CN118134933A - SF6 leakage quantitative detection method and system for sensing state of power transformation equipment - Google Patents
SF6 leakage quantitative detection method and system for sensing state of power transformation equipment Download PDFInfo
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Abstract
The invention discloses a quantitative SF6 leakage detection method and a quantitative SF6 leakage detection system for sensing the state of power transformation equipment, wherein the method comprises the steps of acquiring infrared video images of the power transformation equipment and performing multi-frame synthesis pretreatment; SF6 leakage gas cloud cluster detection and rectangular labeling are carried out on the preprocessed infrared video image; dividing the rectangular labeling area image to separate SF6 leakage gas cloud cluster images; and determining SF6 leakage points, a gas cloud center and cloud edges according to the SF6 leakage gas cloud image, measuring SF6 concentration values of the SF6 leakage points, the gas cloud center and the cloud edges and on-site environment data, and further calculating to obtain SF6 leakage rate. The SF6 leakage point detection method not only can automatically detect the SF6 leakage point position, but also combines the SF6 leakage gas cloud cluster shape with the gas concentration at the leakage position, quantitatively analyzes the SF6 leakage rate, and provides quantitative reference for the state sensing of the follow-up power transformation equipment.
Description
Technical Field
The invention belongs to the technical field of SF6 gas leakage detection of power equipment, and particularly relates to a quantitative SF6 leakage detection method and system for sensing the state of power transformation equipment.
Background
SF6 is a colorless and odorless gas that is widely used in the power field due to its excellent insulation and arc extinguishing properties. While SF6 leakage due to sealing problems occurs during operation of the electrical equipment. When SF6 pressure is reduced, the insulation level of the equipment is reduced, the arc extinguishing performance of the switch equipment is reduced, and the operation safety of the power equipment is seriously affected. In addition, since the SF6 gas has a high density, the leaked SF6 in the indoor environment is accumulated toward the bottom, which may be harmful to the human body. SF6 gas is also a very damaging greenhouse gas, and environmental problems caused by SF6 leakage are not small. Therefore, when the SF6 leakage problem occurs in the power equipment, the leakage point is timely and accurately found, and corresponding treatment measures are adopted according to the gas leakage rate, so that the method has great significance in guaranteeing the safety of the equipment and personnel.
In recent years, due to the advantages of far distance, non-contact, dynamic intuitiveness and the like of an infrared thermal imaging technology, SF6 leakage detection based on the infrared thermal imaging technology is widely applied in the electric field, but various challenges still exist, such as a scheme for SF6 leakage detection in an infrared imaging mode, an image comparison method is mostly adopted, SF6 leakage points are marked through changes among different video frames, the SF6 leakage points are analyzed through changes among the video frames, a certain effect is achieved when the gas leakage amount is large and the injection speed is high, the method is not suitable for the situation of slow gas leakage, and for a handheld infrared imager, the video frame comparison algorithm effect is poor due to hand shake; the image enhancement algorithm is adopted, so that SF6 gas is more obvious in the image, and an operator can find leakage points conveniently, namely the currently widely adopted SF6 image enhancement algorithm still needs to analyze and judge the infrared video image manually, and the operation of the operator cannot be reduced effectively. In addition, the infrared imaging technology can only perform qualitative analysis on SF6 gas leakage, and cannot quantitatively analyze the gas leakage condition, so that the accurate quantification basis is lacking when the equipment leakage defect is treated. In the aspect of quantitative detection of SF6 gas leakage, the currently widely used handheld leak detectors can only measure the concentration of SF6 at the leakage position, and cannot accurately analyze the gas leakage rate. According to industry regulations, the annual leakage rate of sulfur hexafluoride of electrical equipment must not exceed 0.5%, and the leakage rate must be correspondingly treated once exceeding a standard value. The traditional quantitative detection method comprises a buckling cover method, a bottle hanging method, a local binding method, a pressure drop method and the like, and the methods basically need tens of hours, so that time and labor are wasted, and part of the positions can be detected only by power failure. Therefore, how the leakage rate of sulfur hexafluoride is judged through the interval time of two air supplementing, and the interval time of two air supplementing is generally less than half a year, so that the equipment is evaluated as an abnormal state, corresponding maintenance is needed, and the leaked sulfur hexafluoride in the interval time of two air supplementing can possibly damage the environment and personnel. Therefore, an immediate quantitative detection method is needed to make an overhaul decision and assist in time.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides the quantitative detection method and the quantitative detection system for SF6 leakage for sensing the state of the power transformation equipment, which not only can automatically detect the position of the SF6 leakage point, but also combine the shape of SF6 leakage gas cloud cluster with the gas concentration at the leakage position, quantitatively analyze the SF6 leakage rate and provide quantitative reference for the state sensing of the follow-up power transformation equipment.
