CN117934458A - Smart grid safety protection method based on short shared power wireless communication - Google Patents
Smart grid safety protection method based on short shared power wireless communication Download PDFInfo
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Abstract
The invention relates to the field of power grid safety protection, in particular to a smart power grid safety protection method based on short shared power wireless communication. The method comprises the following steps: acquiring a multi-frame power transmission line gray level map; constructing a sliding window, calculating the similarity between a target gray value histogram of a frame corresponding to the sliding window position and a standard histogram, and traversing gray value histograms of all frames at the same sliding window position as definition evaluation to obtain a power transmission line graph with high definition evaluation; fitting the pixel points in the region to be detected by using a preset model to obtain the linearity degree of the region to be detected; and calculating the abnormal degree of the region to be detected based on the linear degree set, comparing the abnormal degree with a preset threshold value, judging whether the region to be detected is a fault region, and finishing the safety protection of the power grid. By the technical scheme, the scattered faults of the power grid can be detected more accurately, and the running safety of the power grid is ensured.
Description
Technical Field
The present invention relates generally to the field of grid security. More particularly, the invention relates to a smart grid security protection method based on short shared power wireless communication.
Background
In recent years, a short sharing technology is introduced and used in a private network for power transaction, and the short sharing technology has the following advantages: the extremely low communication latency can be provided, the large capacity and high bandwidth of the network can cope with the huge amount of data in the virtual power plant system, and the short sharing technology can create customized communication networks for virtual power plant data predictions.
With the modern and intelligent development of power systems, smart grids have become an important development direction of the power industry. The short shared power wireless communication technology is used as a key support technology of the smart grid, provides an efficient and safe communication means for a power system, and realizes information interaction and cooperative control between power equipment. However, with the continuous expansion of the smart grid scale and the improvement of the power informatization degree, the security of the power grid faces more and more challenges and threats, and how to ensure the safe and stable operation of the power grid becomes a problem to be solved.
Disclosure of Invention
To solve one or more of the above technical problems, the present invention proposes a smart grid security protection method based on short-sharing power wireless communication. To this end, the present invention provides solutions in various aspects as follows.
A smart grid security protection method based on short shared power wireless communication comprises the following steps: acquiring a multi-frame power transmission line gray level diagram, and acquiring a gray level histogram corresponding to the power transmission line gray level diagram; taking a gray value histogram of the first frame of power line gray level diagram as a standard histogram; constructing sliding windows, and calculating the similarity between a gray value histogram and a standard histogram of each sliding window in each frame of the power line gray level diagram from the second frame of the power line gray level diagram to be used as definition evaluation; obtaining a power transmission line graph with the highest definition evaluation value; extracting a connected domain area from the power transmission line graph to serve as an area to be detected; fitting pixel points in the to-be-detected area by using a preset model to obtain the linearity degree of the to-be-detected area, thereby obtaining a linearity degree set of all the to-be-detected areas; calculating the abnormality degree of the region to be detected, and judging whether the region to be detected is a fault region or not: responding to the abnormal degree of the area to be detected being smaller than a preset threshold value, judging that the area to be detected is a fault area, and generating and sending a repair detection instruction; and responding to the abnormality degree of the area to be detected is not smaller than a preset threshold value, judging that the area to be detected is not a fault area, generating and sending a continuous inspection instruction.
In one embodiment, the acquiring the gray-scale map of the power line includes the steps of: shooting an electric transmission line RGB video, and converting multi-frame RGB images in the electric transmission line RGB video into gray images; inputting the gray image into a preset segmentation network to obtain a mask image of the gray image; setting the pixel value of a power transmission line area in the mask map to be 1, setting the pixel value of an abnormal area to be 0, and obtaining a power transmission line 0-1 map; and multiplying the gray level image with the power transmission line 0-1 graph to obtain a power transmission line gray level graph.
In one embodiment, the sharpness evaluation satisfies a polynomial:
wherein, Indicate the corresponding/>, of the sliding windowSimilarity between frame target gray value histogram and standard histogram,/>Representing a standard histogram,/>Indicate the corresponding/>, of the sliding windowTarget gray value histogram of frame,/>Indicate the corresponding/>, of the sliding windowFirst/>, in target gray value histogram of frameIndividual Unit,/>Indicate the corresponding/>, of the sliding windowAverage value of all cells in target gray value histogram of frame,/>Representing the/>, in a standard histogramIndividual Unit,/>Representing the mean of all cells in the standard histogram.
