Partial discharge ultrasonic imaging method based on DAMAS2 joint algorithm

Abstract

The invention discloses a partial discharge ultrasonic imaging method based on a DAMAS2 joint algorithm, and the method can obtain the distance between an array and a focusing plane and solve an acoustic power diagram of a specified frequency band, thereby achieving the imaging of an insulation defect part. When a partial discharge phenomenon of the power equipment is monitored, the array system transmits an acquired image signal and an array element signal to a PC terminal; the distance between the array plane and the focusing plane is obtained through a monocular distance measurement method; a potential sound source focus point of a specified frequency band is screened out through a DAS algorithm, and then a sub-region with the focus point as the center is solved through a DAMAS2 algorithm; and sensing fusion is carried out on the images acquired by the array central camera, so that the insulation defect part of the power equipment is positioned. Compared with a traditional DAMAS2 algorithm, the DAMAS2 joint algorithm provided by the invention has the advantages that the applicability of acoustic imaging is improved, and the operation cost is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of power equipment fault detection, and in particular relates to a partial discharge ultrasonic imaging method based on a DAMAS2 joint algorithm. Background Art

[0002] Failure of power equipment may lead to local power outages or accidents such as personnel safety, resulting in unpredictable property losses. Therefore, the safe and reliable operation of power equipment is given great attention. Insulation failure is one of the most frequent failures of power equipment, which is mainly caused by local discharge of insulation defects in power equipment. When the local discharge of power equipment is serious, it is particularly important to use array positioning method to determine the defect location and repair it in time to reduce insulation failure. The detection frequency of UHF signal is extremely high, and the cost of its sensor is relatively high; the propagation speed of electromagnetic waves is much higher than that of ultrasonic waves, and its propagation attenuation is seriously not conducive to the realization of multi-element defect positioning. Therefore, the use of acoustic array positioning method can obtain higher positioning accuracy.

[0003] Beamforming can achieve acoustic imaging. The traditional delay-and-sum beamforming DAS algorithm has fast calculation speed, but has disadvantages such as low spatial resolution and wide sidelobes. The deconvolution beamforming algorithm can reduce the impact of spatial resolution and sidelobes. It constructs an equation between the point spread function, the traditional beamforming output, and the acoustic power of each point on the focal plane, and iterates to find the acoustic power map of the focal plane. Common beamforming algorithms include DAMAS, DAMAS2, NNLS, FISTA, etc. However, due to the number of iterations and the complexity of the point spread matrix, the time cost of this type of algorithm is much higher than that of traditional beamforming. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention proposes a local discharge ultrasonic imaging method based on the DAMAS2 joint algorithm. Compared with the DAMAS2 algorithm, the algorithm can solve the distance from the array to the focal plane, and its time cost is greatly controlled, which can realize ultrasonic imaging of local discharge.

[0005] The present invention is implemented by the following technical solutions: A local discharge ultrasonic imaging method based on DAMAS2 joint algorithm comprises the following steps:

[0006] S1, collect image signals through the camera in the center of the array, and solve the distance z from the array plane to the focus plane 0 ;

[0007] S2, the 64-channel acoustic signal is obtained by FFT to obtain the ultrasonic signal of the specified frequency band. The DAS algorithm is first used to obtain the conventional beamforming output, and then the DAMAS2 algorithm is used to solve the corresponding sound power of the sub-area that meets the threshold, and the sound power of other areas is assigned to zero value;

[0008] S3, performs sensor fusion of acoustic power and image signals to achieve ultrasonic imaging of partial discharge in the distribution cabinet.

[0009] Because in S1, in previous beamforming algorithms, the distance z from the array to the focal plane is known by default. 0 However, it is difficult to predict this distance in advance in actual acoustic imaging applications.

[0010] The distance z from the array plane to the focusing plane in S1 0 Calculate z by monocular ranging method 0 , the formula is as follows:

[0011]

[0012] Among them, H is the actual height of the power equipment, in meters; h is the height of the power equipment in the image, in pixels; f is the focal length of the camera, in pixels (which can be solved by Zhang Zhengyou calibration method).

[0013] In S2, the array has a total of 64 eight-arm MEMS sensors, and the 64-channel acoustic signals received by the array elements are transmitted to the PC through another independent data line. The PC performs fast Fourier transform on the 64-channel acoustic signals and images the acoustic signals in the central frequency band of partial discharge, which can greatly reduce the interference of acoustic signals in other frequency bands and improve the accuracy of partial discharge positioning.

[0014] The conventional beamforming output of S2 using the DAS algorithm is N x and N y are the number of focal points on the x-axis and y-axis of the focal plane, respectively, then the focal point r n =[x,y,z 0 ](x, y, z 0 is the focal point n The beamforming output of the three-dimensional coordinate value of is:

[0015]

[0016] in The focus point r n The steering vector, T represents the transpose; [f d ,f h ] is the scanning frequency; C(f) is the cross-spectral matrix of the acoustic signal at frequency f; m represents the array element, M is the number of array elements; n represents the variable (x, y) of the focal plane, taking 1, 2, …, N = N x ×N y .