The invention adopts the following technical scheme.
An SF6 leakage quantitative detection method for state sensing of power transformation equipment, comprising:
step1, acquiring an infrared video image of power transformation equipment and performing multi-frame synthesis pretreatment;
step 2, SF6 leakage gas cloud cluster detection and rectangular labeling are carried out on the preprocessed infrared video image;
step 3, dividing the rectangular labeling area image, and separating SF6 leakage gas cloud cluster images;
And 4, determining SF6 leakage points, a gas cloud center and cloud edges according to the SF6 leakage gas cloud image, measuring SF6 concentration values of the SF6 leakage points, the gas cloud center and the cloud edges and on-site environment data, and further calculating to obtain SF6 leakage rate.
Preferably, in step 1, the manner of performing the multi-frame synthesis preprocessing is as follows: extracting continuous three-frame images from infrared video of power transformation equipment, taking the continuous three-frame images as a front frame, a reference frame and a rear frame, and calculating inter-frame SAD; and calculating the weight of each frame of data according to the inter-frame SAD, and carrying out multi-frame image weighted fusion to generate an image infrared video image after noise reduction, namely the preprocessed infrared video image.
Preferably, in step 2, SF6 leakage gas cloud detection is performed on the preprocessed infrared video image, and a rectangular label of the gas cloud is obtained on the infrared video image, which is specifically as follows:
Carrying out repeated downsampling on the preprocessed infrared video image by adopting Darknet-53 networks with a plurality of stacked residual modules;
constructing an FPN feature pyramid, and carrying out enhanced feature extraction on the gas cloud cluster in the image after multiple downsampling to obtain a gas cloud cluster boundary box;
and obtaining a prediction result of the gas cloud image by utilizing the YOLO Head, taking a gas cloud bounding box as an image segmentation limit of the gas cloud rectangular label, and carrying out the gas cloud rectangular label on the prediction result.
Preferably, step 3 specifically includes: and (3) carrying out image pixel area calculation and segmentation condition judgment on the rectangular labeling area image, and segmenting the rectangular labeling area image by adopting a pre-trained U2-Net image segmentation model if the segmentation condition is met, so as to separate SF6 leakage gas cloud cluster images, otherwise, returning to the step (1).
Preferably, the image pixel area calculation formula is: ; wherein m and n are the number of pixels in the length direction and the width direction of the rectangular labeling area image respectively;
the segmentation conditions are as follows: ; wherein/> Is a threshold value.
Preferably, a pre-trained U2-Net image segmentation model is adopted to segment the rectangular labeling area image, and the specific steps of separating SF6 leakage gas cloud cluster images are as follows:
the pre-trained U2-Net image segmentation model adopts an encoder to extract image characteristics of a rectangular marked gas cloud cluster region in an infrared video image;
the decoder is adopted to map the extracted image characteristics back to the rectangular marked gas cloud cluster area;
And capturing the details and the edge information of the original image by using a self-attention mechanism, and separating the gas cloud clusters marked by the rectangles in the infrared video image.
Preferably, in the step 4, the highest point of the SF6 concentration value in the gas cloud cluster image is taken as an SF6 leakage point;
the gas cloud center was determined according to the following formula ：
、/>，
Wherein,The horizontal and vertical coordinates of the center of the gas cloud;
n is the total number of pixel points in the gas cloud;
x i、yi is the horizontal and vertical coordinates of the ith pixel point;
w i is the weight of the ith pixel point.
