CN115601707A - Online monitoring method and system for power transmission line of power system - Google Patents
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Abstract
The invention relates to the field of on-line monitoring of power lines, in particular to an on-line monitoring method and system for a power system power transmission line, which are used for monitoring foreign matter invasion of the power transmission line, dividing the environment into a relatively static environment and a relatively dynamic environment according to ambient wind speed, running an image similarity calculation program through a processor carried by a camera in the relatively static environment to judge the foreign matter invasion condition, uploading a monitoring video to a server in the relatively dynamic environment, and judging the foreign matter invasion condition through an image recognition model; therefore, in a relatively static environment, uploading operation of images and running operation of an image recognition model are not needed, electric energy consumption is reduced, and solar energy is better applied to each module of the power transmission line on-line monitoring system.
Description
Technical Field
The invention belongs to the field of on-line monitoring of power lines, and particularly relates to an on-line monitoring method and system for a power transmission line of a power system.
Background
In recent years, with the continuous perfection of the basic design construction and the high-speed development of the economic level in China, the construction of the power transmission line also enters the high-speed development period, but meanwhile, the safe operation of the power transmission line also encounters various challenges. According to statistics, in the reasons of trip accidents of national transmission lines in the last decade, the foreign matter invasion causes a large proportion, the foreign matter invasion refers to the invasion of foreign matters of the transmission lines caused by artificial reasons such as irregular construction of a line channel and the like and the invasion of foreign matters caused by natural reasons such as swinging of trees in the line channel along with wind power, wherein dangerous sources enter the safe distance area of the transmission lines; therefore, the foreign matter invasion accident of the transmission line must be effectively prevented and restrained so as to ensure the safe, stable and reliable transmission of the electric power.
In the prior art, generally, a camera is arranged to acquire image information in real time and identify an image so as to identify a hazard source, for example, a chinese patent of invention (CN 211509200U) discloses an external damage prevention monitoring system for a power transmission line based on an artificial intelligence technology, as shown in fig. 1, the system comprises a video monitoring camera and a monitoring terminal in signal connection with the video monitoring camera, wherein the monitoring terminal comprises a ground device, a graphic processor and a data memory, the video monitoring camera sends a video signal to the graphic processor, processed video data is stored in the data memory, and the graphic processor and the data memory are electrically connected with the ground device; by automatically identifying the video, the real-time monitoring of operating personnel is not needed, the danger can be found in time, the warning information can be pushed in real time, and the safety benefit is remarkable; however, in the above technical solution, real-time images need to be uploaded to the graphics processor, and the graphics processor is executing the image recognition program all the time, so that data transmission is always performed and the image recognition program is always running, and both data uploading and processor running need to consume a large amount of electric energy.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an online monitoring method and system for a power transmission line of a power system aiming at the defects of the technical scheme, which are used for monitoring the foreign matter invasion of the power transmission line, dividing the environment into a relatively static environment and a relatively dynamic environment according to the environment wind speed condition, running an image similarity calculation program through a processor carried by a camera in the relatively static environment to judge the foreign matter invasion condition, uploading a monitoring video to a server in the relatively dynamic environment, and judging the foreign matter invasion condition through an image recognition model; therefore, in a relatively static environment, uploading operation of the image and running operation of the image recognition model are not needed, and energy consumption is reduced.
In order to achieve the above object, according to one aspect of the present invention, an online monitoring method for a power transmission line of a power system is characterized by comprising the following steps:
step 1: collecting a monitoring field video through a camera of an online monitoring system;
the camera is a high pixel monitoring snapshot all-in-one machine, and in order to enable devices such as the camera to work normally in the outdoor independent environment of the power transmission line, the solar cell panel is selected for power generation in the embodiment, and the solar cell panel is considered to be incapable of generating power at night and generate less power in rainy days, and a lithium battery with proper capacity is required to be equipped as a standby battery.
Furthermore, in order to enable the camera to have a better monitoring effect, specific consideration should be given to the installation position of the camera, on one hand, the voltage grades of the power transmission lines are different, and the requirements on the safety distance of the power transmission lines are also different, so that the safety distance from the camera to the power transmission lines needs to be determined, and the normal working operation of the power transmission lines cannot be influenced by the installation of the camera; meanwhile, the installation position of the camera cannot be too high and the installation is too high in consideration of the definition of images shot by the camera, so that the difficulty of image recognition is increased by too far distance of a monitored target object.
