Method and system for detecting acetylene gas production rate of gas relay of oil immersed transformer based on light scattering method

Abstract

The invention relates to the technical field of oil-immersed transformer state monitoring, in particular to an oil-immersed transformer gas relay acetylene gas production rate detection method and system based on a light scattering method. Laser signals emitted by the light source unit are incident to an observation window on one side of the gas relay, the sensor unit captures scattered light intensity signals at different angles, and the processing unit calculates bubble particle size distribution according to the scattered light intensity signals according to the Mie scattering principle; and the machine vision unit shoots a bubble group in the gas relay, and monitors a bubble rising rate through a matching window correlation method in the processing unit, so that a corresponding gas production rate is obtained by combining the bubble particle size distribution and the bubble rising rate. Based on a light scattering method and a high-speed camera shooting technology, the dynamic change of the bubbles can be accurately monitored.

Description

Technical Field

[0001] The invention relates to the technical field of oil-immersed transformer state monitoring, and in particular to a method and system for detecting acetylene gas production rate of a gas relay of an oil-immersed transformer based on a light scattering method. Background Art

[0002] In the power system, the operating status of oil-immersed transformers is directly related to the stability and safety of the power grid. During long-term operation, transformers will experience problems such as overheating, partial discharge, and insulation aging. The internal insulating oil and insulating materials will decompose and generate a certain amount of gas. The generation and accumulation of these gases reflect the health status of the transformer. Therefore, monitoring the generation rate of gas inside the transformer is a key indicator for evaluating the status of the transformer. Traditional gas relay monitoring is mainly based on changes in gas accumulation. Although it can detect faults in a timely manner, it is difficult to accurately monitor the generation rate of gas, which makes early warning and diagnosis of faults difficult.

[0003] In recent years, the application of optical detection technology in transformer condition monitoring has gradually attracted attention. Compared with traditional monitoring methods, detection technology based on optical sensing can detect small changes in gas generation rate at the early stage of fault, thus providing earlier warning.

[0004] The invention patent with publication number CN119514141A discloses a method and system for simulating bubble movement in transformer oil considering multi-physical field coupling. The method establishes a multi-physical field model coupling electric field, thermal field and flow field, solves parameters through multiple physical field models, and thus obtains the bubble movement process in transformer oil. This detection method requires the construction of multiple models, which is relatively complicated and cannot directly obtain the gas production rate. Summary of the invention

[0005] Purpose of the invention: In order to solve the above technical problems, the present invention provides a method for detecting the acetylene gas production rate of an oil-immersed transformer gas relay based on a light scattering method, which solves the problem that the gas generation rate cannot be accurately monitored and the monitoring accuracy of the gas relay is low. The present invention also provides a system for detecting the acetylene gas production rate of an oil-immersed transformer gas relay based on a light scattering method.

[0006] Technical solution: To achieve the above objectives, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides a method for detecting the acetylene gas production rate of an oil-immersed transformer gas relay based on a light scattering method, the method comprising: Polystyrene particles with known particle size distribution were doped into transformer oil and introduced into a gas relay; Determine the incident position of the light source unit; the laser signal emitted by the light source unit is incident on the observation window on one side in the gas relay, and the sensor unit processes the scattered light on the observation window on the other side in the gas relay, so as to capture the scattered light intensity signals at different angles, and the processing unit calculates the bubble size distribution according to the Mie scattering principle based on the scattered light intensity signals; The machine vision unit takes pictures of the bubble group in the gas relay, and monitors the bubble rising rate through the matching window correlation method in the processing unit, so as to obtain the corresponding gas production rate by combining the bubble size distribution and the bubble rising rate. The matching window correlation method first selects the bubble group image taken by the machine vision unit to calculate the velocity field from a coarse grid to a fine grid, and then tracks and measures the velocity of a single bubble after meeting the resolution requirements.

[0007] Furthermore, it includes: At least two piezoelectric ultrasonic sensors are arranged on each observation window. The piezoelectric ultrasonic sensors receive the sound waves generated by the bubbles and send them to the processing unit. The processing unit calculates the positions of each bubble according to the time when the sound waves generated by the bubbles arrive by using the TDOA principle, so as to realize the three-dimensional target positioning of the bubble group, and the sensor unit adjusts its position according to the result of the three-dimensional target positioning.

[0008] Furthermore, it includes: The sensor unit includes a CCD detector. The CCD detector adjusts its position according to the result of the three-dimensional positioning, including: the center of the CCD detector is aligned with the result of the three-dimensional target positioning.

[0009] Furthermore, it includes: The processing unit calculates the bubble size distribution according to the Mie scattering principle for the scattered light intensity signals, including: The processing unit performs circular segmentation on the light intensity distribution regions corresponding to the scattered light intensity signals at different angles captured by the CCD detector; Obtain the specific angle range represented by each part according to the number of circular segmentation parts N, and obtain the light intensity distribution signal under this part according to the scattered light intensity signal of each part. The light intensity distribution signal is the scattered light intensity signal of the currently captured part minus the background signal, and the background signal is the light intensity signal corresponding to the case without bubbles; Combine multiple light intensity distribution signals to form a light intensity distribution column vector E, and its dimension is expressed as .

[0010] Furthermore, it includes: The processing unit calculates the bubble size distribution according to the Mie scattering principle for the scattered light intensity signals, and further includes: According to the Mie scattering theory, the light intensity coefficient matrix T caused by the scattered light of bubbles is calculated. The light intensity coefficient matrix includes the light intensity distribution information of the scattered light of bubbles with different particle sizes at different angles, which is non-reversible, and the corresponding dimension is expressed as ; According to the relational expression Solve the particle size distribution matrix , and the method used for solving is: using the Tikhonov iterative algorithm to iteratively solve the initial particle size distribution matrix until the convergence condition is met, so as to obtain the final particle size distribution matrix , and the elements in the particle size distribution matrix are the proportions of bubbles corresponding to different particle sizes.