Preferably, the method comprises the steps of,，
，
，
Wherein,Is the equilibrium coefficient; /(I)Is a brightness weight; /(I)Is the brightness value of the pixel point; /(I)Is an adjustment coefficient; is the spatial density weight; /(I) Is to set the number of adjacent pixel points in the radius,/>Is a threshold.
Preferably, in step4, measuring SF6 concentration values of SF6 leakage points, gas cloud center and cloud edges by using a negative corona discharge sensor, wherein the SF6 concentration values of the cloud edges are determined by taking the average value of a plurality of edge point concentration values; the field environment data is wind speed.
Preferably, in step 4, the SF6 leakage rate is calculated as:
，
Wherein, (L= L, C, E) represents the SF6 concentration value; q is SF6 leakage rate; σ y and σ z are diffusion parameters; u is wind speed; x, y, z are the horizontal, vertical and vertical coordinates of the video image acquisition point relative to the leakage point; h is the height of the leak;
And substituting SF6 concentration values C L,CC,CE of the SF6 leakage point, the gas cloud center and the cloud edge into the calculation formula respectively to obtain SF6 leakage rates Q, sigma y and sigma z, wherein Q is the SF6 leakage rate obtained through quantitative detection.
An SF6 leak quantitative detection system for power transformation equipment status awareness, comprising:
The image acquisition module is used for acquiring the infrared video image of the power transformation equipment and carrying out multi-frame synthesis pretreatment;
the gas cloud cluster labeling module is used for carrying out SF6 leakage gas cloud cluster detection and rectangular labeling on the preprocessed infrared video image;
The gas cloud cluster separation module is used for dividing the rectangular marked area image and separating SF6 leakage gas cloud cluster images;
and the SF6 leakage rate calculation module is used for determining SF6 leakage points, gas cloud centers and cloud edges according to the SF6 leakage gas cloud cluster images, measuring SF6 concentration values of the SF6 leakage points, the gas cloud centers and the cloud edges and on-site environment data, and further calculating to obtain the SF6 leakage rate.
A terminal comprising a processor and a storage medium; the storage medium is used for storing instructions; the processor is configured to operate in accordance with the instructions to perform the steps of the method.
A computer readable storage medium having stored thereon a computer program which when executed by a processor realizes the steps of the method.
The invention has the beneficial effects that compared with the prior art:
1. in the aspect of quantitative analysis of SF6 leakage, the invention provides accurate quantitative analysis of SF6 leakage rate for the first time, so as to make overhaul decision assistance in time;
2. The invention realizes the extraction of key information of multi-frame images based on the inter-frame SAD, automatically eliminates noise points, synthesizes the final infrared video image by utilizing the multi-frame images, effectively improves the image quality and can effectively reduce the subsequent calculation consumption;
3. According to the invention, the infrared video image is detected in real time, the leaked gas cloud cluster is automatically marked in the image in a frame selection way, and the SF6 leakage position is dynamically and intuitively marked on the image, so that an operator can conveniently and rapidly and accurately find the SF6 leakage point, the workload of the operator is reduced, and the accuracy and the detection efficiency of SF6 leakage point detection are improved;
4. According to the invention, through image segmentation of the SF6 gas cloud, and combining with a calculation formula of the center of the gas cloud and the SF6 leakage rate, quantitative calculation analysis of the SF6 gas leakage rate is realized through multipoint SF6 concentration values and environmental data at the leakage points measured by the handheld negative corona discharge sensor, and data support is provided for subsequent maintenance. The self-attention mechanism is introduced into the segmentation model, so that the model has higher capability of capturing details and edge information of an original image, thereby realizing accurate segmentation of SF6 leakage gas cloud cluster images, and calculating SF6 leakage rate according to SF6 leakage points, the centers of the gas cloud clusters, SF6 concentration values of cloud cluster edges and on-site environment data.
Drawings
Fig. 1 is a flowchart of a quantitative detection method for SF6 leakage for sensing the state of power transformation equipment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. The described embodiments of the application are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present application.
