An unmanned aerial vehicle stationing detection system and method

Abstract

The application belongs to the technical field of partial discharge of power equipment, and provides a UAV partial discharge detection system and method, in order to solve the problem that the motor rotation noise of the UAV itself and the frequency band of the partial discharge signal are highly overlapped, and the precision of the partial discharge detection is affected, in the partial discharge detection load module, sound insulation walls for isolating interference noise are arranged around the microphone array, most of the interference noise is isolated from the sensing end, the anti-interference effect on the UAV body noise during the partial discharge detection is realized, and the detection precision is improved; meanwhile, the beam forming algorithm in the voiceprint information processing module is adopted, the noise and the partial discharge signal are further stripped in the rear end, the effect of isolating the interference noise is greatly improved, and the detection precision is improved.

Description

Technical Field

[0001] This invention belongs to the field of partial discharge technology for power equipment, and particularly relates to a partial discharge detection system and method for unmanned aerial vehicles (UAVs). Background Technology

[0002] Partial discharge is a major cause of insulation breakdown in high-voltage power equipment and a significant indicator of insulation degradation. In recent years, with the continuous increase in voltage levels of power equipment, strong partial discharges from high-voltage equipment cause a faster decline in insulation strength, ultimately leading to equipment damage and seriously affecting the safe and stable operation of the power grid. Because partial discharge is difficult to detect with the naked eye, and regular maintenance and preventative insulation testing are time-consuming and labor-intensive, how to efficiently and accurately detect partial discharge has become a hot topic in recent years.

[0003] Currently, the main methods for partial discharge detection in primary power equipment include pulse current method, ultra-high frequency detection method, and ultrasonic detection method. Among them, pulse current detection method has a low measurement frequency, narrow bandwidth, and relatively less information, and weak anti-interference ability; ultra-high frequency detection method and ultrasonic detection method require manual hand-held sensor probe for contact measurement, which is costly and greatly affected by background interference. Each method has its shortcomings.

[0004] The inventors discovered that as power grids become increasingly intelligent, drones are gradually replacing humans in inspection tasks, saving labor costs and improving inspection efficiency. However, in the field of partial discharge (PD) detection, the frequency band of the drone's motor rotation highly overlaps with that of PD signals, affecting the accuracy of PD detection. Currently, there is no effective method to address the interference of drone noise on PD detection, and no practical drone-based PD detection solution has been found. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a partial discharge detection system and method for unmanned aerial vehicles (UAVs). The front end employs a structure of "microphone array + acoustic absorbing metamaterial" to detect suspicious discharge points, isolating most interference noise from the sensing end. The collected acoustic fingerprint information is transmitted to the back end, where beamforming algorithms are used to further separate noise from the partial discharge signal, and acoustic virtual imaging technology is used to display the partial discharge point on a display screen.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a partial discharge detection system for unmanned aerial vehicles (UAVs), employing the following technical solution:

[0007] A partial discharge detection system for unmanned aerial vehicles (UAVs) includes:

[0008] The partial discharge detection payload module includes a microphone array for collecting acoustic text information, and a soundproof wall disposed around the microphone array to isolate interference noise.

[0009] The voiceprint information processing module is used to process the voiceprint information collected by the microphone array using a beamforming algorithm, suppressing interference information in non-target directions and enhancing the voiceprint information in the target direction.

[0010] Furthermore, a drone partial discharge detection system also includes:

[0011] An image acquisition device used to acquire visible light images of partial discharge signals;

[0012] An acoustic imaging module is used to receive image information from the image acquisition device and voiceprint information processed by the voiceprint information processing module.

[0013] Furthermore, the image acquisition device is positioned in the middle of the microphone array.

[0014] Furthermore, the acoustic imaging module normalizes the sound intensity distribution and takes its logarithm, compressing the value range to generate a sound field cloud map.

[0015] The acoustic field cloud image is transformed to a Cartesian coordinate system. The acoustic cloud image is then superimposed with the visible light image to obtain the partial discharge detection image.

[0016] Furthermore, the microphone array includes multiple microphone elements arranged in an array; the soundproof wall includes multiple frames, within which a thin film is fixed, and a metal sheet is fixed on the thin film within each frame.