In one embodiment, the step of obtaining a power transmission line graph with the highest definition evaluation value includes the steps of: obtaining the similarity of all frames at the same sliding window position, and constructing a similarity set; acquiring a minimum value in the similarity set, and taking a power transmission line gray level diagram corresponding to the minimum value as a power transmission line diagram of the same sliding window position; traversing the sliding window through the whole power transmission line according to a preset step length and a preset size, thereby obtaining the power transmission line with the highest definition evaluation value.
In one embodiment, obtaining the linearity of the region to be detected comprises the steps of: randomly selecting two pixel points in the region to be detected, obtaining a fitting straight line by using a least square method, and verifying that the loss function meets the following relation:
wherein, Representing the loss function of a fitted line,/>Representing the/>, within the area to be detectedDistance from each pixel point to a fitting straight line,/>Representing the distance from the pixel point in the region to be detected to the fitting straight line,/>An abscissa value representing the position of a pixel point within the region to be detected,/>Ordinate value representing the position of a pixel point within the region to be detected,/>Representing parameters of the fitted line.
When the loss function is minimum, fitting of a fitting straight line is completed; traversing all pixel points in the area to be detected to obtain a fitting straight line set, and solving a loss value corresponding to each fitting straight line; and taking the fitting straight line data with the minimum loss value as the linearity degree data of the region to be detected.
In one embodiment, the degree of abnormality of the region to be detected satisfies the relationship:
wherein, Represents the/>Abnormality degree of each region to be detected,/>Represents the/>The linear abnormality degree of each region to be detected,/>Representing a set of linear anomalies.
The invention has the following beneficial effects:
Through the advantages of high frequency and high speed of 5G/4G short sharing wireless communication, the shot image of the power transmission line is rapidly judged, and a power transmission line diagram with a fault area mark is generated, so that the power transmission line area with faults can be more accurately and rapidly detected, and the running safety of a power grid is ensured.
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The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. In the drawings, embodiments of the invention are illustrated by way of example and not by way of limitation, and like reference numerals refer to similar or corresponding parts and in which:
Fig. 1 is a flowchart of a smart grid security protection method based on short shared power wireless communication according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that when the terms "first," "second," and the like are used in the claims, the specification and the drawings of the present invention, they are used merely for distinguishing between different objects and not for describing a particular sequential order. The terms "comprises" and "comprising" when used in the specification and claims of the present invention are taken to specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The invention provides a smart grid security protection method based on short shared power wireless communication. As shown in fig. 1, the smart grid security protection method based on short-sharing power wireless communication includes steps S1 to S4, which are described in detail below.
S1, acquiring a multi-frame power transmission line gray scale map.
Specifically, a camera mounted on the unmanned aerial vehicle is used for shooting a high-voltage power transmission line, an RGB (red green blue) video stream of the power transmission line is obtained, and real-time video stream data is sent to a data center through 5G/4G short sharing wireless communication.
And converting the multi-frame RGB image in the RGB video stream into a gray image, and extracting a power transmission line region in the gray image. In one embodiment, a plurality of gray level images are collected, a power line area in the gray level images is marked, the gray level images carrying the marks are put into a semantic segmentation network, and a trained power line segmentation network is obtained. Based on the real-time gray level image, inputting the gray level image into a power line segmentation network to obtain a mask image of the gray level image of the power line, setting the pixel value of the power line region in the mask image as 1, setting the pixel value of the abnormal region as 0 to obtain a power line 0-1 segmentation image, and multiplying the real-time gray level image by the power line 0-1 segmentation image to obtain the gray level image of the power line.
S2, constructing a sliding window, calculating the similarity between a target gray value histogram of a frame corresponding to the sliding window position and a standard histogram, and traversing gray value histograms of all frames at the same sliding window position as definition evaluation to obtain a power transmission line graph with high definition evaluation.
Specifically, because the video stream is shot by a camera fixed on the unmanned aerial vehicle, if the unmanned aerial vehicle encounters unstable air flow in actual flight, or the service skills of an unmanned aerial vehicle operator are unskilled, the unmanned aerial vehicle is caused to shake in the air, and the position of a power transmission line in the shot video stream is deviated, so that the follow-up treatment is influenced. Therefore, the power lines in the image need to be screened in monitoring the video stream.