[0011] Furthermore, it includes: The machine vision unit takes pictures of the bubble group in the gas relay, and monitors the bubble rising rate by the method of matching window correlation. The method of matching window correlation includes: Convert multiple continuously taken pictures into grayscale images, subtract the background image without gas flow from the grayscale images to obtain a grayscale image with only bubble information, and perform noise reduction on it; Taking the target bubble group in the image as the center, select a rectangular window , select four windows B, C, D, and E of the same size with the four corners of the current window as the centers, and calculate the grayscale distribution matrices corresponding to the windows B, C, D, and E respectively. The grayscale distribution matrix is a matrix formed by extracting the grayscale values of each pixel block in the corresponding window; Perform sliding matching on the grayscale distribution matrices of the four windows B, C, D, and E in the current frame and the matrices of the same-size regions near the window in the next frame respectively, so as to find the window with the highest cross-correlation degree with the local window in the current frame in the next frame of the picture. At this time, the position of the cross-correlation peak is the coordinate offset of the current window. The same-size regions near the window are slid in four directions of up, down, left, and right with the theoretical maximum displacement as the initial search radius and one pixel point as the step size; Obtain the speeds of the corresponding windows according to the coordinate offsets of the windows B, C, D, and E, and use the speeds of the four windows B, C, D, and E as the speeds of the four corners of the window A; Taking the maximum speed of the bubble in the oil as the speed threshold, if any one or more of the speeds of the four corners of the obtained window A are greater than the speed threshold, discard it, expand the size of the window and recalculate the corresponding speed, otherwise, perform the next fine-grid speed measurement.

[0012] Furthermore, it includes: The method steps corresponding to the fine-grid speed measurement include: In the rectangular window A, a rectangular window smaller than window A is selected with the target bubble as the center , and the rectangular window includes at most one bubble; Denote the size corresponding to the fine grid as , and the corresponding center coordinates as , then the normalized coordinates of the fine grid in the rectangular window A are expressed as: ; Calculate the weights corresponding to the four corners of window A according to the normalized coordinates, so as to obtain the velocity of the current fine grid according to the velocities of the four corners of window A; Use the obtained fine grid velocity to predict the corresponding fine grid displacement, use the obtained displacement as the search radius, and use one pixel point as the step size to make the rectangular window make position offsets up, down, left, and right, and find the offset window with the highest matching degree with the rectangular window according to the peak value of the cross-correlation function after quadratic fitting, and obtain the rectangular window and the contour lengths of the corresponding bubbles in the window with the highest matching degree with it. If the contour length change rate is greater than 10% or the fine grid velocity is greater than the corresponding threshold, reselect the target bubble for velocity measurement; If there are more than one bubble in the fine grid, further reduce the size of the fine grid for velocity measurement until a single bubble is located for velocity measurement.

[0013] Further, it includes: The matching window correlation method further includes: Track and measure a certain number of bubbles in the field of view, obtain the corresponding velocity distribution, and calculate the average velocity of the corresponding bubbles as the velocity of the bubble population , expressed as: ; where, is the number of bubbles being tracked.

[0014] Further, it includes: The obtaining the corresponding gas production rate by combining the bubble size distribution and the bubble rising rate includes: Calculate the average bubble diameter using the weighted average method, expressed as: ; where, m is the number of particle size bins, is the particle size of the bubbles in the j th bin, is the proportion of bubbles with a particle size of , which is obtained from the particle size distribution matrix ; Calculate the gas production rate: ; where N is the total number of bubbles in the field of view.

[0015] On the other hand, the present invention also provides an acetylene gas production rate detection system for an oil-immersed transformer based on the light scattering method. The system includes: a gas relay, a light source unit, a sensor unit, a machine vision unit, and a processing unit; Dope polystyrene particles with a known particle size distribution into the transformer oil and introduce them into the gas relay; Determine the incident position of the light source unit and make the laser signal emitted by the light source unit incident on the observation window on one side in the gas relay; The sensor unit processes the scattered light at the observation window on the other side in the gas relay, thereby capturing the scattered light intensity signals at different angles. The processing unit calculates the bubble particle size distribution based on the Mie scattering principle for the scattered light intensity signals; The machine vision unit takes pictures of the bubble group in the gas relay and monitors the bubble rising rate through the matching window correlation method in the processing unit. Thus, the corresponding gas production rate is obtained by combining the bubble particle size distribution and the bubble rising rate. The matching window correlation method first selects the image of the bubble group captured by the machine vision unit to calculate the velocity field from a coarse grid to a fine grid, and then tracks and measures the velocity of a single bubble after meeting the resolution requirements.

[0016] Finally, the present invention also provides a storage medium containing computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the above-mentioned method.

[0017] Compared with the prior art, the present application has the following beneficial effects: The present invention connects the machine vision unit, the sensor unit, and the light source unit to the gas relay and performs data processing. That is, by utilizing the non-contact and high sensitivity of the optical sensing technology, the method can still work stably under high load and harsh environments; The light source unit of the present invention is mainly a laser. When the laser passes through the bubble group, bubbles with different particle sizes have different scattering abilities for the laser. By using the Mie scattering principle to analyze the scattered signals at different angles, the particle size distribution of the bubbles can be accurately calculated, improving the measurement accuracy of the bubble particle size distribution. At the same time, by recording the movement trajectory and rising speed of the bubbles through the machine vision unit and combining the calculation of the particle size distribution, the gas generation rate can be accurately deduced, thereby improving the detection accuracy of the bubble rising rate. Therefore, the present invention adopts the light scattering method and high-speed imaging technology to achieve precise monitoring of the dynamic changes of bubbles.

