A partial discharge detection method and system based on acousto-optic thermal fusion

Abstract

This invention discloses a partial discharge detection method and system based on acoustic-optical-thermal fusion, wherein the method includes: Step 1, calculating the thermal map of the sound source. I S ; Calculate the infrared thermal phase diagram I T Acquiring optical images I O Step 2: Overlay the acoustic, optical, and thermal images; Step 3: Identify background interference and false noise sources caused by sound wave reflection; Step 4: Update the acoustic and optical overlay image obtained in Step 2 after removing false noise sources based on Step 3. This invention achieves mutual verification of information from array sensors using acoustic, optical, and thermal technologies through adaptive learning of a fusion algorithm. It effectively filters out environmental noise and automatically eliminates reflective false defects, exhibiting extremely low false negative and false positive rates, and providing highly reliable detection results.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment discharge detection, specifically to a partial discharge detection method and system based on acoustic-optical-thermal fusion. Background Technology

[0002] Partial discharge (PD) occurs when the internal insulation of high-voltage electrical equipment (transmission lines, high-voltage switches, surge arresters, high-voltage cabinets, etc.) is inadequate. If this fault is not addressed promptly, it can lead to the eventual breakdown and failure of the insulating medium, causing damage to the electrical equipment. Once electrical equipment is put into operation, it is generally intended to operate energized as much as possible, with the principle of "no power interruption unless absolutely necessary." To detect PD in a timely manner and prevent potential hazards from developing into accidents, there is an urgent need for a method and instrument that can perform online detection without affecting the energized operation of the equipment.

[0003] Partial discharge in high-voltage electrical equipment is accompanied by physical phenomena such as pulsed electromagnetic radiation, low-frequency (tens of kHz) ultrasound, infrared thermal energy, and ultraviolet light, as well as various chemical products. This discharge information contains rich information about the condition of the insulating medium. Currently, there are various methods for detecting partial discharge, mainly divided into two categories: electromagnetic measurement methods and non-electromagnetic measurement methods. Electromagnetic measurement methods mainly include pulsed current methods and ultra-high frequency electromagnetic wave methods. Although these methods are widely used, their practical application results are often less than ideal, mainly because of significant electromagnetic noise interference at the site and difficulty in extracting partial discharge signals. Non-electromagnetic measurement methods mainly include ultrasonic detection methods, infrared detection methods, ultraviolet light detection methods, and chemical detection methods. Their advantages are that they are not affected by electrical signals during the measurement process and have strong anti-interference capabilities. Corresponding detection equipment includes ultrasonic detectors, infrared thermal imagers, ultraviolet imagers, transformer oil analyzers, and detection methods based on light radiation during ionization, excitation, and recombination processes.

[0004] There are also some fusion detection methods, such as the Chinese invention patent application "A method and system for combined acoustic and optical detection of partial discharge in high-voltage cables CN110275094A", which proposes a method that integrates two non-electrical detection technologies, ultrasound and ultraviolet sensing. It performs spectrum analysis on the ultrasound signal and statistical analysis on the ultraviolet pulse signal. However, it does not involve the beamforming and positioning function of the microphone array for the ultrasonic source, the video imaging function of the optical camera, the infrared thermal imaging function, or the fusion and superposition function of acoustic image, optical image, and thermal image.

[0005] Chinese utility model patent application CN212111659U, entitled "Detection device for corona discharge of high-voltage live equipment based on acoustic-optical-thermal combined technology", proposes an acoustic-optical-thermal combined technology, but does not involve the beamforming positioning function of the microphone array for the ultrasonic source, nor does it involve the video imaging function of the optical camera, nor does it involve the fusion and superposition function of acoustic images and optical images.

[0006] Chinese invention patent application "An acoustic imaging method CN112017688A" proposes to fuse sound source intensity distribution map with image information to obtain acoustic imaging image, but it does not involve infrared thermal imaging and fusion overlay function.

[0007] Chinese invention patent application "An acoustic-optical combined wind turbine fault location device and method CN113607447A" proposes a method for fusing and superimposing an acoustic sensor array and an optical camera, but does not involve infrared thermal imaging and fusing and superimposing functions.

[0008] The Chinese invention patent application "A method for intelligent monitoring of abnormal operating status of equipment CN113313146A" proposes a method of transparently superimposing real-time captured background images with sound field cloud maps using a microphone array carrying a camera. This method does not involve infrared thermal imaging and fusion superposition functions, and is mainly deployed on-site for fixed-point monitoring, making it unsuitable for portable inspection.