And splicing the power transmission line areas in the shot power transmission line gray level diagram according to the relative position relation between the unmanned aerial vehicle and the cable. When the unmanned aerial vehicle fluctuates, three angular accelerations are obtained through the three-axis gyroscope carried by the unmanned aerial vehicle, the three angular accelerations are input into the integrated accelerometer to obtain lens deflection displacement, adjacent power transmission line gray level images are aligned along the opposite direction and equal length of the lens deflection displacement, the power transmission line gray level images are moved, so that the power transmission line areas in the adjacent frame of power transmission line gray level images are continuous, and continuous power transmission lines consisting of multiple frames of power transmission line gray level images are obtained.
In one embodiment, the powerline gray map alignment satisfies the process: after shooting of the whole section of line is completed, taking the last frame of image as a substrate, and carrying out reverse equidistant adjustment on the image according to the lens deflection displacement and direction of the last frame of image and the adjacent previous frame of image. And so on to finish the adjustment of the first frame image.
In another embodiment, the lens deflection displacement and direction between each frame image and the initial frame image is calculated, aligning each frame image with the initial frame image.
Since there is a power line region repeatedly photographed in the power line gray-scale pictures of consecutive plural adjacent frames, it is necessary to perform sharpness screening on the power line repeated region. And counting a gray value histogram of a power transmission line region in the first frame of power transmission line gray level diagram, wherein the number of units of the gray value histogram is 255, and taking the gray value histogram as a standard histogram. According to the preset sliding window size and step length, a sliding window for definition screening is constructed, and the sliding window length is set asThe width is the width of the power line region, and the step length is set as/>Comprising sliding window length/>For a plurality of image frames of the wire area, the length of the sliding window is set to 2, and the step length is set to 2, that is, the sliding window with the size of 2 moves by 2 units of length each time. And carrying out sliding detection on the continuous power transmission lines by using a preset sliding window, and counting a gray value histogram of each frame of power transmission line gray level picture at the position of the sliding window.
In one embodiment, taking a power line gray level diagram as an example, calculating the similarity degree between a gray level value histogram and a standard histogram of the sliding window position of the power line gray level diagram, and taking the similarity degree as a definition evaluation, wherein the definition evaluation satisfies a polynomial:
wherein, Indicate the corresponding/>, of the sliding windowSimilarity between frame target gray value histogram and standard histogram,/>Representing a standard histogram,/>Indicate the corresponding/>, of the sliding windowTarget gray value histogram of frame,/>Indicate the corresponding/>, of the sliding windowFirst/>, in target gray value histogram of frameIndividual Unit,/>Indicate the corresponding/>, of the sliding windowAverage value of all cells in target gray value histogram of frame,/>Representing the/>, in a standard histogramIndividual Unit,/>Representing the mean of all cells in the standard histogram.
Obtaining the similarity of all frames at the same sliding window position, and constructing a similarity setAnd taking the minimum value in the similarity set, and acquiring a power transmission line gray level diagram corresponding to the minimum value as a power transmission line diagram of the same sliding window position. Traversing the sliding window through the whole power transmission line according to the preset step length and the preset size, thereby obtaining a power transmission line diagram with high definition evaluation.
And S3, fitting the pixel points in the region to be detected by using a preset model to obtain the linearity degree of the region to be detected.
Specifically, canny edge detection is performed on the power line image to obtain an edge map, wherein the edge map comprises edge points and non-edge points, the numerical value of the non-edge points is 0, and the numerical value of the edge points is 1. And extracting a connected domain area in the edge map as an area to be detected.
All pixel points in the area to be detected can be fitted into a straight line, and the higher the fitting degree is, the higher the probability that the area is a normal area is.
In one embodiment, taking an area to be measured as an example, the process of fitting a straight line is: according to a preset function model, randomly selecting two pixel points in a region to be detected without repetition, and obtaining a fitting straight line by using a least square methodThe validation loss function satisfies the following relationship:
wherein, Representing the loss function of a fitted line,/>Representing the/>, within the area to be detectedDistance from each pixel point to a fitting straight line,/>Representing the distance from the pixel point in the region to be detected to the fitting straight line,/>An abscissa value representing the position of a pixel point within the region to be detected,/>Ordinate value representing the position of a pixel point within the region to be detected,/>Representing parameters of the fitted line.
Fitting the line is done in response to the loss function being minimal.