[0017] Furthermore, the microphone array is a rectangular array, with multiple microphone elements evenly arranged in the microphone array.

[0018] Furthermore, the frame is an aluminum frame, and the film is a silicone rubber film; the metal sheets fixed on the film in different frames have different masses.

[0019] Furthermore, the voiceprint information processing module generates an excitation matrix containing 0 and 1 elements, and the positions of the matrix elements correspond to the positions of each array element on the microphone array; if an element at a certain position is 0, the microphone array element at that position does not receive a signal, and if the element is 1, it receives a signal, and the microphone array element at that position receives a signal.

[0020] By calculating the different time delays of the noise signal to each microphone array element, the spatial coordinates of the noise signal can be determined, and the noise source can be located, enabling the UAV to have prior location information of the noise.

[0021] After receiving the partial discharge signal, a directional beam is generated by adjusting the excitation amplitude and phase of the microphone array elements, and a null point in the direction of the noise source is generated based on the prior position information.

[0022] Furthermore, if the target device has a partial discharge point, the direction of the discharge point is the direction of the maximum power of the received signal. The direction of the maximum power is used to determine whether the target device has a partial discharge phenomenon.

[0023] To achieve the above objectives, in a second aspect, the present invention also provides a method for detecting partial discharge from unmanned aerial vehicles (UAVs), employing the following technical solution:

[0024] A method for detecting partial discharge in unmanned aerial vehicles (UAVs) employs the UAV partial discharge detection system as described in the first aspect, comprising: firstly using a partial discharge detection payload module to initially isolate noise, and then using an adaptive beamforming method to process the multi-channel microphone signals in the partial discharge detection payload module to suppress interference information in non-target directions and enhance acoustic fingerprint information in the target direction.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] 1. This invention innovatively proposes a partial discharge detection system for unmanned aerial vehicles (UAVs). It constructs a dual noise isolation method at the sensing end and the back end. Soundproof walls are set up around the microphone array to isolate interference noise, thus isolating most of the interference noise at the sensing end. At the same time, the beamforming algorithm in the voiceprint information processing module is used to further separate noise from partial discharge signals at the back end. This avoids the influence of motor rotation noise and partial discharge signal frequency bands being highly overlapping, improves the anti-interference and noise reduction effect on partial discharge signals, achieves the purpose of removing UAV body noise, and ensures detection accuracy.

[0027] 2. This invention innovatively proposes a partial discharge detection load module, which constructs a high-directivity microphone array + acoustic metamaterial mode acquisition unit. Multiple frames on the acoustic metamaterial are fixed with thin films, and metal sheets are fixed on the thin films in each frame. Compared with ordinary sound insulation materials, the load weight and volume are reduced, the directivity of the microphone array is improved, and the interference of the UAV body noise on the acoustic fingerprint information of the target device is avoided, thus ensuring the accuracy of partial discharge detection.

[0028] 3. This invention innovatively proposes a method for detecting partial discharge in unmanned aerial vehicles (UAVs), and constructs a dual noise isolation method. First, a partial discharge detection payload module is used to achieve the purpose of initial noise isolation. Then, an adaptive beamforming method is used to process the signals from multiple microphones, which avoids interference from the UAV's own noise to the detection, suppresses interference signals from non-target directions, and enhances the sound signal from the target direction.

[0029] 4. This invention innovatively proposes a method for partial discharge detection in unmanned aerial vehicles (UAVs), and constructs an analysis method for the detection signal. After generating a sound field cloud map, the sound field cloud map is transformed into a coordinate system, converting the spherical vector to a Cartesian coordinate system. The acoustic cloud map is then superimposed with a visible light image to obtain a partial discharge detection image, which improves the accuracy and intuitive display effect of the partial discharge detection results, and achieves the purpose of visualizing the acoustic signal, making it convenient for inspection workers to use. Attached Figure Description

[0030] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0031] Figure 1 This is a framework diagram of Embodiment 1 of the present invention;

[0032] Figure 2 This is a schematic diagram of the partial discharge detection load module structure in Embodiment 1 of the present invention;

[0033] Among them, 1. camera; 2. microphone; 3. thin-film resonant unit; 4. acoustic absorbing metamaterial. Detailed Implementation

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0035] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0036] Example 1:

[0037] A partial discharge detection system for unmanned aerial vehicles (UAVs) includes a partial discharge detection payload module, an acoustic fingerprint information processing module, an acoustic imaging module, and a client.