[0018] The matching window correlation method adopted by the present invention can obtain a velocity field distribution with lower resolution through a progressive search strategy from coarse to fine. After gradually performing sliding matching on the fine grid and calculating the cross-correlation coefficient, finally, it locates to a single bubble for velocity measurement. This method improves the spatial resolution while reducing the computational complexity.

[0019] At least two piezoelectric ultrasonic sensors are arranged on each observation window of the gas relay of the present invention. The piezoelectric ultrasonic sensors receive the sound waves generated by the bubbles and send them to the processing unit. The processing unit calculates the positions of the bubbles according to the arrival time of the sound waves generated by the bubbles using the TDOA principle, thereby realizing the three-dimensional target positioning of the bubble group. The sensor unit adjusts the position according to the result of the three-dimensional target positioning. This method not only makes the position of the sensor more accurate, but also uses the sound signal as a trigger source, enabling the optical system to start only when the bubble occurs, avoiding the misalignment between the sampling window and the dynamic time sequence of the bubble, and improving the accuracy of subsequent detection. Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0021] Figure 1 It is a flowchart of the method for detecting the acetylene gas production rate of the oil-immersed transformer gas relay based on the light scattering method according to the embodiment of the present invention; Figure 2 It is a schematic structural diagram of the device for detecting the acetylene gas production rate of the oil-immersed transformer gas relay based on the light scattering method according to the embodiment of the present invention; Figure 3 It is a schematic layout diagram of the piezoelectric ultrasonic sensors according to the embodiment of the present invention; Figure 4 It is a schematic diagram of a relationship structure between the fine grid and the coarse grid according to the embodiment of the present invention; The drawings include: machine vision unit 1, light source unit 2, gas relay 3, sensor unit 4, and processing unit 5. Detailed Embodiments

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0023] Embodiment 1: As Figure 1 shown, this embodiment provides a method for detecting the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method. The method includes the following steps: S1 Dope polystyrene particles with a known particle size distribution into the transformer oil and introduce them into the gas relay 3.

[0024] In view of the difficulty in generating bubbles with a stable particle size distribution to verify the reliability of the device, this embodiment uses polystyrene particles with a known particle size distribution for experiments to verify the accuracy of the system in measuring the particle size and its distribution; different gradients of gas flow rates are used to conduct gas injection experiments on the gas relay 3 to simulate the gas production rates under different fault conditions.

[0025] S2 Determine the incident position of the light source unit 2. The laser signal emitted by the light source unit 2 is incident on the observation window on one side in the gas relay 3, and the sensor unit 4 processes the scattered light on the observation window on the other side in the gas relay 3, thereby capturing the scattered light intensity signals at different angles. The processing unit 5 calculates the bubble particle size distribution based on the Mie scattering principle for the scattered light intensity signals.

[0026] In this embodiment, the light source unit 2 includes a laser and a laser beam expander. The laser is arranged between the adjusting rod and the heavy gas dry reed contact of the gas relay 3. The laser preferably uses a wavelength of 1531 nm and a power of 10 mW, and the laser power is adjustable. In this embodiment, the gas relay 3 is provided with an observation window that allows the light beam to pass through, and its interior is in an oil-filled state. The gas relay 3 includes an oil inlet and an oil outlet channel, and is connected to the oil-immersed transformer through the oil inlet and oil outlet channels, thereby allowing the oil circuit to circulate and the bubbles to be transported. The sensor unit 4 in this embodiment includes a CCD detector, which can collect the scattered light intensity at different angles.

[0027] S3 The machine vision unit 1 takes pictures of the bubble group in the gas relay 3 and monitors the bubble rising rate through the matching window correlation method in the processing unit 5, so as to obtain the corresponding gas production rate by combining the bubble particle size distribution and the bubble rising rate. The matching window correlation method first selects the bubble group image taken by the machine vision unit 1 to calculate the velocity field from a coarse grid to a fine grid, and then tracks and measures the velocity of a single bubble after meeting the resolution requirements.

[0028] In this embodiment, the machine vision unit 1 includes a high-speed camera, which is used to take pictures of the bubble motion images and transmit them to the processing unit 5. The processing unit 5 conducts analysis and calculations to obtain the measured value of the gas production rate and compares it with the theoretical value to verify the accuracy of the method. The processing unit 5 in this embodiment can be a host computer.

[0029] In summary, the method in this embodiment is implemented based on an acetylene gas production rate detection device for oil-immersed transformers using the light scattering method. The structural relationship among the machine vision unit 1, light source unit 2, gas relay 3, sensor unit 4, and processing unit 5 involved in this device is as Figure 2 shown. Specifically, the laser emitted by the light source unit 2 is incident on the bubble group in the gas relay 3. Bubbles with different particle sizes have different scattering abilities for the laser. The sensor unit 4 processes the scattered light and captures the scattered light intensity signals at different angles; analyzes and processes the scattered light intensities at different angles of the sensor unit 4 to calculate the bubble particle size distribution; uses the machine vision unit 1 to photograph the bubble group in the gas relay 3, and the processing unit 5 tracks the bubbles through continuous photography, monitors the bubble rising rate, and calculates the gas production rate by combining the bubble particle size distribution and the bubble rising rate.