As shown in fig. 1, embodiment 1 of the present invention provides a quantitative detection method for SF6 leakage for sensing the state of power transformation equipment, which includes the following steps:
step1, acquiring an infrared video image of power transformation equipment and performing multi-frame synthesis pretreatment;
And shooting positions such as bolts, flanges, air supplementing interfaces and the like, which possibly have SF6 leakage, by adopting a fixed SF6 gas detection infrared imager or a handheld SF6 gas detection infrared imager arranged in the transformer substation, acquiring infrared video images, carrying out multi-frame synthesis pretreatment, and storing the pretreated infrared video images of equipment at the positions.
Considering that the original video has higher frame rate, the target detection calculation amount is large directly, the original video is processed by multi-frame synthesis, and the image quality is improved. Specifically, the noise reduction algorithm can be adopted to reduce the original video frame rate to 1 frame of image per second, and the processing steps are as follows:
Step 1.1, extracting continuous three-frame images from infrared video of power transformation equipment, taking the continuous three-frame images as a front frame, a reference frame and a rear frame, and calculating inter-frame SAD;
The inter SAD calculation method comprises the following steps: and comparing gray value differences of corresponding pixel points between the continuous frames, and calculating the sum of absolute values of the gray value differences to obtain an inter-frame SAD value. The SAD value can effectively reflect the variation degree between frames and provide key data for the weight determination of the subsequent frames;
Gradient analysis can also be performed on reference frames, preceding frames, following frames: by calculating the horizontal and vertical gradients of pixels in each frame and averaging, the dynamic changes of SF6 gas leakage are accurately identified and tracked. The process utilizes the gradient result to obviously highlight the motion trail of the gas cloud cluster, and provides basis for quantitative detection.
And 1.2, calculating the weight of each frame of data according to the inter-frame SAD, and carrying out multi-frame image weighted fusion to generate an image infrared video image after noise reduction, namely the preprocessed infrared video image.
Determining frame weights from the inter-frame SAD: weights are assigned based on the SAD value between each frame and the reference frame. Frames with lower SAD values, i.e. frames with less image variation and more stable information, will be given higher weights.
Multi-frame image composition application: and carrying out weighted fusion on the multi-frame images by using the calculated frame weights to generate the noise-reduced infrared video images. The method improves the accuracy of leakage detection and the image quality by optimizing frames with large information quantity and small noise.
Step 2, SF6 leakage gas cloud cluster detection and rectangular labeling are carried out on the preprocessed infrared video image;
SF6 leakage gas cloud cluster detection is carried out on the preprocessed infrared video image by adopting a Yolo v-based target detection model, and gas cloud cluster rectangular labels are obtained on the infrared video image picture;
yolo v3 is a commonly used image target detection algorithm, and the target detection model based on Yolo v is trained by using data containing the leaked gas cloud cluster marks in the model training process, so that the model can obtain the capability of detecting the leaked gas cloud cluster. And inputting each frame of image in the video into a trained model, automatically marking the leaked gas cloud cluster (in a rectangular frame form) in the image based on a Yolo v target detection model, and outputting the model as four-corner vertex coordinates of a marked rectangular frame.
Further, SF6 leakage gas cloud cluster detection is carried out on the preprocessed infrared video image by adopting a target detection model based on Yolo v, and gas cloud cluster rectangular labels are obtained on the infrared video image, and the method comprises the following specific steps of:
Step 2.1, adopting Darknet-53 networks with a plurality of residual modules stacked, and downsampling the preprocessed infrared video image for a plurality of times;
2.2, constructing an FPN feature pyramid, and carrying out enhanced feature extraction on the gas cloud cluster in the image after multiple downsampling to obtain a gas cloud cluster boundary box;
And 2.3, obtaining a prediction result of the gas cloud image by utilizing the YOLO Head, taking a gas cloud bounding box as an image segmentation limit of the gas cloud rectangular label, and carrying out the gas cloud rectangular label on the prediction result.