[0038] The partial discharge detection payload module is used to acquire partial discharge signals from the target device; the partial discharge detection payload module includes a microphone array and a soundproof wall surrounding the microphone array; the microphone array includes multiple microphone elements arranged in an array; the soundproof wall includes multiple frames, within which a thin film is fixed, and a metal sheet is fixed on the thin film within each frame; specifically, as shown... Figure 2As shown, the partial discharge detection payload module includes a microphone array and a soundproof wall composed of acoustic absorbing metamaterials. The microphone array is used to directionally collect partial discharge data from the target device. Its array elements are arranged in a rectangular array. Based on the concept of sparse arrays, whether each element collects target acoustic signature information depends on the random excitation matrix generated by the signal processing end during actual use. Multiple random array acquisitions are performed on the same device, and the acquisition results are transferred to the information processing module for analysis. The acoustic absorbing metamaterial 4 is a thin-film metamaterial based on a local resonance structure. Compared with ordinary soundproofing materials, it effectively reduces the load weight and volume, improves the directivity of the microphone array, and reduces the interference of UAV noise on the acoustic signature information of the target device.

[0039] In this embodiment, the microphone array is a rectangular array, with multiple microphone elements evenly arranged within it. An image acquisition device is positioned in the center of the microphone array to acquire visible light images of partial discharge signals. The frame can be an aluminum frame, and the thin film can be a silicone rubber film; the mass of the metal sheet fixed to the film varies depending on the frame. Specifically, the partial discharge detection load module includes a soundproof wall composed of the microphone array and acoustic absorbing metamaterial 4. The microphone array is used to directionally acquire partial discharge data from the target device. Its elements are arranged in a rectangular array. Based on the concept of sparse arrays, whether each element acquires target acoustic signature information depends on the random excitation matrix generated by the signal processing end during actual use. Multiple random array acquisitions are performed on the same device, and the acquisition results are transferred to the information processing module for analysis. The acoustic absorbing metamaterial 4 is a thin-film metamaterial based on a local resonance structure. Compared with ordinary soundproofing materials, it effectively reduces the load weight and volume, improves the directivity of the microphone array, and reduces the interference of UAV noise on the acoustic signature information of the target device.

[0040] The voiceprint information processing module is used to process the multi-channel microphone signals collected by the partial discharge detection load module using a beamforming algorithm. Specifically, it processes the voiceprint information collected by the microphone array to suppress interference signals from non-target directions and enhance the sound signal from the target direction. Before collecting the device's voiceprint information, the voiceprint information processing module first generates a centrally symmetric random excitation matrix, which has only values ​​0 or 1. It then feeds each microphone element according to this matrix. The module receives the voiceprint information collected from the microphone array, first calculates the maximum energy of each microphone to obtain the approximate direction of the device's partial discharge source, and then adjusts the excitation matrix according to the array synthesis principle. Finally, it uses a beamforming algorithm to process the multi-channel microphone signals, suppressing interference signals from non-target directions and enhancing the sound signal from the target direction.

[0041] The acoustic imaging module is used to receive image information from the image acquisition device and sound signals processed by the voiceprint information processing module. In this embodiment, the sound signal and the voiceprint signal can be understood as the same type of signal. The image acquisition device can be set as camera 1. Specifically, the acoustic imaging module receives image information transmitted back from camera 1 and sound signals processed by the voiceprint information processing module, performs visualization processing on the received sound signals, obtains the spatial distribution of the sound source based on the microphone array, and uses the color and brightness of the image to represent the strength of the sound signal. It forms an intuitive image in the form of a cloud map and superimposes it with the image information obtained by the camera. The superimposed image is then transmitted to the back end to facilitate the inspection personnel in determining the partial discharge location of the equipment.