[0030] In this embodiment, the light source unit 2 emits a laser with a known wavelength. After beam expansion, it is incident on the bubble group. The sensor unit 4 focuses the scattered light and selectively enhances the scattered light of the incident frequency, and then receives the scattered light intensities at multiple angles. The machine vision unit 1 is used to track the bubbles and detect the bubble rising rate. When the processing unit 5 calculates the gas production rate, first, based on the Mie scattering principle, the bubble particle size distribution is calculated according to the light intensity signals at different angles, and then the bubble rising rate is obtained from the image measured by the machine vision unit 1. The gas production rate is calculated by combining the two results.

[0031] Therefore, the present invention realizes real-time monitoring and rapid response because the optical detection method has extremely high sensitivity and can respond at the initial stage of gas generation to timely detect potential faults.

[0032] In this embodiment, in order to obtain a more accurate position of the CCD detector and to solve the following problems: the non-triggered optical system needs to sample periodically, such as scanning once every 10 ms. If bubbles appear during the interval between two scans, they cannot be captured, and a single optical probe has a limited field of view angle, usually within 120°. Bubbles at the edge of the oil cavity are easily missed. In this embodiment, at least two piezoelectric ultrasonic sensors are provided on the observation window on each side of the gas relay 3. The piezoelectric ultrasonic sensors receive the sound waves generated by the bubbles and send them to the processing unit 5, as Figure 3 shown. The left side is a side window of the gas relay 3, which is the incident side of the laser. Two piezoelectric ultrasonic sensors at different positions are provided therein, that is, the small squares in the figure. The position of the triangle is the position where the laser is input. At least two piezoelectric ultrasonic sensors are also provided on the right window. In this embodiment, the positions and numbers of the two piezoelectric ultrasonic sensors are not limited, but in order to obtain three-dimensional coordinates, at least two piezoelectric ultrasonic sensors are required on each side.

[0033] Since the amplitude of the bubble acoustic wave is usually 5-10 times higher than the background noise, the acoustic signal trigger threshold is set to 5 times the background, and the signal frequency trigger range is set to 50-200 kHz. When the piezoelectric ultrasonic sensor receives a signal within the above frequency range and with an amplitude exceeding the trigger threshold, it triggers the optical system to detect. The sensor only serves as a receiving end to detect the acoustic waves generated by the bubbles themselves, such as rupture, oscillation, etc. The acoustic waves generated by the bubbles range from 35-120 kHz and propagate in the form of spherical waves. Four sensors calculate the three-dimensional coordinates of the bubbles by recording the absolute time difference of the acoustic wave arrival and combining the sound speed, about 1480 m / s in oil. In this embodiment, the three-dimensional coordinates of the bubbles mainly use the TDOA principle to calculate the positions of each bubble through the arrival time of the acoustic waves generated by the bubbles, and then realize the three-dimensional target positioning of the bubble group. The sensor unit 4 adjusts its position according to the result of the three-dimensional target positioning. That is, in this embodiment, a piezoelectric ultrasonic sensor is integrated on the gas relay 3, and the acoustic signal is used as the trigger source. For example, bubble oscillation, bubble rupture, bubble fragmentation, bubble coalescence, etc. will generate acoustic waves of 50-200 kHz to make the optical system start only when the bubbles occur, avoiding the misalignment between the sampling window and the bubble dynamic time sequence. And in this embodiment, a total of four piezoelectric ultrasonic sensors are integrated. The positions of the bubbles are calculated by the arrival time of the acoustic waves, with an accuracy of ±0.1 mm, realizing the three-dimensional positioning of the bubble group. According to the result of the three-dimensional positioning, that is, the position of the bubbles, the CCD is rotated or translated.

[0034] In this embodiment, the CCD detector adjusts its position according to the result of the three-dimensional positioning, including: the signal line of the piezoelectric ultrasonic sensor is connected to the CCD detector, and the center of the CCD detector is adjusted to be aligned with the result of the three-dimensional target positioning.

[0035] In this embodiment, in the specific step S2, the processing unit 5 calculates the bubble particle size distribution based on the Mie scattering principle for the scattered light intensity signal, as Figure 4 shown, specifically including the following steps: S21 The processing unit 5 performs circular segmentation on the light intensity distribution regions corresponding to the scattered light intensity signals captured by the CCD detector at different angles.

[0036] In this embodiment, the light intensity distribution region captured by the sensor unit 4 is circularly segmented. In this embodiment, no limitation is made on the light intensity distribution region, as long as circular segmentation is performed. In this embodiment, the distribution region is equally divided into 30 parts, and each part represents a specific angular range. The background signal is subtracted from the captured light intensity signal to obtain the light intensity distribution column vector at different angles.

[0037] S22 Obtain the specific angular range represented by each part according to the number of parts N of the circular segmentation, and obtain the light intensity distribution signal for each part according to the scattered light intensity signal of each part. The light intensity distribution signal is the scattered light intensity signal of the currently captured part minus the background signal, and the background signal is the light intensity signal corresponding to the case without bubbles.

[0038] In this embodiment, the light intensity distribution signal includes the light intensity signal with bubbles and the light intensity signal without bubbles. Specifically, since bubbles have a scattering effect on light, which also causes the light intensity passing through the solution to decrease, the light intensity signal with bubbles generally has an obvious difference in value from the light intensity signal without bubbles.

[0039] S23 Combine the light intensity distribution signals of multiple parts to form a light intensity distribution column vector E, and its dimension is expressed as .