And 2, step: the camera carries out preprocessing operation on the collected video image;
due to the influences of field environment, light, weather and the like, some noises are mixed in the acquired field video, so that the video image needs to be preprocessed; the preprocessing operation comprises color conversion, image enhancement and image filtering; the color conversion mainly completes the conversion of the video from a color image to a gray image so as to be beneficial to further image identification; the image enhancement is to enhance the image of the image after conversion, adjust the image contrast, highlight the detail characteristic of the target, the image filtering is to eliminate the noise interference through filtering, raise the accuracy rate of characteristic extraction; in summary, the purpose of image preprocessing is to remove noise in an image, highlight useful information in the image, solve the problems of exposure and focusing during image acquisition, and improve image contrast.
And 3, step 3: the wind speed sensor detects the wind speed grade;
the model of the wind speed sensor is Siemens wind speed sensor QVM62.1, and the wind speed sensor has the characteristics of small volume, simplicity and convenience in installation, high measurement precision, wide range, good stability, strong anti-interference capability and the like, and has good application in national power grids;
and 4, step 4: judging the wind speed grade, and if the wind speed grade is less than 6 grades, performing step 5; if the wind speed grade is greater than 6 grades, executing a step 6;
when the wind speed is less than 6 grades, the main factor causing the foreign matter invasion risk to the power transmission line channel is the phenomenon of nonstandard construction of the power transmission line channel; the foreign matter invasion is in a relatively static environment, the examination of an image recognition system is small, at the moment, whether the foreign matter invasion condition exists can be judged through graph similarity comparison, and the requirement on hardware is low when a similarity comparison program is operated, so that the similarity comparison program can be operated by utilizing a processor of the camera, the data transmission step of uploading video data to a server is avoided, and the energy is saved; when the wind speed is higher than 6 levels, the power transmission line and the surrounding environment, particularly surrounding trees, are in a dynamic process, foreign matter invasion is in the dynamic environment, and the result error obtained by comparing the graph similarity is large, so that whether foreign matter invasion exists is intelligently judged through the image recognition model, the image recognition model needs to run in a large-scale processor, and therefore, video data needs to be uploaded to the server to judge the foreign matter invasion in the dynamic environment;
and 5: converting the video image subjected to the preprocessing operation into a video frame picture by using a processor of the camera, and carrying out similarity calculation on the video frame picture at the current moment and the video frame picture at the previous 10s moment; judging the invasion condition of foreign matters;
specifically, the similarity calculation is carried out through a perceptual hash algorithm, the perceptual hash algorithm can generate a 'fingerprint' character string for each picture, and then the fingerprints are compared to judge the similarity of the two pictures;
further, the step of similarity determination is:
(1) Reducing the size of the video frame picture. The size of the picture to be compared is reduced to 10 × 10, and the scaling is because the resolution of the original picture is generally very high, and the number of pixels is too many, which increases the energy consumption during model operation, so that the video frame picture needs to be scaled very small;
(2) And carrying out gray processing on the video frame picture. Details of the zoomed video frame picture are hidden, but the zoomed video frame picture is not enough because the zoomed video frame picture is colorful, if the RGB value is directly used for comparing the color intensity difference, the calculation is still complex, so that the original picture is converted into a gray image, and the comparison of the three-dimensional space is reduced to one dimension for comparison;
(3) Performing discrete cosine transform, wherein the discrete cosine transform decomposes the video frame picture to obtain a 32x 32 transform coefficient matrix, and the principle of the discrete cosine transform is as follows:
in the formula, F (i, j) is an original video frame picture, F (u, v) is a result after discrete cosine transform, N is a pixel point of the video frame picture, and c (u) and c (v) are compensation coefficients;
(4) Calculating to obtain the mean value of the transformation coefficient matrix;
(5) A hash value is calculated. Performing hash operation on elements in the transformation coefficient matrix to form a 64-bit binary number, wherein the value of the element in the matrix is greater than or equal to the average value calculated in the step 4 and is 1, and the value of the element in the matrix is 0 if the value of the element is less than the average value, and the calculated number is the fingerprint of the video frame picture;
(6) And comparing the similarity of the video frame pictures through the fingerprints in the step 5.