Traversing all pixel points in the region to be detected to obtain a fitting straight line setCalculating a loss value/>, corresponding to each fitting straight lineAnd taking the fitting straight line data with the minimum loss value as the linearity degree data of the current region to be detected.
And S4, calculating the abnormal degree of the region to be detected based on the linear degree set, comparing the abnormal degree with a preset threshold value, judging whether the region to be detected is a fault region, and finishing the safety protection of the power grid.
Specifically, based on the obtained linearity degree set, abnormal linear extraction is performed. The abnormal region is an irregular region caused by scattered strands of the wire, the linear abnormal degree corresponding to the region is large, and the loss gap between the region and the normal metal wire and the metal wire seam region is obvious, so that the abnormal degree of an individual and the whole in the loss set is calculated, and the gap of most of losses of the individual in the linear abnormal degree set is judged. The degree of abnormality of the area to be detected satisfies the relation:
wherein, Represents the/>Abnormality degree of each region to be detected,/>Represents the/>The linear abnormality degree of each region to be detected,/>Representing a set of linear anomalies.
And in response to the abnormality degree being smaller than a preset abnormality degree threshold, marking the region as a power grid fault region, marking the fault region on the power transmission line image, transmitting the region to the unmanned aerial vehicle through 5G/4G short sharing wireless communication, repeatedly shooting the region corresponding to the position in the lead, and uploading the clear image to a power grid database. Illustratively, the preset abnormality degree threshold is set to 0.8.
Thus, the safety protection of the power grid is completed by timely troubleshooting.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (6)
1. The smart grid safety protection method based on short shared power wireless communication is characterized by comprising the following steps:
Acquiring a multi-frame power transmission line gray level diagram, and acquiring a gray level histogram corresponding to the power transmission line gray level diagram;
Taking a gray value histogram of the first frame of power line gray level diagram as a standard histogram;
constructing sliding windows, and calculating the similarity between a gray value histogram and a standard histogram of each sliding window in each frame of the power line gray level diagram from the second frame of the power line gray level diagram to be used as definition evaluation;
Obtaining a power transmission line graph with the highest definition evaluation value;
extracting a connected domain area from the power transmission line graph to serve as an area to be detected;
Fitting pixel points in the to-be-detected area by using a preset model to obtain the linearity degree of the to-be-detected area, thereby obtaining a linearity degree set of all the to-be-detected areas;
calculating the abnormality degree of the region to be detected, and judging whether the region to be detected is a fault region or not:
Responding to the abnormal degree of the area to be detected being smaller than a preset threshold value, judging that the area to be detected is a fault area, and generating and sending a repair detection instruction;
and responding to the abnormality degree of the area to be detected is not smaller than a preset threshold value, judging that the area to be detected is not a fault area, generating and sending a continuous inspection instruction.
2. The smart grid security method based on short shared power wireless communication according to claim 1, wherein the acquiring the multi-frame power line gray level map comprises the steps of:
Converting a multi-frame color image in the power transmission line color video into a gray level image;
Inputting the gray image into a preset segmentation network to obtain a mask image of the gray image;
Setting the pixel value of a power transmission line area in the mask map to be 1, setting the pixel value of an abnormal area to be 0, and obtaining a power transmission line 0-1 map;
And multiplying the gray level image with the power transmission line 0-1 graph to obtain a power transmission line gray level graph.
3. The smart grid security method based on short-sharing power wireless communication according to claim 1, wherein the sharpness evaluation satisfies a polynomial:
wherein, Indicate the corresponding/>, of the sliding windowSimilarity between frame target gray value histogram and standard histogram,/>Representing a standard histogram,/>Indicate the corresponding/>, of the sliding windowTarget gray value histogram of frame,/>Indicate the corresponding/>, of the sliding windowFirst/>, in target gray value histogram of frameIndividual Unit,/>Indicate the corresponding/>, of the sliding windowAverage value of all cells in target gray value histogram of frame,/>Representing the/>, in a standard histogramIndividual Unit,/>Representing the mean of all cells in the standard histogram.
4. The smart grid security protection method based on short-sharing power wireless communication according to claim 1, wherein the step of obtaining a power transmission line graph with the highest definition evaluation value comprises the steps of:
Obtaining the similarity of all frames at the same sliding window position, and constructing a similarity set;
Acquiring a minimum value in the similarity set, and taking a power transmission line gray level diagram corresponding to the minimum value as a power transmission line diagram of the same sliding window position;
traversing the sliding window through the whole power transmission line according to a preset step length and a preset size, thereby obtaining the power transmission line with the highest definition evaluation value.