Step 3, dividing the rectangular labeling area image, and separating SF6 leakage gas cloud cluster images;
taking the rectangular area image obtained in the step 2 as input, and separating the leaked gas cloud cluster from the image by using a U2-Net image segmentation algorithm, wherein the method specifically comprises the following steps of:
Step 3.1, carrying out image pixel area calculation and segmentation condition judgment on the rectangular labeling area image, if the segmentation condition is met, entering step 3.2, otherwise, returning to step 1;
Before image segmentation, the pixel ratio of the target rectangle in the whole picture is ensured to exceed a certain threshold, so that the gas cloud cluster to be segmented is ensured to completely appear and occupy the main body position of the picture, and the picture segmentation effect and the accuracy of subsequent calculation are ensured. Specific:
The image pixel area calculation formula is: ; wherein m and n are the number of pixels in the length direction and the width direction of the rectangular labeling area image respectively;
the segmentation conditions are as follows: ; wherein/> Is a threshold value.
That is, the number of pixels in the length direction of the image in the rectangular labeling area is m, the number of pixels in the width direction is n, and the threshold value isWhen thenAnd (3) if not, prompting the user to move the near infrared imager to shoot again.
And 3.2, segmenting the rectangular labeling area image by adopting a pre-trained U2-Net image segmentation model, and separating out SF6 leakage gas cloud cluster images.
Inputting the rectangular marked area image meeting the segmentation condition into a pre-trained U2-Net image segmentation model (namely the pre-trained U2-Net image segmentation model adopted in the process is subjected to gas cloud cluster segmentation task), wherein the output result of the U2-Net image segmentation model is a binary mask matrix with the same length and width pixel number as that of the original imageWherein the pixels corresponding to 1 in the mask matrix are the gas cloud image, and the pixels corresponding to 0 are the background image. The specific separation steps are as follows:
the pre-trained U2-Net image segmentation model adopts an encoder to extract image characteristics of a rectangular marked gas cloud cluster region in an infrared video image;
the decoder is adopted to map the extracted image characteristics back to the rectangular marked gas cloud cluster area;
And capturing the details and the edge information of the original image by using a self-attention mechanism, and separating the gas cloud clusters marked by the rectangles in the infrared video image.
And 4, determining SF6 leakage points, a gas cloud center and cloud edges according to the SF6 leakage gas cloud image, measuring SF6 concentration values of the SF6 leakage points, the gas cloud center and the cloud edges and on-site environment data, and further calculating to obtain SF6 leakage rate.
Taking the point with the highest SF6 concentration value (namely the point with the deepest color) in the gas cloud cluster image as an SF6 leakage point;
determining the center coordinates of the gas cloud according to the following formula ：
、/>
Wherein,The horizontal and vertical coordinates of the center of the gas cloud;
n is the total number of pixel points in the gas cloud;
x i、yi is the horizontal and vertical coordinates of the ith pixel point;
w i is the weight of the ith pixel point, and is determined by the brightness and density of the pixel.
Weighting of pixel pointsThe calculation may be performed as follows:
Wherein/> Is a coefficient for balancing luminance weight and spatial density weight;
for brightness weight, converting brightness value of pixel point into weight:
，
Wherein the method comprises the steps of Is the brightness value of the pixel point,/>Is an adjustment coefficient; /(I)For spatial density weights, the number of neighboring pixels around a pixel point is calculated:
，
Wherein the method comprises the steps of Is to set the number of adjacent pixel points in the radius,/>Is a threshold. Based on the analysis, the SF6 gas leakage rate is calculated, and the method specifically comprises the following steps.
And 4.1, respectively measuring SF6 concentration values of the leakage points, the center of the SF6 gas cloud and the edge of the cloud by using a handheld negative corona discharge sensor. The method comprises the following steps: collecting SF6 concentration value (C L) at SF6 leakage point (L) with unit of ppm; SF6 concentration values (C C and C E) were collected at the center (C) and edge (E) of the gas cloud, respectively. The SF6 concentration value C E at the edge of the cloud cluster can be determined by taking the average value of the concentration value of any point at the edge or the concentration values of a plurality of edge points according to the actual situation.
Wind speed is measured in meters per second (m/s) using the relevant instrument.
Step 4.2, SF6 leakage in the transformer substation can be approximately regarded as continuous point source type diffusion in an open and flat area and under uniform atmospheric conditions, and SF6 leakage rate modeling is conducted on gas leakage conditions. The image after image segmentation is approximately regarded as an equal concentration plane, and the gas diffusion flow speed can be reversely deduced by the wind speed and the multi-point SF6 concentration value.