When the preprocessed image is converted into the video frame picture, in order to facilitate the similarity calculation of the subsequent video frame pictures, the preprocessed image is converted into a plurality of video frame pictures in a form of one frame per second; certainly, if more detailed conversion is performed to improve the monitoring precision, for example, the preprocessed image is converted into a plurality of video frame pictures in the form of n frames per second, where n is a natural number greater than or equal to 2;
according to the above description, the processor of the video machine is used to run the similarity comparison program to perform the judgment of the intrusion of the foreign object, which is in the purpose of reducing energy consumption, in the embodiment, the similarity calculation is performed on the video frame picture at the current time and the video frame picture at the previous 10s time; therefore, for the purpose of improving the monitoring precision, similarity calculation can be performed on the video frame picture at the current moment and the video frame picture at the previous n seconds (n is a positive integer less than 10), but in this case, the energy consumption is increased, and therefore, the energy consumption condition of the camera should be considered when determining the n value;
exemplarily, a 9-point 20-20 s video frame picture in the morning is p1, and at this time, the processor of the camera finds out a 9-point 20-10 s video frame picture p2, and calculates the similarity between p1 and p2, so as to perform subsequent determination;
further, the process of determining the foreign object intrusion condition specifically includes: if the similarity of the two video frame pictures is less than 80%, judging that a foreign matter invasion condition possibly exists, and triggering an alarm device by the online monitoring system to remind an operator on duty;
step 6: uploading the video image subjected to the preprocessing operation to a server, operating an image recognition model, and judging the foreign matter invasion condition;
the image recognition model is an R-CNN image recognition model, wherein the R-CNN is a regional image recognition intelligent algorithm framework based on deep learning, and the framework comprises three components, namely a feature extraction part, a region to be selected generation part and a target classification part; the feature extraction part and the candidate region generation part are used for generating a network of the R-CNN-shaped candidate region, and the feature extraction part and the target classification part form an R-CNN detector together; that is to say, the two modules of the generation network of the area to be selected and the R-CNN detector form an R-CNN framework together, and the two modules share the feature extraction convolution layer; the feature extraction of the image data is realized through a convolutional neural network, and the convolutional neural network comprises a convolutional layer, a pooling layer and a normalization layer; the convolution layer processes input image data by utilizing a convolution kernel, so that not only can the expansion of data dimension be realized, but also the characteristics with higher robustness can be learned; in the deep learning process, after the convolution is carried out on input original data, a characteristic response graph is obtained by further carrying out nonlinear processing on an activation function; when a convolution kernel is considered to have a certain characteristic, the convolution result is that the input corresponds to the response of the characteristic; although the local connection and parameter sharing feature in the convolution process can greatly reduce the number of connections between the input and the convolution kernel, the feature map dimension is still large, and needs to be further reduced in the pooling layer. The spatial dimension reduction of the pooling layer is realized by down-sampling, statistical information is extracted in the down-sampling process, the spatial dimension between layers is reduced, and the calculated amount is simplified;
it is worth emphasizing that in the target classification part, an effective object frame is formed by adopting a non-maximum suppression method, then the characteristics of the area where the effective object frame is located are extracted through pooling and pooling treatment, and a target class, namely a boundary frame, is predicted by adopting a prediction function;
specifically, many repeated regions inevitably exist among the candidate regions formed by the candidate region generation network, and in order to remove the repeated regions, the repeated regions are removed according to the score of the intersection ratio in the embodiment; the intersection ratio refers to the overlapping degree between a target candidate region and a real target candidate region generated by an image recognition algorithm, namely the ratio of the intersection area of the two regions to the union area of the two regions; expressed by a mathematical formula as:
in the formula, area (P) represents a target candidate region generated by an image recognition algorithm, and area (G) represents a real target candidate region.