5. The smart grid security method based on short-sharing power wireless communication according to claim 1, wherein obtaining the linearity degree of the region to be detected comprises the steps of:
randomly selecting two pixel points in the region to be detected, obtaining a fitting straight line by using a least square method, and verifying that the loss function meets the following relation:
wherein, Representing the loss function of a fitted line,/>Representing the/>, within the area to be detectedDistance from each pixel point to a fitting straight line,/>Representing the distance from the pixel point in the region to be detected to the fitting straight line,/>An abscissa value representing the position of a pixel point within the region to be detected,/>Ordinate value representing the position of a pixel point within the region to be detected,/>Parameters representing the fitted line;
when the loss function is minimum, fitting of a fitting straight line is completed;
traversing all pixel points in the area to be detected to obtain a fitting straight line set, and solving a loss value corresponding to each fitting straight line;
And taking the fitting straight line data with the minimum loss value as the linearity degree data of the region to be detected.
6. The smart grid security protection method based on short-sharing power wireless communication according to claim 1, wherein the degree of abnormality of the area to be detected satisfies a relationship:
wherein, Represents the/>Abnormality degree of each region to be detected,/>Represents the/>The linear abnormality degree of each region to be detected,/>Representing a set of linear anomalies.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108765373A (en) * | 2018-04-26 | 2018-11-06 | 西安工程大学 | A kind of insulator exception automatic testing method based on integrated classifier on-line study |
| US20190006847A1 (en) * | 2017-06-29 | 2019-01-03 | Washington State University | Electrical circuit control in power systems |
| JP2020181250A (en) * | 2019-04-23 | 2020-11-05 | 富士通株式会社 | Diffuse case image determination program, system, and method |
| CN113470017A (en) * | 2021-08-31 | 2021-10-01 | 海门裕隆光电科技有限公司 | Power transmission line safety early warning detection method, device and equipment based on artificial intelligence |
| CN115937683A (en) * | 2022-12-21 | 2023-04-07 | 南京理工大学 | High-altitude power transmission line foreign object target detection method and system based on laser obstacle clearing equipment |
| CN117274722A (en) * | 2023-11-21 | 2023-12-22 | 深圳市咏华宇电子有限公司 | Intelligent detection method for distribution box based on infrared image |
| CN117318050A (en) * | 2023-11-28 | 2023-12-29 | 国网湖北省电力有限公司 | Virtual power plant load data prediction method integrating 5G short sharing |
-
2024
- 2024-03-21 CN CN202410322932.1A patent/CN117934458B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190006847A1 (en) * | 2017-06-29 | 2019-01-03 | Washington State University | Electrical circuit control in power systems |
| CN108765373A (en) * | 2018-04-26 | 2018-11-06 | 西安工程大学 | A kind of insulator exception automatic testing method based on integrated classifier on-line study |
| JP2020181250A (en) * | 2019-04-23 | 2020-11-05 | 富士通株式会社 | Diffuse case image determination program, system, and method |
| CN113470017A (en) * | 2021-08-31 | 2021-10-01 | 海门裕隆光电科技有限公司 | Power transmission line safety early warning detection method, device and equipment based on artificial intelligence |
| CN115937683A (en) * | 2022-12-21 | 2023-04-07 | 南京理工大学 | High-altitude power transmission line foreign object target detection method and system based on laser obstacle clearing equipment |
| CN117274722A (en) * | 2023-11-21 | 2023-12-22 | 深圳市咏华宇电子有限公司 | Intelligent detection method for distribution box based on infrared image |
| CN117318050A (en) * | 2023-11-28 | 2023-12-29 | 国网湖北省电力有限公司 | Virtual power plant load data prediction method integrating 5G short sharing |
Non-Patent Citations (2)
| Title |
|---|
| XIANGLING ZHOU.ET.: "Latest Research Progress and Practice of 5G Slicing Technology in China\'s New Power System", 《 2023 8TH ASIA CONFERENCE ON POWER AND ELECTRICAL ENGINEERING (ACPEE)》, 31 May 2023 (2023-05-31), pages 1509 - 1516 * |
| 段巍等: "基于Elman神经网络的风力机风轮子***故障预警", 《太阳能学报》, vol. 42, no. 12, 31 December 2021 (2021-12-31), pages 157 - 162 * |
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