[0040] S24 Calculate the light intensity coefficient matrix T caused by the scattered light of bubbles according to the Mie scattering theory. The light intensity coefficient matrix includes the light intensity distribution information of the scattered light of bubbles with different particle sizes at different angles, and it is non-invertible. The corresponding dimension is expressed as ; S25 According to the relational expression S25 According to the relational expression Solve the particle size distribution matrix W. The method used for solving is: use the Tikhonov iterative algorithm to iteratively solve the initial particle size distribution matrix until the convergence condition is satisfied, so as to obtain the final particle size distribution matrix , and the elements in the particle size distribution matrix are the proportions of bubbles corresponding to different particle sizes.

[0041] In this embodiment, first give an initial value W, and then perform iteration until the convergence condition is satisfied, so as to obtain the particle size distribution matrix W. Among them, is the proportion of bubbles with different particle sizes, and M is the total number of different levels of particle sizes.

[0042] In this embodiment, the corresponding calculation results are shown in Table 1, and the particle size distribution errors are all within 10%, thus verifying the accuracy of the monitoring method.

[0043] Table 1 True value, measured value and percentage error of particle size distribution

[0044] In this embodiment, step S3 specifically includes: S31 Introduce nitrogen with different velocity gradients to simulate the gas production rate under different fault conditions, and first measure the particle size and distribution of bubbles by the above method.

[0045] The S32 machine vision unit 1 captures the movement of bubbles at a rate of 1000 frames per second, stores the resulting image sequence in the camera memory, and then transmits it to the processing unit 5 for visualization processing and image analysis. The matching window correlation method is used to process two consecutive captured images, achieving the counting of bubbles in the image and the tracking measurement of the speeds of some bubbles. The main steps are as follows: By processing two consecutive captured images, the counting of bubbles in the image and the tracking measurement of the speeds of some bubbles are achieved.

[0046] This method is based on the classical particle image velocimetry (PIV) technique: by taking two images within a defined time interval, tracking the trajectories of individual bubbles, thereby determining their displacements from one image to the next, and then calculating the speeds.

[0047] The traditional PIV technique cannot obtain an accurate bubble velocity field mainly because: (1) it becomes difficult to capture individual bubbles when the bubble overlap increases; (2) the movement of bubbles is complex; (3) bubbles have unstable deformations, which causes irregular flickering in the captured images. Therefore, based on the above problems, the matching window correlation method described in this embodiment covers the following key steps: S321 processes multiple consecutively captured pictures, converts the pictures to grayscale mode, and the output image has 256 gray levels, ranging from 0 (black) to 255 (white), and prepares a grayscale version of the background image. Subtracting the background image from the continuous flow image can enhance the image contrast. This process can eliminate any information unrelated to the gas phase, or color variations, thus obtaining a grayscale image with only bubble information. The continuous flow image in this embodiment is an image containing liquid and gas, and the background image corresponds to a snapshot with stagnant liquid and no gas flow.

[0048] Apply a median filter to the above image, and each pixel of the image is replaced by the median of its adjacent pixels, obtaining a bubble grayscale image with reduced noise.

[0049] S322 repeats the above correlation calculation from a large query area to a small query area to reduce the computational burden and increase the spatial data output density in the final calculation result. In multi-scale bubble velocity analysis, the cross-correlation algorithm needs to find the displacement with the best-matched gray-scale distribution, that is, the highest peak of the cross-correlation function. If the search range is too large, it may misjudge the secondary peak. The previous velocity field provides the macroscopic flow trend, while a finer grid requires a more accurate displacement prediction.

[0050] In this embodiment, initially a relatively sparse velocity distribution is obtained through a large query region. The result of the previous time is used to guide the next search, and the second processing is performed with a smaller query region. This process is repeated, continuously refining the sampling grid while reducing the query window size. Finally, the final data with good spatial resolution can be extracted.

[0051] In a preferred solution of this embodiment, the specific implementation method of step S322 is as follows: Step 1: Taking the target bubble group in the image as the center, select a rectangular window , taking the four corners of the current window as the centers, select four windows B, C, D, and E of the same size, and calculate the gray-scale distribution matrices corresponding to windows B, C, D, and E respectively. The gray-scale distribution matrix in this embodiment is a matrix formed after extracting the gray-scale values of each pixel block of the corresponding window.

[0052] In this embodiment, taking the target bubble group as the center, select a rectangular window A, such as a window of 64×64. Taking the four corners of the current window as the centers, select four windows (B, C, D, and E) of 64×64, and extract the gray-scale distribution matrices corresponding to the four windows.

[0053] Step 2: Slide and match the gray-scale distribution matrices of these four windows B, C, D, and E in the current frame with the matrices of the same-size regions near the windows in the next frame respectively, so as to find the window with the highest cross-correlation degree with the local window in the current frame in the next-frame picture. At this time, the position of the cross-correlation peak is the coordinate offset of the current window. The same-size regions near the window are the initial search radius of the theoretical maximum displacement, and the sliding is performed in the four directions of up, down, left, and right with a step of one pixel.

[0054] In this embodiment, the cross-correlation peak of the local gray-scale distribution is first found, and the cross-correlation is defined as follows: Among them, f and g represent gray-scale, and the subscripts i and j are the corresponding digital image positions. M and N are the sizes of the query regions. The gray-scale used in the formula needs to subtract the local average gray-scale of each query region to evaluate the unique similarity of the two images. The maximum correlation coefficient reflects the confidence level of the match. The cross-correlation peak of the same bubble should be significantly higher than other positions.