According to another aspect of this application, this application still includes an electric power system transmission line on-line monitoring system, its characterized in that: comprises that
The camera is used for collecting monitoring field videos;
the video image preprocessing module is used for preprocessing the acquired video image by the camera;
the wind speed sensor is used for detecting the wind speed grade;
the processor is used for executing the steps of the online monitoring method for the power system transmission line;
and the server is used for executing the R-CNN image recognition model to judge the foreign matter invasion condition.
Based on the technical scheme, the electric power system transmission line on-line monitoring method and the system have the following technical effects:
the system is used for monitoring the foreign matter invasion of the power transmission line, the environment is divided into a relatively static environment and a relatively dynamic environment according to the environment wind speed condition, an image similarity calculation program is operated by a processor carried by a camera in the relatively static environment to judge the foreign matter invasion condition, in the relatively dynamic environment, a monitoring video is uploaded to a server, and the foreign matter invasion condition is judged through an image recognition model; therefore, in a relatively static environment, uploading operation of images and running operation of an image recognition model are not needed, energy consumption is reduced, and solar energy is better applied to each module of the power transmission line on-line monitoring system.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of a determination scheme for foreign object intrusion in the prior art;
fig. 2 is a flowchart of an online monitoring method for a power transmission line of an electric power system according to an embodiment of the present disclosure;
fig. 3 is a schematic view of a high-pixel monitoring snapshot integrated camera provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The concept to which the present application relates will be first explained below with reference to the drawings. It should be noted that the following descriptions of the concepts are only for the purpose of facilitating understanding of the contents of the present application, and do not represent limitations on the scope of the present application.
The technical problem to be solved by the invention is to provide an on-line monitoring method and system for a power transmission line of an electric power system aiming at the defects of the technical scheme, which are used for monitoring the foreign matter invasion danger of the power transmission line; as shown in fig. 2, an on-line monitoring method for a power transmission line of a power system is characterized by comprising the following steps:
step 1: collecting a monitoring field video through a camera of an online monitoring system;
as shown in fig. 3, the camera is a high-pixel monitoring and capturing integrated camera, and the monitoring data is acquired through a camera; simultaneously, in order to make devices such as camera can normally work in the outdoor independent environment of transmission line, the solar cell panel electricity generation is chooseed for use to this embodiment, considers that solar cell panel can't generate electricity at night and the rainy weather produces the less condition of electric power, still must be equipped with the lithium cell of suitable capacity for it as backup battery, and simultaneously, this camera has still integrateed the treater for handle monitoring data.
Furthermore, in order to enable the camera to have a better monitoring effect, specific consideration should be given to the installation position of the camera, on one hand, the voltage grades of the power transmission lines are different, and the requirements on the safety distance of the power transmission lines are also different, so that the safety distance from the camera to the power transmission lines needs to be determined, and the normal working operation of the power transmission lines cannot be influenced by the installation of the camera; meanwhile, the installation position of the camera cannot be too high and the installation is too high in consideration of the definition of images shot by the camera, so that the difficulty of image identification is increased due to too far distance of a monitored target object.
Step 2: the camera carries out preprocessing operation on the collected video image;
influenced by the field environment, light, weather and the like, some noises are mixed in the collected field video, so that the video image needs to be preprocessed; the preprocessing operation comprises color conversion, image enhancement and image filtering; the color conversion mainly completes the conversion of the video from a color image to a gray image so as to be beneficial to further image identification; the image enhancement is to enhance the image of the converted image, adjust the image contrast and highlight the detail features of the target, and the image filtering is to eliminate noise interference and improve the accuracy of feature extraction through filtering; in summary, the purpose of image preprocessing is to remove noise in an image, highlight useful information in the image, solve the problems of exposure and focusing during image acquisition, and improve image contrast.
