific implementation process of the processor 601 can refer to the above method embodiments. It adopts an embedded processor to perform real-time processing on the pre-processed signal, calculates the position information of the partial discharge, and generates an acoustic image. The processor has high-speed computing power and powerful data processing capability, ensuring the real-time performance and accuracy of the system.

[0185] The communication module 603 integrates a wireless communication module, supports Wi-Fi, Bluetooth, and other communication protocols, and can transmit detection data and acoustic images to the background software to realize remote management and analysis of data.

[0186] The ultrasonic pickup 604 is responsible for collecting ultrasonic signals generated by the discharge of the high-voltage switch cabinet based on a preset decomposition sound field distribution algorithm and a three-dimensional microphone array algorithm.

[0187] In addition, it also includes a power module to provide stable power supply for the entire imager, and uses a rechargeable battery to ensure that the device can work stably for a long time on site.

[0188] The memory can contain a random access memory (RAM) and can also include a non-volatile memory (NVM), such as at least one disk memory.

[0189] ​The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0190] The embodiment also provides a discharge detection system of a high-voltage switch cabinet, comprising a computer program and a hardware module, the computer program realizes the discharge detection method of the high-voltage switch cabinet as above when executed by a processor, and the hardware module is used for storing data when the computer program is executed by the processor

[0191] The specific implementation of each operation can refer to the foregoing embodiments, and will not be described here.

[0192] Those skilled in the art can understand that all or part of the steps of various methods in the above embodiments can be completed by instructions, or by related hardware controlled by the instructions, the instructions can be stored in a computer readable storage medium and loaded and executed by a processor.

[0193] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0194] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a series of action combinations, but those skilled in the art should know that the present application is not limited to the order of the described actions, because according to the present application, certain steps can be performed in other order or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0195] It should be understood that the present application is not limited to the precise construction that has been described above and illustrated in the accompanying drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the application is limited only by the claims that follow.

Claims

1. A discharge detection method for a high-voltage switchgear, characterized in that, include: Based on a microphone array, ultrasonic signals generated by high-voltage switchgear are acquired in real time. The ultrasonic signal is preprocessed to obtain a discharge signal. The preprocessing includes signal amplification, filtering, and digital-to-analog conversion. Based on the preset decomposition sound field distribution algorithm and three-dimensional microphone array algorithm, the discharge signal is analyzed and processed to determine the discharge location information; Based on the discharge location information, an acoustic image is generated, which is used to show the discharge location and discharge intensity of the high-voltage switchgear. The acoustic image is analyzed and processed to generate fault warning information, thus completing the discharge detection of the high-voltage switchgear.

2. The discharge detection method according to claim 1, characterized in that, Based on a preset sound field distribution decomposition algorithm and a three-dimensional microphone array algorithm, the discharge signal is analyzed and processed to determine the discharge location information, including: Based on a preset decomposition sound field distribution algorithm, the discharge signal is subjected to sound field decomposition processing to obtain multiple sub-sound fields; Based on the three-dimensional microphone array algorithm, the discharge signal is analyzed and processed to determine the signal time difference and signal strength difference received by different microphones; The discharge location information is determined based on the multiple sub-sound fields, the signal time difference, and the signal intensity difference.

3. The method according to claim 2, characterized in that, Based on a three-dimensional microphone array algorithm, the discharge signal is analyzed and processed to determine the time difference and signal strength difference between the signals received by different microphones, including: Based on a three-dimensional microphone array algorithm, the discharge signal is processed to obtain the signal to be analyzed. The signal processing includes a unified signal acquisition clock and positioning of the effective segment of the signal. The waveform characteristics of the discharge signal are obtained by performing time-domain and frequency-domain analysis on the signal to be analyzed. Based on the waveform characteristics of the discharge signal, the signal time difference and signal strength difference received by different microphones are determined.

4. The method according to claim 1, characterized in that, Based on the discharge location information, an acoustic image is generated, including: Based on the discharge location information, discharge signal parameters, signal time difference, and signal strength difference are determined. The discharge signal parameters include the three-dimensional coordinates of the discharge point, signal strength, frequency, and duration. The discharge signal parameters are correlated with the signal time difference and the signal intensity difference to obtain a location and acoustic parameter dataset; Based on the acoustic imaging algorithm, the location and acoustic parameter dataset is processed to generate an acoustic image.

5. The discharge detection method according to claim 4, characterized in that, Based on an acoustic imaging algorithm, the location and acoustic parameter dataset is processed to generate an acoustic image, including: Construct a three-dimensional spatial mesh model, in which each mesh cell corresponds to an actual spatial location; Based on the acoustic imaging algorithm, the location and acoustic parameter dataset is processed to determine the coordinates of the sound source in three-dimensional space; The coordinates of the sound source in three-dimensional space are matched with the three-dimensional space grid model to obtain a grid matrix with spatial and acoustic characteristics; The grid matrix is ​​then rendered and its effects optimized to generate an acoustic image.

6. The discharge detection method according to claim 1, characterized in that, The acoustic image is analyzed and processed to generate fault early warning information, and the discharge detection of the high-voltage switchgear is completed, including: The acoustic image is subjected to spectral analysis to obtain spectral feature information; The spectral feature information is compared with preset spectral feature information to determine the discharge fault information; If the discharge fault information indicates that the high-voltage switchgear does not meet the preset discharge threshold requirements, then a fault warning information is generated to complete the discharge detection of the high-voltage switchgear.

7. The discharge detection method according to claim 6, characterized in that, After comparing the spectral feature information with preset spectral feature information to determine the discharge fault information, the method further includes: Based on the discharge fault information, determine the fault occurrence time, location, and type. The time, location, and type of the fault are displayed to the user through a visual interface, enabling the user to maintain the high-voltage switchgear based on the fault's time, location, and type.

8. A discharge detection device for a high-voltage switchgear, characterized in that, include: The signal acquisition module is used to acquire ultrasonic signals generated by the high-voltage switchgear in real time based on a microphone array; The discharge signal acquisition module is used to preprocess the ultrasonic signal to obtain the discharge signal; The discharge location information determination module is used to analyze and process the discharge signal based on a preset decomposition sound field distribution algorithm and a three-dimensional microphone array algorithm to determine the discharge location information; An acoustic image generation module is used to generate an acoustic image based on the discharge location information; The fault warning information generation module is used to perform time-domain and frequency-domain analysis and processing on the acoustic image to generate fault warning information and complete the discharge detection of the high-voltage switchgear.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 7.

11. A discharge detection system for a high-voltage switchgear, characterized in that, The method includes a computer program and a hardware module, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 7, and the hardware module is used to store data when the computer program is executed by the processor.

Images