[0055] The two images in this formula are obtained based on sliding matching. In this embodiment, the initial search radius is set according to the theoretical maximum displacement. For example, the search radius is selected as ±20, and windows B, C, D, and E are slid in four directions: up, down, left, and right with a step size of one pixel, that is, using an integer pixel step size. A larger search window is defined in the corresponding area of the next frame image, for example: 20 pixels in each of the up, down, left, and right directions, that is, 104×104. In the second frame image, the window with the same position as the window to be matched in the first frame is slid pixel by pixel within the search window, so as to find the window with the highest cross-correlation degree with the local window in the first frame in the second frame image. The position of the obtained cross-correlation peak is the coordinate offset of the current window, denoted as .

[0056] Step 3: Obtain the velocities of the corresponding windows according to the coordinate offsets of windows B, C, D, and E, and use the velocities of the four windows B, C, D, and E as the velocities of the four corners of window A.

[0057] Step 4: Use the maximum velocity of the bubble in the oil as the velocity threshold. If any one or more of the velocities of the four corners of the obtained window A are greater than the velocity threshold, discard it and recalculate the corresponding velocity after expanding the size of the window. Otherwise, proceed to the next fine-grid velocity measurement.

[0058] In this embodiment, since the displacement of the previous step can be used as an estimate of the next higher resolution level, the outlier detection standard should be more stringent than that of the single-step analysis to prevent the new estimate from possibly deviating from the true value. The maximum velocity of the bubble in the oil can be obtained through the fluid properties, and this is used as the velocity threshold to discard the outliers and expand the window for recalculation. Although expanding the window will reduce the resolution, more information can be obtained.

[0059] In the above method, since there are multiple bubbles in the rectangular window, the rate of a single bubble still cannot be obtained. If the accuracy of measuring the rising velocity of the bubble or measuring smaller bubbles is to be improved, after obtaining the velocities of the four corners of the coarse grid A, the coarse grid can be divided into finer grids, the displacement of the fine grids can be predicted, and after performing sliding matching and calculating the cross-correlation coefficient for the fine grids, a velocity field distribution with lower resolution can be obtained, and then it can be located to a single bubble for velocity measurement.

[0060] Step 5: In the rectangular window A, select a rectangular window smaller than window A with the target bubble as the center , the rectangular window includes at most one bubble. For example, 32×32 is a window. Use bilinear interpolation to map the coarse-scale velocity field to the fine grid, predict the current displacement, and its core idea is to calculate the velocity of the target point by weighted averaging the velocities of four known pixel points in the large window A, and the weights are determined by the horizontal and vertical distances between the target point and the adjacent points.

[0061] Record the size corresponding to the fine grid as , and the corresponding center coordinates as . Then the normalized coordinates of the fine grid in the rectangular window A are expressed as: Calculate the weights corresponding to the four corners of window A based on the normalized coordinates, and thus obtain the velocity of the current fine grid according to the velocities corresponding to the four corners of window A.

[0062] In this embodiment, each coarse grid cell, , corresponds to four fine grid cells, . The center coordinates of the coarse grid cell are ( x c , y c ), and the center coordinates of the fine grid cell are ( x f , y f ). The normalized coordinates in the coarse grid are: Example: As Figure 4 shown, if the fine grid is located directly to the lower right of the center of the coarse grid cell, in this embodiment, the origin is located at the upper left corner, then x = 0.75, y = 0.75. Calculate the weights of the four coarse grid points according to the normalized coordinates: Therefore, the velocity of the fine grid point is calculated by weighting the velocities of the coarse grid points. The weight of the lower right corner (w 11 ) is the largest, and the velocity of the fine grid is: .

[0063] Step 6: Use the obtained velocity of the fine grid to predict the corresponding displacement of the fine grid. Take the obtained displacement as the search radius and one pixel as the step size to make the rectangular window make position offsets up, down, left, and right. Find the offset window with the highest matching degree with the rectangular window according to the peak value of the cross-correlation function after quadratic fitting, and obtain the contour lengths of the corresponding bubbles in the rectangular window and the window with the highest matching degree with it. If the change rate of the contour length is greater than 10% or the velocity of the fine grid is greater than the corresponding threshold, reselect the target bubble for velocity measurement; Otherwise, if there are more than one bubble in the fine grid, further reduce the size of the fine grid for velocity measurement until a single bubble is located for velocity measurement.

[0064] In this embodiment, the calculated fine-grid velocity is used to predict the fine-grid displacement, and this displacement data is used to offset the interrogation windows from each other. The interrogation windows are 32×32. A quadratic fit is performed on the peak of the cross-correlation function. After finding the window with the highest matching degree, the bubble contour lengths of the two windows before and after are measured, which can be directly read from the image. If the change rate of the contour length is greater than 10% or the measured velocity is greater than the corresponding threshold, and this corresponding threshold is the theoretical maximum value of the velocity, then a target bubble is reselected for velocity measurement.

[0065] Moreover, in this embodiment, the selection of the window size can be adjusted adaptively, and the number of iterations may not be just one fine grid as in the embodiment. If a single bubble cannot be detected, the size of the fine grid still needs to be reduced, and on this basis, further cycling is performed until a single bubble is located for velocity measurement. On this basis, multiple bubbles in the field of view are tracked and measured.

[0066] Step 7: Track and measure a certain number of bubbles in the field of view, obtain the corresponding velocity distribution, and calculate the average velocity of the corresponding bubbles as the velocity of the bubble population , expressed as: where is the number of bubbles being tracked.

[0067] In this embodiment, half of the bubbles in the field of view are tracked according to the above steps to obtain the velocity distribution, and the average velocity is calculated as the velocity of the bubble population.