The SF6 leak rate calculation model is:
，
Wherein, (L= L, C, E) represents the SF6 concentration value; q is unknown SF6 leakage rate; σ y and σ z are diffusion parameters, related to environmental conditions; u is wind speed; x, y, z are the coordinates of the video image acquisition point relative to the leak point; h is the height of the leak.
And substituting the measured SF6 concentration value C L,CC,CE into the model respectively, and solving to obtain SF6 leakage rates Q, sigma y and sigma z, wherein Q is the SF6 leakage rate obtained by quantitative detection.
The embodiment 2 of the invention provides an SF6 leakage quantitative detection system for sensing the state of power transformation equipment, which comprises the following components:
The image acquisition module is used for acquiring the infrared video image of the power transformation equipment and carrying out multi-frame synthesis pretreatment;
the gas cloud cluster labeling module is used for carrying out SF6 leakage gas cloud cluster detection and rectangular labeling on the preprocessed infrared video image;
The gas cloud cluster separation module is used for dividing the rectangular marked area image and separating SF6 leakage gas cloud cluster images;
and the SF6 leakage rate calculation module is used for determining SF6 leakage points, gas cloud centers and cloud edges according to the SF6 leakage gas cloud cluster images, measuring SF6 concentration values of the SF6 leakage points, the gas cloud centers and the cloud edges and on-site environment data, and further calculating to obtain the SF6 leakage rate.
A terminal comprising a processor and a storage medium; the storage medium is used for storing instructions; the processor is operative to perform steps according to the method in accordance with the instructions.
A computer readable storage medium having stored thereon a computer program which when executed by a processor realizes the steps of the method.
The invention has the beneficial effects that compared with the prior art:
1. in the aspect of quantitative analysis of SF6 leakage, the invention provides accurate quantitative analysis of SF6 leakage rate for the first time, so as to make overhaul decision assistance in time;
2. The invention realizes the extraction of key information of multi-frame images based on the inter-frame SAD, automatically eliminates noise points, synthesizes the final infrared video image by utilizing the multi-frame images, effectively improves the image quality and can effectively reduce the subsequent calculation consumption;
3. According to the invention, the infrared video image is detected in real time, the leaked gas cloud cluster is automatically marked in the image in a frame selection way, and the SF6 leakage position is dynamically and intuitively marked on the image, so that an operator can conveniently and rapidly and accurately find the SF6 leakage point, the workload of the operator is reduced, and the accuracy and the detection efficiency of SF6 leakage point detection are improved;
4. According to the invention, through image segmentation of the SF6 gas cloud, and combining with a calculation formula of the center of the gas cloud and the SF6 leakage rate, quantitative calculation analysis of the SF6 gas leakage rate is realized through multipoint SF6 concentration values and environmental data at the leakage points measured by the handheld negative corona discharge sensor, and data support is provided for subsequent maintenance. The self-attention mechanism is introduced into the segmentation model, so that the model has higher capability of capturing details and edge information of an original image, thereby realizing accurate segmentation of SF6 leakage gas cloud cluster images, and calculating SF6 leakage rate according to SF6 leakage points, the centers of the gas cloud clusters, SF6 concentration values of cloud cluster edges and on-site environment data.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as SMALLTALK, C ++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (13)
1. A quantitative detection method for SF6 leakage for sensing the state of a power transformation device, the method comprising:
step1, acquiring an infrared video image of power transformation equipment and performing multi-frame synthesis pretreatment;
step 2, SF6 leakage gas cloud cluster detection and rectangular labeling are carried out on the preprocessed infrared video image;
step 3, dividing the rectangular labeling area image, and separating SF6 leakage gas cloud cluster images;
And 4, determining SF6 leakage points, a gas cloud center and cloud edges according to the SF6 leakage gas cloud image, measuring SF6 concentration values of the SF6 leakage points, the gas cloud center and the cloud edges and on-site environment data, and further calculating to obtain SF6 leakage rate.