[0009] For acoustic environments with strong background noise interference and high reverberation (such as in-service power plants and high-voltage transmission and distribution equipment), the acoustic thermal images obtained by the above methods often contain "pseudo-noise sources" introduced by background noise and reflection images of the source of interest at the fault. This leads to a high rate of missed detections and false positives, and the confidence level of the detection results is generally low. It is usually necessary to get as close as possible to the object under inspection and perform multiple operations by changing positions to finally confirm the fault location, resulting in low detection efficiency. Summary of the Invention

[0010] The purpose of this invention is to overcome the shortcomings of traditional methods and provide a partial discharge detection method and system based on acoustic-optical-thermal fusion.

[0011] The first aspect of this invention provides a partial discharge detection method based on acousto-optic-thermal fusion, comprising:

[0012] Step 1, Calculate the sound source heat map I S ; Calculate the infrared thermal phase diagram I T Acquiring optical images I O ;

[0013] Step 2: Overlay sound, light, and thermal images.

[0014] 1) Using optical image I OAs a reference standard, the sound source heat map I S and infrared thermal image I T Projected onto the camera imaging coordinate system

[0015]

[0016] In the formula, and These are the projected thermal image of the sound source and the infrared thermal image, respectively. S and R T These are the corresponding rotation matrices, T S and T T These are the corresponding translation vectors;

[0017] 2) Apply weighted addition to each Perform overlay and fusion;

[0018] Step 3: Identify the sources of false noise caused by background interference and sound wave reflection;

[0019] (1) Obtain the heat map of the sound source using a search algorithm. The set P of pixel locations corresponding to the median maxima S ={p S1 p S2 , ..., p SQ}, where Q is the number of detected sound sources;

[0020] (2) Using a search algorithm, obtain the infrared thermogram. The set P of pixel locations corresponding to the median maxima T ={p T1 p T2 , ..., p TR}, where R is the number of heat sources detected;

[0021] (3) By fusing infrared thermal phase results, pseudo-noise sources can be identified:

[0022] a) For the i-th sound source, calculate the Euclidean distance D between it and the locations of all heat sources. ij :

[0023] D ij =||p Si -p Tj || 2 j = 1, ..., R;

[0024] b) If D ij If ε holds true for all values ​​of j, then the sound source i is determined to be a "pseudo-sound source" caused by background noise or sound wave reflection; where ε is the set error threshold.

[0025] c) For i = 1, ..., Q, repeat steps a) and b) to identify and eliminate all possible spurious sound sources;

[0026] Step 4: After removing false noise sources in Step 3, update the sound and light superimposed image obtained in Step 2.

[0027] Based on the above, calculate the sound source heat map I. S The method includes: acquiring raw audio sound pressure time-domain data x from M channels via microphones, and then performing array signal processing on the sound pressure data x to obtain a sound source heatmap I. S ;

[0028] The array signal processing method includes the following steps:

[0029] 1) Perform an FFT of length N on the M channels of data to obtain a complex matrix X with dimensions M×N;

[0030] 2) Select the frequency range to be detected, select the corresponding data subarray Xs from the complex matrix X, and calculate the covariance matrix R. X ;

[0031] 3) Based on any array form and corresponding frequency range, construct the array manifold vector v(r, ω), which is used as the weight w(r, ω) = v(r, ω) / M for the wave velocity formation algorithm;

[0032] 4) Calculate the sound source intensity distribution S

[0033] S(r, ω) = [w(r, ω)] H R X (ω)w(r,ω)

[0034] In the formula, r represents the position of the corresponding pixel on the sound source surface, ω is the frequency, and H represents the Hermitian transpose of the matrix / vector;

[0035] 5) Process the sound source intensity distribution matrix S to obtain the sound source heat map I. S ;

[0036] The sound source intensity distribution matrix S is formatted so that the minimum value becomes 0, the maximum value becomes 255, and other data are adjusted according to the ratio of the maximum to the minimum.

[0037] After formatting the data to 0-255, the color bar is also divided into 256 parts from left to right; the color of each data point is obtained according to the color proportion, realizing the transformation from the sound source intensity distribution matrix S to the sound source heat map I. S The transformation.

[0038] Based on the above, a 640×512 pixel image was acquired using a thermal infrared camera, and a 1920×1080 pixel image was obtained through bilinear interpolation as the infrared thermal phase image I. T .

[0039] Based on the above, the image obtained by recording or photographing the target under detection using a high-definition camera is used as optical image I. O .

[0040] Based on the above, the search algorithm is as follows: given a two-dimensional function image, calculate its difference, and determine the location and number of local extreme points in the image by searching for inflection points.