And 3, step 3: the wind speed sensor detects the wind speed grade;
the model of the wind speed sensor is Siemens wind speed sensor QVM62.1, and the wind speed sensor has the characteristics of small volume, simplicity and quickness in installation, high measurement precision, wide range, good stability, strong anti-interference capability and the like, and has good application in national power grids;
and 4, step 4: judging the wind speed grade, and if the wind speed grade is less than 6 grades, performing step 5; if the wind speed grade is greater than 6 grades, executing a step 6;
when the wind speed is less than 6 grades, the main factor causing the foreign matter invasion risk to the power transmission line channel is the phenomenon of nonstandard construction of the power transmission line channel; the foreign matter invasion is in a relatively static environment, the examination on an image recognition system is small, at the moment, whether the foreign matter invasion condition exists or not can be judged through graph similarity comparison, and the requirement on hardware for operating a similarity comparison program is low, so that the similarity comparison program can be operated by utilizing a processor of the camera, the data transmission step of uploading video data to a server is avoided, and the energy is saved; when the wind speed is higher than 6 levels, the transmission line and the surrounding environment, particularly surrounding trees, are in a dynamic process, the foreign matter invasion is in the dynamic environment, and the result error obtained by comparing the graph similarity is large, so that whether the foreign matter invasion exists or not is intelligently judged through the image recognition model, the image recognition model needs to run in a large-scale processor, and therefore, the video data needs to be uploaded to a server to judge the foreign matter invasion in the dynamic environment;
and 5: converting the video image subjected to the preprocessing operation into a video frame picture by using a processor of the camera, and carrying out similarity calculation on the video frame picture at the current moment and the video frame picture at the previous 10s moment; judging the invasion condition of the foreign matters;
specifically, the similarity calculation is carried out through a perceptual hash algorithm, the perceptual hash algorithm can generate a 'fingerprint' character string for each picture, and then the fingerprints are compared to judge the similarity of the two pictures;
further, the step of judging the similarity includes:
(1) Reducing the size of the video frame picture. The size of the picture to be compared is reduced to 10 × 10, and the scaling is because the resolution of the original picture is generally very high, and the number of pixels is too many, which increases the energy consumption during model operation, so that the video frame picture needs to be scaled very small;
(2) And carrying out gray processing on the video frame picture. Details of the zoomed video frame picture are hidden, but the zoomed video frame picture is not enough because the zoomed video frame picture is colorful, if the RGB value is directly used for comparing the color intensity difference, the calculation is still complex, so that the original picture is converted into a gray image, and the comparison of the three-dimensional space is reduced to one dimension for comparison;
(3) Performing discrete cosine transform, wherein the discrete cosine transform decomposes the video frame picture to obtain a 32x 32 transform coefficient matrix, and the principle of the discrete cosine transform is as follows:
in the formula, F (i, j) is an original video frame picture, F (u, v) is a result after discrete cosine transform, N is a pixel point of the video frame picture, and c (u) and c (v) are compensation coefficients;
(4) Calculating to obtain the mean value of the transformation coefficient matrix;
(5) A hash value is calculated. Performing hash operation on elements in the transformation coefficient matrix to form a 64-bit binary number, wherein the value of the element in the matrix is greater than or equal to the average value calculated in the step 4 and is 1, and the value of the element in the matrix is 0 if the value of the element is less than the average value, and the calculated number is the fingerprint of the video frame picture;
(6) And comparing the similarity of the video frame pictures through the fingerprints in the step 5.