[0042] The client is used for displaying detection results, etc. Specifically, according to the needs of the inspection personnel, the client can adjust the orientation of the partial discharge detection load in the parameter adjustment interface to obtain the best detection effect; in the detection result viewing interface, an overlay image of acoustic cloud map and visible light image can be generated, and the intensity of partial discharge can be displayed in the image. Optionally, the spectral density and radiation pattern of the sound source can be displayed.

[0043] In other embodiments, a partial discharge detection system for unmanned aerial vehicles (UAVs) is also disclosed, including a partial discharge detection payload module, an acoustic fingerprint information processing module, an acoustic imaging module, and a partial discharge detection client. The partial discharge detection payload module includes a rectangular microphone array and acoustic absorbing metamaterials, which can directionally collect acoustic signals from target devices. The acoustic fingerprint information processing module can analyze and process the acoustic signals, and the acoustic imaging module visualizes the acoustic signals and displays them on the client.

[0044] The partial discharge detection payload module uses a rectangular microphone array to collect acoustic information. Compared to a spiral array, a rectangular array is more flexible and easier to model and calculate in a Cartesian coordinate system. For a uniform planar microphone array, assuming the sound source signal received by the microphone element located at (x1, y1) is a1s1(t+τ1)+n1(t), then the straight-line distance between the microphone element located at (x2, y2) and the array element is... The sound source signal received at this point can be expressed as s2(t)=a2s1(t+τ2)+n2(t); where τ is the delay of the signal received at this point, which can be expressed as τ2=d cosθ s , where θ s This can be represented as the angle between the sound source and the array element; n(t) is the noise of the signal; and a is the amplitude of the signal. Then the sum of all signals from all channels of the N-channel microphone array can be expressed as... The microphone array's sound signal reception model lays the foundation for the subsequent voiceprint information processing module. In this embodiment, an 8*8 64-microphone array can be used, with the array element spacing being approximately 2cm, similar to the wavelength of the ultrasonic signal, and the entire array size being 20cm*20cm.

[0045] The microphone array is surrounded by a thin-film acoustic absorbing metamaterial based on resonant units. This material consists of a tensioned thin film and metal sheets of varying masses embedded in it. Let the mass of the thin film be m1, and let the thin film act as a spring with an elastic coefficient of k1 and damping of c1. The metal sheets are tightly attached to the thin film, and their mass is m2. When sound pressure from an external sound source acts on the thin film, the thin-film system converts acoustic energy into elastic strain energy, causing a slight displacement of the resonant units. The displacement of the thin film is defined as x1, and the displacement of the metal sheets as x2. The equations of motion for the thin-film metamaterial are obtained through analysis:

[0046]

[0047] Wherein, the film displacement x1(t) = X1e iωt The displacement of the metal sheet is x(t) = X²e iωt External incentive F(t) = F0e i ωt The transfer function between displacement X1 and excitation force F0 is calculated as follows:

[0048]

[0049] The displacement of the thin-film metamaterial system was obtained by analyzing the system using the global method. The transfer function with respect to the excitation force F0 can be expressed as: in For the effective mass of the thin-film metamaterial system, by combining the equations and neglecting the effect of damping on sound insulation, we can obtain: The natural frequency of the interaction between the thin film and the metal sheet According to the effective mass formula, when the incident sound wave frequency is greater than the natural frequency of the thin-film metamaterial, the effective mass is negative. In this case, the thin film and metal sheet will vibrate in the opposite direction to the sound wave, resulting in zero overall average normal displacement, forming a near-rigid plane that performs total internal reflection of the sound wave. In this embodiment, a thin-film acoustic absorbing metamaterial based on resonant units, with a unit cell structure of an aluminum frame + silicone rubber film + embedded metal sheet, can be used, as shown in the figure. The frame width is 1 mm, the silicone rubber film thickness is 1 mm, the area is 4 mm * 4 mm, and the mass block is made of metal with a thickness of 2 mm and a radius of 0.5 mm.