[0068] S323 Count the bubbles in the field of view: Count the number of bubbles and calculate the average value in the image containing only bubble information to obtain the total number of bubbles in the field of view.

[0069] In this embodiment, the weighted average method is used to calculate the average bubble diameter, expressed as: where m is the number of particle size bins, is the particle size of the j th bin of bubbles, is the proportion of bubbles with a particle size of , which is obtained from the particle size distribution matrix ; Calculate the gas production rate: where N is the total number of bubbles in the field of view.

[0070] The comparison relationship between the measured value and the theoretical value of the gas production rate obtained by calculation is shown in Table 2. It can be seen from Table 2 that the percentage errors of the measured values of the gas production rate are all less than 15%.

[0071] Table 2 Measured values, theoretical values and percentage errors of gas production rate

[0072] In summary, a method for detecting the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method according to the present invention includes, based on the Mie scattering principle, calculating the bubble size distribution according to the scattered light intensity after the laser is incident on the bubble group, using a high-speed camera to record the moving images of the bubble group to monitor the bubble rising speed, and calculating the gas production rate by combining the bubble size and the bubble rising rate. This method uses laser optical sensing technology to monitor the bubble movement speed and distribution in real time, so as to effectively calculate the gas production rate.

[0073] Embodiment 2: The present invention also provides a detection system for the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method. The system includes: a gas relay 3, a light source unit 2, a sensor unit 4, a machine vision unit 1, and a processing unit 5; Dope polystyrene particles with a known particle size distribution into the transformer oil and introduce them into the gas relay 3; Determine the incident position of the light source unit 2 and make the laser signal emitted by the light source unit 2 incident on the observation window on one side in the gas relay 3; The sensor unit 4 processes the scattered light on the observation window on the other side in the gas relay 3, thereby capturing the scattered light intensity signals at different angles, and the processing unit 5 calculates the bubble size distribution according to the Mie scattering principle for the scattered light intensity signals; The machine vision unit 1 takes pictures of the bubble group in the gas relay 3 and monitors the bubble rising rate through the matching window correlation method in the processing unit 5, so as to obtain the corresponding gas production rate by combining the bubble size distribution and the bubble rising rate. The matching window correlation method first selects the bubble group image taken by the machine vision unit 1 to calculate the velocity field from a coarse grid to a fine grid, and then tracks and measures the velocity of a single bubble after meeting the resolution requirements.

[0074] Other technical features of the detection system for the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method in this embodiment are similar to the corresponding detection method for the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method, and will not be elaborated here.

[0075] Finally, the present invention also provides a storage medium containing computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the above-mentioned method.

[0076] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0077] In the present invention, unless otherwise clearly specified and defined, terms such as "mounted", "connected", "coupled", "fixed", etc. shall be construed in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0078] In the present invention, unless otherwise clearly specified and defined, the first feature being "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "beneath" and "underneath" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.

[0079] In the description of this specification, the description with reference to terms such as "an embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0080] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where functions may be executed in a manner other than shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0081] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing a logical function, and can be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0082] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0083] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above-described embodiment methods can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0084] In addition, in each of the embodiments of the present invention, the functional units can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above-integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above-integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0085] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for detecting the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method, characterized in that, The method includes: Doping polystyrene particles with a known particle size distribution into transformer oil and introducing them into a gas relay; Determining the incident position of a light source unit, and the laser signal emitted by the light source unit is incident on an observation window on one side in the gas relay. A sensor unit processes the scattered light on the observation window on the other side in the gas relay, so as to capture scattered light intensity signals at different angles. A processing unit calculates the bubble size distribution according to the Mie scattering principle based on the scattered light intensity signals; A machine vision unit takes pictures of the bubble group in the gas relay, and monitors the bubble rising rate through the matching window correlation method in the processing unit. Thus, the corresponding gas production rate is obtained by combining the bubble size distribution and the bubble rising rate. The matching window correlation method first selects the image of the bubble group captured by the machine vision unit to calculate the velocity field from a coarse grid to a fine grid, and then tracks and measures the velocity of a single bubble after meeting the resolution requirement.

2. The method for detecting the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method according to claim 1, wherein At least two piezoelectric ultrasonic sensors are arranged on each observation window. The piezoelectric ultrasonic sensors receive the sound waves generated by the bubbles and send them to the processing unit. The processing unit calculates the positions of the bubbles according to the time when the sound waves generated by the bubbles arrive by using the TDOA principle, and further realizes the three-dimensional target positioning of the bubble group. The sensor unit adjusts its position according to the result of the three-dimensional target positioning.

3. The method for detecting the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method according to claim 2, wherein The sensor unit includes a CCD detector. The CCD detector adjusts its position according to the result of the three-dimensional positioning, including: the center of the CCD detector is aligned with the result of the three-dimensional target positioning.

4. The method for detecting the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method according to claim 3, wherein, The processing unit calculating the bubble size distribution according to the Mie scattering principle for the scattered light intensity signals includes: The processing unit performs circular segmentation on the light intensity distribution region corresponding to the scattered light intensity signals at different angles captured by the CCD detector; According to the number of circular segmentation parts N, a specific angular range represented by each part is obtained, and according to the scattered light intensity signal of each part, the light intensity distribution signal of this part is obtained. The light intensity distribution signal is the scattered light intensity signal of the currently captured part minus the background signal, and the background signal is the light intensity signal corresponding to the case without bubbles; Combine multiple light intensity distribution signals to form a light intensity distribution column vector E, whose dimension is expressed as .