[0041] The second aspect of the present invention provides a partial discharge detection system based on acoustic-optical-thermal fusion, comprising an acoustic-optical-thermal array probe and a detection host, wherein the acoustic-optical-thermal array probe comprises a miniature digital microphone array, an optical camera and a thermal infrared camera, which are respectively used to acquire thermal images of sound sources, infrared thermal images and optical images;

[0042] The detection host is communicatively connected to the miniature digital microphone array, the optical camera, and the thermal infrared camera to execute the partial discharge detection method based on acoustic-optical-thermal fusion, thereby forming a distribution map of the sound source and heat source in physical space.

[0043] Based on the above, the miniature digital microphone array, the optical camera, and the thermal infrared camera are integrated on the front of the acousto-optic-thermal array probe, wherein the thermal infrared camera is located at the top, the miniature digital microphone array is located in the middle of the front of the acousto-optic-thermal array probe, and the optical camera is located at the center of the acousto-optic-thermal array probe.

[0044] Based on the above, the miniature digital microphone array is a MEMS miniature digital microphone array with 32 to 256 channels and a frequency response range of 20Hz to 100kHz.

[0045] Based on the above, the detection host is equipped with a human-machine interface display.

[0046] This invention has outstanding substantive features and significant progress, specifically:

[0047] 1. This invention uses an array sensor based on three technologies—acoustic, optical, and thermal—to adaptively learn through a fusion algorithm, achieving mutual verification among the three technologies. This effectively filters out environmental noise and automatically eliminates reflective false defects, resulting in extremely low false detection and false judgment rates, and extremely high confidence in the detection results.

[0048] 2. The system structure of this invention has a high degree of integration and the whole machine is small and portable. The acoustic-optical-thermal array probe is separated from the detection host, which makes it convenient for the acoustic-optical-thermal array probe to reach some narrow spaces for detection work. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the system structure of Embodiment 2 of the present invention.

[0050] Figure 2 This is a system principle block diagram of Embodiment 2 of the present invention.

[0051] Figure 3 This is a schematic diagram of the acousto-optic-thermal array probe in Embodiment 2 of the present invention.

[0052] In the diagram: 1. Detection host; 2. Acoustic-optical-thermal array probe; 3. Connecting cable; 21. Thermal infrared camera; 22. Miniature digital microphone array; 23. Optical camera. Detailed Implementation

[0053] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading the present invention, any modifications of the present invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0054] Example 1

[0055] This embodiment provides a partial discharge detection method based on acousto-optic-thermal fusion, including:

[0056] Step 1, Calculate the sound source heat map I S ;

[0057] Calculation of sound source heat map I S The method includes: acquiring raw audio sound pressure time-domain data x from M channels via microphones, and then performing array signal processing on the sound pressure data x to obtain a sound source heatmap I. S ;

[0058] The array signal processing method includes the following steps:

[0059] 1) Perform an FFT of length N on the M channels of data to obtain a complex matrix X with dimensions M×N;

[0060] 2) Select the frequency range to be detected, select the corresponding data subarray Xs from the complex matrix X, and calculate the covariance matrix R. X ;

[0061] 3) Based on any array form and corresponding frequency range, construct the array manifold vector v(r,ω), which is used as the weight w(r,ω)=v(r,ω) / M for the wave speed formation algorithm;

[0062] 4) Calculate the sound source intensity distribution S

[0063] S(r, ω) = [w(r, ω)] H R x (ω)w(r,ω)

[0064] In the formula, r represents the position of the corresponding pixel on the sound source surface, ω is the frequency, and H represents the Hermitian transpose of the matrix / vector;

[0065] 5) Process the sound source intensity distribution matrix S to obtain the sound source heat map I. S ;

[0066] The sound source intensity distribution matrix S is formatted so that the minimum value becomes 0, the maximum value becomes 255, and other data are adjusted according to the ratio of the maximum to the minimum.

[0067] After formatting the data to 0-255, the color bar is also divided into 256 parts from left to right; the color of each data point is obtained according to the color proportion, realizing the transformation from the sound source intensity distribution matrix S to the sound source heat map I. S The transformation.

[0068] Calculation of Infrared Thermal Phase Diagram I T An image of 640×512 pixels was acquired using a thermal infrared camera. A 1920×1080 pixel image was obtained through bilinear interpolation and used as the infrared thermal image I. T .

[0069] Acquiring optical images I O The image obtained by recording or photographing the target (insulator, leakage valve, etc.) using a high-definition camera is used as the optical image. O .

[0070] Step 2: Overlay sound, light, and thermal images.