2. The quantitative detection method for SF6 leakage for sensing the state of power transformation equipment according to claim 1, wherein the quantitative detection method comprises the following steps:
In step 1, the mode of performing multi-frame synthesis pretreatment is as follows: extracting continuous three-frame images from infrared video of power transformation equipment, taking the continuous three-frame images as a front frame, a reference frame and a rear frame, and calculating inter-frame SAD; and calculating the weight of each frame of data according to the inter-frame SAD, and carrying out multi-frame image weighted fusion to generate an image infrared video image after noise reduction, namely the preprocessed infrared video image.
3. The quantitative detection method for SF6 leakage for sensing the state of power transformation equipment according to claim 1, wherein the quantitative detection method comprises the following steps:
in the step2, SF6 leakage gas cloud cluster detection is carried out on the preprocessed infrared video image, and rectangular labels of the gas cloud clusters are obtained on the infrared video image, wherein the method comprises the following steps of:
Carrying out repeated downsampling on the preprocessed infrared video image by adopting Darknet-53 networks with a plurality of stacked residual modules;
constructing an FPN feature pyramid, and carrying out enhanced feature extraction on the gas cloud cluster in the image after multiple downsampling to obtain a gas cloud cluster boundary box;
and obtaining a prediction result of the gas cloud image by utilizing the YOLO Head, taking a gas cloud bounding box as an image segmentation limit of the gas cloud rectangular label, and carrying out the gas cloud rectangular label on the prediction result.
4. The quantitative detection method for SF6 leakage for sensing the state of power transformation equipment according to claim 1, wherein the quantitative detection method comprises the following steps:
The step 3 specifically comprises the following steps: and (3) carrying out image pixel area calculation and segmentation condition judgment on the rectangular labeling area image, and segmenting the rectangular labeling area image by adopting a pre-trained U2-Net image segmentation model if the segmentation condition is met, so as to separate SF6 leakage gas cloud cluster images, otherwise, returning to the step (1).
5. The quantitative detection method for SF6 leakage for sensing the state of power transformation equipment according to claim 4, wherein the quantitative detection method comprises the following steps:
The image pixel area calculation formula is: ; wherein m and n are the number of pixels in the length direction and the width direction of the rectangular labeling area image respectively;
the segmentation conditions are as follows: ; wherein/> Is a threshold value.
6. The quantitative detection method for SF6 leakage for sensing the state of power transformation equipment according to claim 1, wherein the quantitative detection method comprises the following steps:
the method for separating SF6 leakage gas cloud cluster images by segmenting rectangular labeling area images by using a pre-trained U2-Net image segmentation model comprises the following specific steps:
the pre-trained U2-Net image segmentation model adopts an encoder to extract image characteristics of a rectangular marked gas cloud cluster region in an infrared video image;
the decoder is adopted to map the extracted image characteristics back to the rectangular marked gas cloud cluster area;
And capturing the details and the edge information of the original image by using a self-attention mechanism, and separating the gas cloud clusters marked by the rectangles in the infrared video image.
7. The quantitative detection method for SF6 leakage for sensing the state of power transformation equipment according to claim 1, wherein the quantitative detection method comprises the following steps:
in the step 4, taking the highest point of SF6 concentration value in the gas cloud cluster image as an SF6 leakage point;
the gas cloud center was determined according to the following formula ：
、/>，
Wherein,The horizontal and vertical coordinates of the center of the gas cloud;
n is the total number of pixel points in the gas cloud;
x i、yi is the horizontal and vertical coordinates of the ith pixel point;
w i is the weight of the ith pixel point.
8. The quantitative detection method for SF6 leakage for sensing the state of power transformation equipment according to claim 7, wherein the quantitative detection method comprises the following steps:
，
，
，
Wherein, Is the equilibrium coefficient; /(I)Is a brightness weight; /(I)Is the brightness value of the pixel point; /(I)Is an adjustment coefficient; is the spatial density weight; /(I) Is to set the number of adjacent pixel points in the radius,/>Is a threshold.
9. The quantitative detection method for SF6 leakage for sensing the state of power transformation equipment according to claim 1, wherein the quantitative detection method comprises the following steps:
In the step 4, SF6 concentration values of SF6 leakage points, gas cloud cluster centers and cloud cluster edges are measured through a negative corona discharge sensor, wherein the SF6 concentration values of the cloud cluster edges are determined in a mode of taking the average value of a plurality of edge point concentration values; the field environment data is wind speed.