[0050] The voiceprint information processing module mainly performs target localization of partial discharge sound sources and extracts spectral features from multiple microphone signals. Given the statistically non-stationary nature of sound signals, there are currently three main methods for sound source localization: controllable beamforming localization based on maximum output power, localization based on high-resolution spectral estimation, and localization based on arrival delay estimation. Among these, the high-resolution spectral estimation method requires high computational resources and is highly demanding in terms of sound source and noise environment, while the arrival delay estimation method suffers from error propagation amplification and cannot perform multi-source localization. Therefore, this embodiment selects the controllable beamforming localization method based on maximum output power. This method can fully utilize array shape and sound source location information to optimize the beam pattern, reduce power waste, and effectively avoid noise interference from non-main lobe directions by utilizing array pattern nulls.

[0051] Since the noise in UAV partial discharge detection devices mainly originates from the rotation of the UAV motor and the sound generated by the friction between the propeller and the air, its characteristics include a relatively fixed position relative to the partial discharge load, making it easy to obtain prior knowledge of the noise. Furthermore, this noise is additive, incoherent with the partial discharge signal, and its frequency band largely overlaps with the partial discharge signal, while its power is relatively high, making it difficult to distinguish. Therefore, we consider first using acoustic metamaterials to isolate a portion of the noise, then employing an adaptive beamforming method to cancel the noise signal by adjusting the radiation pattern, thereby improving the signal-to-noise ratio. In a high signal-to-noise ratio environment, we then use a controllable beamforming localization method based on maximum output power to identify the partial discharge location.

[0052] The specific signal processing process is as follows: Inspired by the concept of programmable control, the voiceprint information processing module can generate an excitation matrix containing only 0 and 1 elements. The positions of the matrix elements correspond to the array elements on the microphone array, and the microphone array receives signals according to the matrix. If an element at a certain position is 0, the microphone array element at that position does not receive a signal; if the element is 1, it receives a signal. The central symmetry ensures that the microphone array receives sound source signals from each direction with consistency. Random distribution improves detection flexibility. According to the principle of array synthesis, the beam pointing of the array is random. Since there is no partial discharge signal at this time, the microphone array receives noise generated by the UAV. Since the load is close to the UAV propeller motor, it belongs to near-field noise. Considering the use of a simple and computationally inexpensive Time Difference of Arrival (TDOA) algorithm, the spatial coordinates of the noise signal can be calculated by the different time delays of the noise signal arriving at each randomly distributed microphone array element, and the noise source can be located. Due to the symmetry of the UAV motor structure, the microphone array can identify a symmetrical even number of noise sources. The following is a brief derivation of the sound source localization algorithm based on TDOA, using a quadcopter drone equipped with a microphone array as an example. First, the voiceprint processing module generates the array element excitation matrix A. Then, the signal received by the microphone array can be represented as follows: The m-th signal will receive a delay τ. m Then the distance d from the sound source location to the m-th microphone element is... m =vτ m Where v is the speed of sound, and the distance difference between the two microphone array elements can be expressed as d. mn =v(τ) m -τ n Assuming the sound source location is (x) i y i , z i If the distance from the sound source to each microphone is such that the distance can be represented by a hyperbolic equation, then:

[0053]

[0054] The location of the sound source (x) can be solved from this formula. i y i , z i After identifying the noise source location, the UAV possesses prior location information about the noise. When the UAV flies to a fixed waypoint, the partial discharge detection payload is aligned with the target device. The acoustic signature processing module adjusts the excitation matrix of the microphone array. Based on array synthesis theory, it is assumed that there is a uniformly arranged, equally spaced rectangular array on the xoy plane, with an element spacing of d. x d yThe angles between the location of the sound source and the x-axis and y-axis are θ and θ, respectively. The radiation pattern of the microphone array can then be written as: in k is the wavenumber, and A(x, y) is the excitation amplitude of the microphone array element. It can be seen that the main factors affecting the beamform of the microphone array are the excitation amplitude and phase of the microphone array element. By adjusting the excitation amplitude and phase of the microphone array element, a highly directional beam can be generated. Furthermore, based on prior knowledge of noise, null points in the radiation pattern can be intentionally generated in the direction of the noise source, thereby reducing the interference of the UAV's own noise on the partial discharge signal. If the target device has a partial discharge point, then for the microphone array, the direction of the discharge point is the direction of the maximum power of the received signal. The acoustic signature processing module can then determine whether the device has a partial discharge phenomenon based on this direction, calculate the sound field distribution in the space in front based on the time delay and sound pressure of the incoming wave, and transmit the spatial distribution of the sound field to the next-level acoustic imaging module.