[0071] 1) Using optical image I O As a reference standard, the sound source heat map I S and infrared thermal image I T Projected onto the camera imaging coordinate system

[0072]

[0073] In the formula, and These are the projected thermal image of the sound source and the infrared thermal image, respectively. S and RT These are the corresponding rotation matrices, T S and T T These are the corresponding translation vectors;

[0074] 2) Apply weighted addition to each Superimpose and fuse.

[0075] Step 3: Identify the sources of false noise caused by background interference and sound wave reflection;

[0076] The search algorithm is as follows: given a two-dimensional function graph (e.g., ... or ), calculate its difference, and by searching for inflection points, the location and number of local extrema in the image can be determined;

[0077] (1) Using the search algorithm, obtain the heat map of the sound source. The set P of pixel locations corresponding to the median maxima S ={p S1 p S2 , ..., p SQ}, where Q is the number of detected sound sources;

[0078] (2) Using the search algorithm, obtain the infrared thermal phase image. The set P of pixel locations corresponding to the median maxima T ={p T1 p T2 , ..., p TR}, where R is the number of heat sources detected;

[0079] (3) By fusing infrared thermal phase results, pseudo-noise sources can be identified:

[0080] a) For the i-th sound source, calculate the Euclidean distance D between it and the locations of all heat sources. ij :

[0081] D ij =||p Si -p Tj || 2 j = 1, ..., R;

[0082] b) If D ij If ε holds true for all values ​​of j, then the sound source i is determined to be a "pseudo-sound source" caused by background noise or sound wave reflection; where ε is the set error threshold.

[0083] c) For i = 1, ..., Q, repeat steps a) and b) to identify and eliminate all possible spurious sources.

[0084] Step 4: After removing false noise sources in Step 3, update the sound and light superimposed image obtained in Step 2.

[0085] Example 2

[0086] like Figure 1-3 As shown, this embodiment provides a partial discharge detection system based on acoustic-optical-thermal fusion, including an acoustic-optical-thermal array probe 2 and a detection host 1. The acoustic-optical-thermal array probe 2 includes a miniature digital microphone array 22, an optical camera 23, and a thermal infrared camera 21, which are used to acquire thermal images of the sound source, infrared thermal images, and optical images, respectively. The miniature digital microphone array 22, the optical camera 23, and the thermal infrared camera 21 are integrated on the front of the acoustic-optical-thermal array probe 2, wherein the thermal infrared camera 21 is located at the top, the miniature digital microphone array 22 is located in the middle of the front of the acoustic-optical-thermal array probe 2, and the optical camera 23 is located at the center of the acoustic-optical-thermal array probe 2. The miniature digital microphone array 22 is a 32-256 channel MEMS miniature digital microphone array with a frequency response range of 20Hz-100kHz. The optical camera 23 is a 1920×1080 pixel optical camera. The thermal infrared camera 21 is a 640×512 pixel thermal infrared camera. The detection host 1 is equipped with a human-machine interface display.

[0087] The detection host 1 is connected to the miniature digital microphone array, the optical camera and the thermal infrared camera via the connecting cable 3. The collected signals are transmitted to the detection host 1 after signal conditioning and data packaging to execute the partial discharge detection method based on acoustic-optical-thermal fusion as described in Embodiment 1, and to form a distribution state diagram of the sound source and heat source in physical space.

[0088] The detection host's software system uses a high-resolution beamforming algorithm to calculate a sound intensity distribution image and an infrared thermal image. These are then fused and overlaid with visible light video images to form the final distribution map of the sound and heat sources in physical space. Through adaptive learning of the fusion algorithm, the software system compares and verifies the detection results of the three technologies, automatically filters out environmental noise and eliminates reflective false defects, revealing the actual location of partial discharge in electrical equipment.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A partial discharge detection method based on acoustic-optical-thermal fusion, characterized in that, include: Step 1, Calculate the sound source heat map I S ; Calculation of Infrared Thermal Phase Diagram I T Acquiring optical images I O ; Step 2: Overlay sound, light, and thermal images. 1) Using optical image I O As a reference standard, the sound source heat map I S and infrared thermal image I T Projected onto the camera imaging coordinate system In the formula, and These are the projected thermal image of the sound source and the infrared thermal image, respectively. S and R T These are the corresponding rotation matrices, T S and T T These are the corresponding translation vectors; 2) Apply weighted addition to each Perform overlay and fusion; Step 3: Identify the sources of false noise caused by background interference and sound wave reflection; (1) Obtain the heat map of the sound source using a search algorithm. The set P of pixel locations corresponding to the median maxima S ={p S1 ,p S2 ,…,p SQ }, where Q is the number of detected sound sources; (2) Using a search algorithm, obtain the infrared thermogram. The set P of pixel locations corresponding to the median maxima T ={p T1 ,p T2 ,…,p TR }, where R is the number of heat sources detected; (3) By fusing infrared thermal phase results, pseudo-noise sources can be identified: a) For the i-th sound source, calculate the Euclidean distance D between it and the locations of all heat sources. ij : D ij =||p Si -p Tj || 2 ,j=1,…,R; b) If D ij If ε is true for all values ​​of j, then the sound source i is determined to be a "pseudo-sound source" caused by background noise or sound wave reflection; where ε is the set error threshold. c) For i = 1, ..., Q, repeat steps a) and b) to identify and eliminate all possible spurious sound sources; Step 4: After removing false noise sources in Step 3, update the sound and light superimposed image obtained in Step 2.