10. The quantitative detection method for SF6 leakage for sensing the state of power transformation equipment according to claim 1, wherein the quantitative detection method comprises the following steps:
in step 4, the formula for calculating the SF6 leak rate is:
，
Wherein, (L= L, C, E) represents the SF6 concentration value; q is SF6 leakage rate; σ y and σ z are diffusion parameters; u is wind speed; x, y, z are the horizontal, vertical and vertical coordinates of the video image acquisition point relative to the leakage point; h is the height of the leak;
And substituting SF6 concentration values C L,CC,CE of the SF6 leakage point, the gas cloud center and the cloud edge into the calculation formula respectively to obtain SF6 leakage rates Q, sigma y and sigma z, wherein Q is the SF6 leakage rate obtained through quantitative detection.
11. A quantitative detection system for SF6 leakage for sensing the status of a power transformation device, using the method of any of claims 1-10, characterized in that: the system comprises:
The image acquisition module is used for acquiring the infrared video image of the power transformation equipment and carrying out multi-frame synthesis pretreatment;
the gas cloud cluster labeling module is used for carrying out SF6 leakage gas cloud cluster detection and rectangular labeling on the preprocessed infrared video image;
The gas cloud cluster separation module is used for dividing the rectangular marked area image and separating SF6 leakage gas cloud cluster images;
and the SF6 leakage rate calculation module is used for determining SF6 leakage points, gas cloud centers and cloud edges according to the SF6 leakage gas cloud cluster images, measuring SF6 concentration values of the SF6 leakage points, the gas cloud centers and the cloud edges and on-site environment data, and further calculating to obtain the SF6 leakage rate.
12. A terminal comprising a processor and a storage medium; the method is characterized in that: the storage medium is used for storing instructions; the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-10.
13. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-10.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190340914A1 (en) * | 2018-05-03 | 2019-11-07 | Quantum IR Technologies, LLC | Infrared imaging systems and methods for gas leak detection |
| CN110672278A (en) * | 2019-10-16 | 2020-01-10 | 北京工业大学 | Method for quantitatively measuring VOCs leakage of production device based on infrared imaging |
| CN111033231A (en) * | 2017-06-12 | 2020-04-17 | 前视红外***股份公司 | System and method for quantifying gas leaks |
| CN111537157A (en) * | 2020-05-11 | 2020-08-14 | 嘉兴极光物联网科技有限公司 | Method and system for efficiently detecting gas leakage by visual qualitative and accurate quantitative cooperation |
| CN115909180A (en) * | 2023-01-04 | 2023-04-04 | 中环碳和(北京)科技有限公司 | Sulfur hexafluoride measurement method |
-
2024
- 2024-05-08 CN CN202410559119.6A patent/CN118134933A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111033231A (en) * | 2017-06-12 | 2020-04-17 | 前视红外***股份公司 | System and method for quantifying gas leaks |
| US20190340914A1 (en) * | 2018-05-03 | 2019-11-07 | Quantum IR Technologies, LLC | Infrared imaging systems and methods for gas leak detection |
| CN110672278A (en) * | 2019-10-16 | 2020-01-10 | 北京工业大学 | Method for quantitatively measuring VOCs leakage of production device based on infrared imaging |
| CN111537157A (en) * | 2020-05-11 | 2020-08-14 | 嘉兴极光物联网科技有限公司 | Method and system for efficiently detecting gas leakage by visual qualitative and accurate quantitative cooperation |
| CN115909180A (en) * | 2023-01-04 | 2023-04-04 | 中环碳和(北京)科技有限公司 | Sulfur hexafluoride measurement method |
Non-Patent Citations (2)
| Title |
|---|
| 沙利文: "《电力***规划》", 31 December 1984, pages: 117 - 119 * |
| 王琦等: ""被动式红外成像气体目标智能检测算法及量化研究进展"", 《控制与决策》, vol. 38, no. 8, 31 August 2023 (2023-08-31), pages 2265 - 2282 * |
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