[0017] The sub-regions of S2 that meet the threshold are determined according to the following conditions:

[0018] Judgment condition A: Focus point r n The beamforming output b(r n ) is greater than a threshold value γ, γ is set to 0.6 to 0.8 times the maximum value of b; if condition A is not met, the next focus point is judged; if condition A is met, condition B is judged;

[0019] Judgment condition B: b(r n ) is it based on r n The sub-region R is the center n Maximum output value, sub-region R n is defined as follows:

[0020]

[0021] Among them, 2α+1 and 2β+1 are the scales of the sub-regions, usually N x 、N y 1 / 5 to 1 / 100;

[0022] If condition B is not met, the focal point of the maximum output value is used as the center to judge condition B; if condition B is met, the DAMAS2 algorithm is used to analyze the sub-area R n To solve.

[0023] The DAMAS2 algorithm is an extension of the DAMAS algorithm. This algorithm assumes that the point spread function is spatially invariant and depends only on the acoustic point source r. t With focus r n The relative positions between them have nothing to do with their specific spatial positions.

[0024] The S2 adopts the DAMAS2 algorithm to establish the convolution equation:

[0025]

[0026] Among them, x(r n ) is the focal point r n The sound power, psf(r n ) is the point spread function of the point, and “*” represents convolution.

[0027] The beneficial effects of the present invention are as follows:

[0028] (1) The present invention uses the image signal obtained by the camera in the center of the array to solve the distance from the array to the focal plane, so that the positioning of the two-dimensional array in the three-dimensional space can be realized in the subsequent acoustic imaging algorithm.

[0029] (2) The present invention first uses the DAS algorithm to screen out the focal points that meet the requirements, and then uses the non-DAMAS2 algorithm to solve its sub-area, which significantly reduces the calculation time cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the implementation of the present invention or the existing technical solutions, the following briefly introduces the drawings required to be used in the embodiments or the description of the prior art.

[0031] Figure 1 It is the overall flow chart of DAMAS2 joint algorithm;

[0032] Figure 2 This is a diagram of a 64-element eight-arm MEMS acoustic sensor array;

[0033] Figure 3 Flow chart for calculating sound power output. DETAILED DESCRIPTION

[0034] The following is a clear and complete description of the technical solutions in the embodiments of the invention in conjunction with the drawings in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention, not all of the embodiments. Based on the embodiments of the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0035] like Figure 1 As shown, the main steps of the method provided by the present invention are as follows:

[0036] (1) The image obtained by the central camera can be solved by the monocular signal ranging method to obtain z 0 , calculate z by monocular ranging method 0 , the formula is as follows:

[0037]

[0038] Among them, H is the actual height of the power equipment, in meters; h is the height of the power equipment in the image, in pixels; f is the focal length of the camera, in pixels (which can be solved by Zhang Zhengyou calibration method).

[0039] In step (1), the 64-element eight-arm MEMS array is as follows Figure 1 As shown in Figure 1, compared with conventional arrays, this array has a narrower main lobe bandwidth, higher resolution and lower side lobes. The image obtained by the central camera of the array is solved by the monocular ranging method to obtain z 0 =2m.

[0040] (2) The 64-channel acoustic signal is obtained through FFT to obtain the ultrasonic signal of the partial discharge center frequency band. The DAS algorithm is first used to obtain the conventional beamforming output, and then the DAMAS2 algorithm is used to solve the sub-area that meets the threshold, and finally the acoustic power output of the specified frequency band is obtained.

[0041] Step (2-1) The sampling rate of the array is 192kHz, which can meet the needs of collecting acoustic signals below 80kHz. Since the ultrasonic frequency components generated by partial discharge of power equipment are extremely rich, they are mainly concentrated around 40kHz. Therefore, this embodiment mainly images acoustic signals with a frequency range of 30kHz to 50kHz.

[0042] Step (2-2) solves the conventional beamforming output by formula (b): N x and N y are the number of focal points on the x-axis and y-axis of the focal plane, respectively, then the focal point r n =[x,y,z 0 ](x, y, z 0 is the focal point n The beamforming output of the three-dimensional coordinate value of is:

[0043]

[0044] in The focus point r n The steering vector, T represents the transpose; [f d ,f h ] is the scanning frequency; C(f) is the cross-spectral matrix of the acoustic signal at frequency f; m represents the array element, M is the number of array elements; n represents the variable (x, y) of the focal plane, taking 1, 2, ..., N = N x ×N y .