5. The method for detecting the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method according to claim 4, wherein, The processing unit calculating the bubble size distribution according to the Mie scattering principle for the scattered light intensity signals further includes: According to Mie scattering theory, the light intensity coefficient matrix T caused by the scattered light of bubbles is calculated. The light intensity coefficient matrix includes the light intensity distribution information of the scattered light of bubbles with different particle sizes at different angles, which is non-reversible, and the corresponding dimension is expressed as ; According to the relational expression Solve the particle size distribution matrix , and the method used for solving is: using the Tikhonov iterative algorithm to iteratively solve the initial particle size distribution matrix until the convergence condition is satisfied, so as to obtain the final particle size distribution matrix , and the elements in the particle size distribution matrix are the proportions of bubbles corresponding to different particle sizes.

6. The method for detecting the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method according to claim 1, wherein When the machine vision unit takes pictures of the bubble group in the gas relay and monitors the bubble rising rate through the matching window correlation method, the matching window correlation method includes: Converting multiple continuously captured pictures into grayscale images, subtracting the background image without gas flow from the grayscale images to obtain grayscale images with only bubble information, and performing noise reduction on them; Select a rectangular window centered on the target bubble group in the image , select four windows B, C, D, and E of the same size centered on the four corners of the current window, and calculate the gray-scale distribution matrices corresponding to windows B, C, D, and E respectively. The gray-scale distribution matrix is a matrix formed by extracting the gray-scale values of each pixel block in the corresponding window; Perform sliding matching on the grayscale distribution matrices of these four windows B, C, D, and E in the current frame and the matrices of the same-sized regions near these windows in the next frame respectively, so as to find the window in the next frame of the picture with the highest cross-correlation degree with the local window in the current frame. At this time, the position of the cross-correlation peak is the coordinate offset of the current window. The same-sized regions near the window are slid in four directions of up, down, left, and right with the theoretical maximum displacement as the initial search radius and one pixel as the step size. Obtain the velocities of the corresponding windows according to the coordinate offsets of windows B, C, D, and E, and use the velocities of the four windows B, C, D, and E as the velocities of the four corners of window A. Use the maximum velocity of the bubbles in the oil as the velocity threshold. If any one or more of the velocities of the four corners of window A obtained are greater than the velocity threshold, discard them and recalculate the corresponding velocities after expanding the size of the window. Otherwise, perform the next step of fine-grid velocity measurement.

7. The method for detecting the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method according to claim 6, characterized in that, The method steps corresponding to the fine-grid velocity measurement include: In the rectangular window A, a rectangular window smaller than the window A is selected with the target bubble as the center , the rectangular window includes at most one bubble; Denote the size corresponding to the fine grid as , and the corresponding center coordinates as . Then the normalized coordinates of the fine grid in the rectangular window A are expressed as: ; Calculate the weights corresponding to the four corners of window A according to the normalized coordinates, so as to obtain the velocity of the current fine grid based on the velocities corresponding to the four corners of window A; Predict the corresponding fine-grid displacement using the obtained fine-grid velocity. Taking the obtained displacement as the search radius and one pixel as the step size, make the rectangular window make position offsets up, down, left, and right, and find the offset window with the highest matching degree with the rectangular window according to the peak of the cross-correlation function after quadratic fitting and obtain the rectangular window and the contour length of the corresponding bubble in the window with the highest matching degree with it. If the change rate of the contour length is greater than 10% or the fine-grid velocity is greater than the corresponding threshold, reselect the target bubble for velocity measurement; If there are more than one bubble in the current fine grid, further reduce the size of the current fine grid for velocity measurement until a single bubble is located and the velocity is measured.

8. The method for detecting the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method according to claim 7, wherein The matching window correlation method further includes: Track and measure a certain number of bubbles in the field of view, obtain the corresponding velocity distribution, and calculate the average velocity of the corresponding bubbles as the velocity of the bubble population , expressed as: ; where is the number of bubbles being tracked 9. The method for detecting the acetylene gas production rate of the gas relay of an oil-immersed transformer based on the light scattering method according to claim 7, wherein, The obtaining of the corresponding gas production rate by combining the bubble size distribution and the bubble rising rate includes: The average bubble diameter is calculated by the weighted average method and expressed as: ; Among them, m is the number of particle size bins, is the particle size of the j th bin of bubbles, is the proportion of bubbles with a particle size of , which is obtained from the particle size distribution matrix ; Calculate the gas production rate: ; Where N is the total number of bubbles in the field of view.

10. An acetylene gas production rate detection system for oil-immersed transformer gas relays based on the light scattering method, characterized in that, The system includes: a gas relay, a light source unit, a sensor unit, a machine vision unit, and a processing unit; Dope polystyrene particles with a known particle size distribution into the transformer oil and introduce them into the gas relay. Determine the incident position of the light source unit and make the laser signal emitted by the light source unit incident on the observation window on one side in the gas relay. The sensor unit processes the scattered light on the observation window on the other side in the gas relay, thereby capturing the scattered light intensity signals at different angles. The processing unit calculates the bubble size distribution based on the Mie scattering principle for the scattered light intensity signals. The machine vision unit takes pictures of the bubble group in the gas relay and monitors the bubble rising rate through the matching window correlation method in the processing unit, so as to obtain the corresponding gas production rate by combining the bubble size distribution and the bubble rising rate. The matching window correlation method first selects the image of the bubble group captured by the machine vision unit to calculate the velocity field from a coarse grid to a fine grid, and then tracks and measures the velocity of a single bubble after meeting the resolution requirements.

11. A storage medium containing computer-executable instructions, characterized in that, When the computer-executable instructions are executed by the processor, the processor executes the method according to any one of claims 1-9.

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