When the preprocessed image is converted into the video frame picture, in order to facilitate the similarity calculation of the subsequent video frame picture, the preprocessed image is converted into a plurality of video frame pictures according to the form of one frame per second; of course, if the monitoring precision is improved, more detailed conversion may be performed, for example, the preprocessed image is converted into a plurality of video frame pictures in the form of n frames per second, where n is a natural number greater than or equal to 2;
according to the above description, the processor of the video machine is used to run the similarity comparison program to perform the judgment of the intrusion of the foreign object, which is in the purpose of reducing energy consumption, in the embodiment, the similarity calculation is performed on the video frame picture at the current time and the video frame picture at the previous 10s time; therefore, for the purpose of improving the monitoring precision, similarity calculation can be performed on the video frame picture at the current moment and the video frame picture at the previous n seconds (n is a positive integer less than 10), but in this case, the energy consumption is increased, and therefore, the energy consumption condition of the camera should be considered when determining the n value;
exemplarily, a 9-point 20-20 s video frame picture in the morning is p1, and at this time, the processor of the camera finds out a 9-point 20-10 s video frame picture p2, and calculates the similarity between p1 and p2, so as to perform subsequent determination;
furthermore, the process of determining the intrusion condition of the foreign object specifically includes: if the similarity of the two video frame pictures is less than 80%, judging that a foreign matter invasion condition possibly exists, and triggering an alarm device by an online monitoring system to remind an attendant;
and 6: uploading the video image subjected to the preprocessing operation to a server, operating an image recognition model, and judging the foreign matter invasion condition;
the image recognition model is an R-CNN image recognition model, wherein the R-CNN is a regional image recognition intelligent algorithm framework based on deep learning, and the framework comprises three components, namely a feature extraction part, a region to be selected generation part and a target classification part; the feature extraction part and the candidate region generation part are used for generating a network of the R-CNN-shaped candidate region, and the feature extraction part and the target classification part form an R-CNN detector together; that is to say, the two modules of the generation network of the area to be selected and the R-CNN detector form an R-CNN framework together, and the two modules share the feature extraction convolution layer; the feature extraction of the image data is realized through a convolutional neural network, and the convolutional neural network comprises a convolutional layer, a pooling layer and a normalization layer; the convolution layer processes input image data by utilizing a convolution kernel, so that not only can the expansion of data dimension be realized, but also the characteristics with higher robustness can be learned; in the deep learning process, after the convolution is carried out on input original data, a characteristic response graph is further obtained by carrying out nonlinear processing on an activation function; when a convolution kernel is considered to have a certain characteristic, the convolution result is that the input corresponds to the response of the characteristic; although the local connection and parameter sharing characteristics in the convolution process can greatly reduce the number of connections between the input and the convolution kernel, the dimension of the feature map is still large, and needs to be further reduced in the pooling layer. The spatial dimension reduction of the pooling layer is realized by down-sampling, statistical information is extracted in the down-sampling process, the spatial dimension between layers is reduced, and the calculated amount is simplified;
it is worth emphasizing that in the target classification part, an effective object frame is formed by adopting a non-maximum suppression method, then the characteristics of the area where the effective object frame is located are extracted through pooling and pooling treatment, and a prediction function is adopted to predict a target category, namely a boundary frame;
specifically, many repeated regions inevitably exist among the regions to be selected generated by the network for generating the regions to be selected, and in order to remove the repeated regions, the repeated regions are removed according to the score of the intersection ratio in the embodiment; the intersection ratio refers to the overlapping degree between a target candidate region and a real target candidate region generated by an image recognition algorithm, namely the ratio of the intersection area of the two regions to the union area of the two regions; expressed as follows by the mathematical formula:
in the formula, area (P) represents a target candidate region generated by an image recognition algorithm, and area (G) represents a real target candidate region.
According to another embodiment of the present application, the present application further includes an on-line monitoring system for power transmission lines of an electric power system, which is characterized in that: comprises that
The camera is used for collecting monitoring field videos;
the video image preprocessing module is used for preprocessing the acquired video image by the camera;
the wind speed sensor is used for detecting the wind speed grade;
the processor is used for executing the steps of the online monitoring method for the power system transmission line;
and the server is used for executing the R-CNN image recognition model to judge the foreign matter invasion condition.
The above-described embodiments and/or implementations are only for illustrating the preferred embodiments and/or implementations of the present technology, and are not intended to limit the implementations of the present technology in any way, and those skilled in the art can make many modifications or changes without departing from the scope of the technology disclosed in the present disclosure, but should be construed as technology or implementations that are substantially the same as the present technology.
Claims (10)
1. An on-line monitoring method for a power transmission line of an electric power system comprises the following steps:
step 1: collecting a monitoring field video through a camera of an online monitoring system;
step 2: the camera carries out preprocessing operation on the acquired video image;
and step 3: the wind speed sensor detects the wind speed grade;
and 4, step 4: judging the wind speed grade, and if the wind speed grade is less than 6 grades, performing step 5; if the wind speed grade is greater than 6 grades, executing a step 6;
and 5: converting the video image subjected to the preprocessing operation into a video frame picture by using a processor of the camera, and performing similarity calculation on the video frame picture at the current moment and the video frame picture at the previous 10s moment; judging the invasion condition of foreign matters;
and 6: and uploading the video image subjected to the preprocessing operation to a server, operating an image recognition model, and judging the foreign matter invasion condition.