[0045] Step (2-3) Focus point r n The beamforming output b(r n ) is greater than the threshold γ, γ is set to 0.8 times the maximum value of b. If condition A is not met, the next focus point is judged; if condition A is met, condition B is judged. Judgment condition B: b(r n ) is it based on r n The sub-region R is the center n Maximum output value, sub-region R n The definition of is shown in formula (c):

[0046]

[0047] Among them, 2α+1 and 2β+1 are the scales of the sub-regions, usually N x 、Ny 1 / 5 to 1 / 100;

[0048] If condition B is not met, the focus point of the maximum output value is used as the center to judge condition B; if condition B is met, the next statement is executed;

[0049] Step (2-4) is for the sub-region R that meets the conditions n The DAMAS2 algorithm is used to solve the local sound power output, and the sound power in other areas is assigned zero value:

[0050]

[0051] Among them, x(r n ) is the focal point r n The sound power, psf(r n ) is the point spread function of the point, and “*” represents convolution.

[0052] The output of beamforming can be expressed as the convolution of the sound source distribution and the array spatial transfer invariant point spread function. The frequency domain convolution is converted into the beam domain product through discrete spatial Fourier transform for acceleration. The matrix formation is expressed as follows:

[0053]

[0054] Among them, F and F -1 Respectively represent two-dimensional Fourier transform and its inverse transform, "*" represents convolution, Represents the Hadamard product. The specific steps of the DAMAS2 algorithm are as follows:

[0055] a. Calculate the sub-region R n The spatial transfer invariant point propagation function matrix A;

[0056] b. Calculate sub-region R n The wave number domain point propagation function A w : Perform a fast Fourier transform on A, and then use the zero boundary to generate the zero-filled A w ;

[0057] c. Use zero boundary to generate zero-filled sub-region R n The wavenumber domain beamforming output b w (R n );

[0058] d. Calculate the constant a = ∑∑A;

[0059] e. Assign an initial value x to the sound power (0) (R n )=0;

[0060] f. Iteration process:

[0061]

[0062] Where i represents the number of iterations.

[0063] (3) Sensor fusion of acoustic power output and image signals.

[0064] Thereby, ultrasonic imaging of the insulation defect part of the power equipment causing partial discharge is realized. Compared with the DAMAS and DAMAS2 algorithms, the operation speed is greatly improved. It is proved that the partial discharge ultrasonic imaging method based on the DAMAS2 combined algorithm proposed in the present invention is feasible.

[0065] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be within the protection scope of the present invention.

Claims

1. A local discharge ultrasonic imaging method based on DAMAS2 joint algorithm, characterized in that The following steps are involved: S1, collect image signals through the camera in the center of the array and solve the distance z0 from the array plane to the focus plane; S2, the acoustic signal is obtained by FFT to obtain the ultrasonic signal of the specified frequency band. The DAS algorithm is first used to obtain the conventional beamforming output, and then the DAMAS2 algorithm is used to solve the corresponding sound power for the sub-areas that meet the threshold, and the sound power of other areas is assigned to zero value; S3, performs sensing fusion of acoustic power and image signals to achieve ultrasonic imaging of partial discharge in the distribution cabinet.

2. The partial discharge ultrasonic imaging method based on the DAMAS2 joint algorithm according to claim 1, characterized in that: The distance z0 from the array plane to the focusing plane in S1 is calculated by a monocular ranging method, and the formula is as follows: Among them, H is the actual height of the power equipment, in meters; h is the height of the power equipment in the image, in pixels; f is the focal length of the camera, in pixels.

3. The partial discharge ultrasonic imaging method based on the DAMAS2 joint algorithm according to claim 1, characterized in that: The conventional beamforming output of S2 using the DAS algorithm is N x and N y are the number of focal points on the x-axis and y-axis of the focal plane, respectively, then the focal point r n =[x,y,z0](x,y,z0 are the focal point r n The beamforming output of the three-dimensional coordinate value of is: in The focus point r n The steering vector, T represents the transpose; [f d ,f h ] is the scanning frequency; C(f) is the cross-spectral matrix of the acoustic signal at frequency f; m represents the array element, M is the number of array elements; n represents the variable (x, y) of the focal plane, taking 1, 2, ..., N = N x ×N y .

4. The partial discharge ultrasonic imaging method based on the DAMAS2 joint algorithm according to claim 1, characterized in that: The sub-regions of S2 that meet the threshold are determined according to the following conditions: Judgment condition A: Focus point r n The beamforming output b(r n ) is greater than a threshold value γ, γ is set to 0.6 to 0.8 times the maximum value of b; if condition A is not met, the next focus point is judged; if condition A is met, condition B is judged; Judgment condition B: b(r n ) is it based on r n The sub-region R is the center n Maximum output value, sub-region R n is defined as follows: Among them, 2α+1 and 2β+1 are the scales of the sub-regions, usually N x 、N y 1 / 5 to 1 / 100; If condition B is not met, the focal point of the maximum output value is used as the center to judge condition B; if condition B is met, the DAMAS2 algorithm is used to analyze the sub-area R n To solve.

5. The partial discharge ultrasonic imaging method based on the DAMAS2 joint algorithm according to claim 1, characterized in that: The S2 adopts the DAMAS2 algorithm to establish the convolution equation: Among them, x(r n ) is the focal point r n The sound power, psf(r n ) is the point spread function of the point, and * represents convolution.

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