[0055] The acoustic imaging module receives the sound field information transmitted from the previous stage acoustic signature information processing module, normalizes the sound intensity distribution and takes the logarithm, compresses the value range, reduces noise interference, and improves the performance of the partial discharge signal. After data processing, an acoustic cloud map is generated, and the sound field intensity is represented by color. Since the microphone array and the visible light camera in the partial discharge detection payload are in a concentric and coaxial position, it is only necessary to perform coordinate transformation on the sound field cloud map, transforming the spherical vector to the Cartesian coordinate system. By superimposing the acoustic cloud map with the visible light image, the partial discharge detection image can be obtained.

[0056] Example 2:

[0057] A method for detecting partial discharge in unmanned aerial vehicles (UAVs) employs the UAV partial discharge detection system described in Example 1, comprising: firstly using a partial discharge detection payload module to initially isolate noise, and then using an adaptive beamforming method to process the multi-channel microphone signals in the partial discharge detection payload module to suppress interference signals in non-target directions and enhance the sound signals in the target direction.

[0058] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A partial discharge detection system for unmanned aerial vehicles (UAVs), characterized in that, include: The partial discharge detection payload module includes a microphone array for collecting acoustic text information, and a soundproof wall disposed around the microphone array to isolate interference noise. The voiceprint information processing module is used to process the voiceprint information collected by the microphone array using a beamforming algorithm, suppressing interference information in non-target directions and enhancing the voiceprint information in the target direction. The voiceprint information processing module generates an excitation matrix containing 0 and 1 elements, and the position of the matrix element corresponds to the position of each array element on the microphone array; if an element at a certain position is 0, the microphone array element at that position does not receive a signal, and if the element is 1, it receives a signal. By calculating the different time delays of the noise signal to each microphone array element, the spatial coordinates of the noise signal can be determined, and the noise source can be located, enabling the UAV to have prior location information of the noise. After receiving the partial discharge signal, a directional beam is generated by adjusting the excitation amplitude and phase of the microphone array elements, and a null point in the direction of the noise source is generated based on the prior position information.

2. The UAV partial discharge detection system as described in claim 1, characterized in that, Also includes: An image acquisition device used to acquire visible light images of partial discharge signals; An acoustic imaging module is used to receive image information from the image acquisition device and voiceprint information processed by the voiceprint information processing module.

3. The UAV partial discharge detection system as described in claim 2, characterized in that, The image acquisition device is positioned in the middle of the microphone array.

4. The UAV partial discharge detection system as described in claim 2, characterized in that, The acoustic imaging module normalizes the sound intensity distribution and takes the logarithm, compresses the value range, and generates a sound field cloud map. The acoustic field cloud image is transformed to a Cartesian coordinate system. The acoustic cloud image is then superimposed with the visible light image to obtain the partial discharge detection image.

5. The UAV partial discharge detection system as described in claim 1, characterized in that, The microphone array includes multiple microphone elements arranged in an array; the soundproof wall includes multiple frames, each frame having a thin film fixed inside it, and each frame having a metal sheet fixed to the thin film.

6. The UAV partial discharge detection system as described in claim 5, characterized in that, The microphone array is a rectangular array, with multiple microphone elements evenly arranged in the microphone array.

7. The UAV partial discharge detection system as described in claim 5, characterized in that, The frame is an aluminum frame, and the film is a silicone rubber film; the metal sheets fixed on the film in different frames have different masses.

8. The UAV partial discharge detection system as described in claim 1, characterized in that, If the target device has a partial discharge point, the direction of the discharge point is the direction of the maximum power of the received signal. The direction of the maximum power is used to determine whether the target device has a partial discharge phenomenon.

9. A method for detecting partial discharge from unmanned aerial vehicles (UAVs), characterized in that, The UAV partial discharge detection system described in any one of claims 1-8 includes: firstly, using a partial discharge detection payload module to initially isolate noise, and then using an adaptive beamforming method to process the multi-channel microphone signals in the partial discharge detection payload module to suppress interference information in non-target directions and enhance acoustic fingerprint information in the target direction.

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