2. The partial discharge detection method based on acoustic-optical-thermal fusion according to claim 1, characterized in that, Calculation of sound source heat map I S The method includes: acquiring raw audio sound pressure time-domain data x from M channels via microphones, and then performing array signal processing on the sound pressure data x to obtain a sound source heatmap I. S ; The array signal processing method includes the following steps: 1) Perform an FFT of length N on the M channels of data to obtain a complex matrix X with dimensions M×N; 2) Select the frequency range to be detected, select the corresponding data subarray Xs from the complex matrix X, and calculate the covariance matrix R. X ; 3) Based on any array form and corresponding frequency range, construct the array manifold vector v(r,ω), which is used as the weight w(r,ω)=v(r,ω) / M for the wave speed formation algorithm; 4) Calculate the sound source intensity distribution S S(r,ω)=[w(r,ω)] H R X (ω)w(r,ω) In the formula, r represents the position of the corresponding pixel on the sound source surface, ω is the frequency, and H represents the Hermitian transpose of the matrix / vector; 5) Process the sound source intensity distribution matrix S to obtain the sound source heat map I. S ; The sound source intensity distribution matrix S is formatted so that the minimum value becomes 0, the maximum value becomes 255, and other data are adjusted according to the ratio of the maximum to the minimum. After formatting the data to 0-255, the color bar is also divided into 256 parts from left to right; the color of each data point is obtained according to the color proportion, realizing the transformation from the sound source intensity distribution matrix S to the sound source heat map I. S The transformation.

3. The partial discharge detection method based on acousto-optic-thermal fusion according to claim 1, characterized in that: A 640×512 pixel image was acquired using a thermal infrared camera. A 1920×1080 pixel image was obtained through bilinear interpolation and used as the infrared thermal phase image I. T .

4. The partial discharge detection method based on acousto-optic-thermal fusion according to claim 1, characterized in that: The image obtained by recording or photographing the target using a high-definition camera is used as the optical image I. O .

5. The partial discharge detection method based on acoustic-optical-thermal fusion according to claim 1, characterized in that, The search algorithm is as follows: Given a two-dimensional function graph, calculate its difference and determine the location and number of local extrema in the graph by searching for inflection points.

6. A partial discharge detection system based on acoustic-optical-thermal fusion, characterized in that, The device includes an acoustic-optical-thermal array probe and a detection host. The acoustic-optical-thermal array probe includes a miniature digital microphone array, an optical camera, and a thermal infrared camera, which are used to acquire thermal images of the sound source, infrared thermal images, and optical images, respectively. The detection host is communicatively connected to the miniature digital microphone array, the optical camera, and the thermal infrared camera, respectively, to execute the partial discharge detection method based on acoustic-optical-thermal fusion as described in any one of claims 1-5, and to form a distribution map of the sound source and heat source in physical space.

7. The partial discharge detection system based on acoustic-optical-thermal fusion according to claim 6, characterized in that: The miniature digital microphone array, the optical camera, and the thermal infrared camera are integrated on the front of the acousto-optic-thermal array probe. The thermal infrared camera is located at the top, the miniature digital microphone array is located in the middle of the front of the acousto-optic-thermal array probe, and the optical camera is located at the center of the acousto-optic-thermal array probe.

8. The partial discharge detection system based on acoustic-optical-thermal fusion according to claim 6, characterized in that: The miniature digital microphone array is a 32-256 channel MEMS miniature digital microphone array with a frequency response range of 20Hz-100kHz.

9. The partial discharge detection system based on acoustic-optical-thermal fusion according to claim 6, characterized in that: The detection host is equipped with a human-machine interface display.

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