2. The on-line monitoring method for the power system transmission line according to claim 1, wherein the camera is a high-pixel monitoring and snapshot integrated camera, a solar panel is selected for power generation, and a lithium battery is further provided as a standby battery.
3. The on-line monitoring method for the power system transmission line of the claim 1 is characterized in that the preprocessing operation comprises color conversion, image enhancement and image filtering; the color conversion mainly completes the conversion of the video from a color image to a gray image; the image enhancement is to enhance the image of the converted image, adjust the image contrast and highlight the detail features of the target, and the image filtering is to eliminate noise interference and improve the accuracy of feature extraction through filtering.
4. The on-line monitoring method for the power system transmission line according to claim 1, wherein the type of the wind speed sensor is siemens wind speed sensor QVM62.1.
5. The on-line monitoring method for power transmission line of power system of claim 1,
in the step 4: when the wind speed is less than 6 grades, defining the environment at the moment as a relatively static environment, and when the wind speed is more than 6 grades, defining the environment at the moment as a relatively dynamic environment; and according to different wind speeds, different methods are selected to judge the foreign matter invasion.
6. The on-line monitoring method for the power system transmission line according to claim 5, wherein the step 5 is specifically: when the video camera is in a relatively static environment, the processor of the video camera is utilized to judge whether a foreign matter invasion condition exists or not through comparison of picture similarity, and when the video camera is in a dynamic environment, the preprocessed video data is uploaded to a server to judge the foreign matter invasion.
7. The on-line monitoring method for power transmission line of power system of claim 6,
the similarity comparison and judgment steps are as follows:
(1) Reducing the size of the video frame picture, and reducing the picture to be compared to the size of 10 × 10;
(2) Carrying out gray processing on the video frame picture;
(3) Performing discrete cosine transform, wherein the discrete cosine transform decomposes the video frame picture to obtain a 32x 32 transform coefficient matrix, and the principle of the discrete cosine transform is as follows:
in the formula, F (i, j) is an original video frame picture, F (u, v) is a result after discrete cosine transform, N is a pixel point of the video frame picture, and c (u) and c (v) are compensation coefficients;
(4) Calculating to obtain the mean value of the transformation coefficient matrix;
(5) Calculating a hash value, performing hash operation on elements in the transformation coefficient matrix to form a 64-bit binary number, wherein the element value in the matrix is greater than or equal to the average value calculated in the step 4 and is 1, and the average value is 0 if the element value is smaller than the average value, and the calculated number is the fingerprint of the video frame picture;
(6) And comparing the similarity of the video frame pictures through the fingerprints in the step 5.
8. The on-line monitoring method for the power transmission line of the power system as claimed in claim 1, wherein in the step 5, if the similarity between the two video frame pictures is less than 80%, it is determined that a foreign object invasion condition may exist, and the on-line monitoring system triggers an alarm device to remind an operator on duty.
9. The on-line monitoring method for the power system transmission line of the claim 1 is characterized in that the image recognition model is an R-CNN image recognition model, comprising three components of a feature extraction part, a candidate region generation part and a target classification part;
forming an effective object frame in the target classification part by adopting a non-maximum suppression method, then performing pooling treatment to extract the characteristics of the region where the effective object frame is located, and predicting the target category, namely a boundary frame by adopting a prediction function;
many repeated regions exist among the regions to be selected formed by the network generated by the regions to be selected, so that the repeated regions are removed according to the score of the intersection ratio (IOU); expressed as follows by the mathematical formula:
in the formula, area (P) represents a target candidate region generated by an image recognition algorithm, and area (G) represents a real target candidate region.
10. The utility model provides an electric power system transmission line on-line monitoring system which characterized in that: comprises that
The camera is used for collecting monitoring field videos;
the video image preprocessing module is used for preprocessing the acquired video image by the camera;
the wind speed sensor is used for detecting the wind speed grade;
a processor for performing the steps of the power system transmission line on-line monitoring method of any one of claims 1-9;
and the server is used for executing the R-CNN image recognition model to judge the foreign matter invasion condition.
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