CN114429421A - Method for detecting state of disconnecting link facing scheduling service scene - Google Patents
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Abstract
The invention relates to a method for detecting the state of a switch facing a scheduling service scene, which comprises the following steps: and reading the state of the disconnecting link by a sensor, identifying the state of the disconnecting link by an image, comparing the states and the like. The method for detecting the state of the disconnecting link facing to the dispatching service scene has high correct recognition rate, relatively low calculation complexity and high operation efficiency, so that the method can be rapidly deployed in the power industry in large quantities, and the intelligent level of power inspection work can be effectively improved.
Description
Technical Field
The invention relates to a method for detecting the state of a disconnecting link facing a scheduling service scene, and belongs to the technical field of power equipment.
Background
With the continuous promotion of electric power construction work, the scale of the power grid in China is continuously enlarged. The method also brings huge pressure to the operation and maintenance work of the power transformation equipment while promoting the economic construction process of China. On the other hand, as robotics and cameras have grown mature, remote camcorder systems based on robots and cameras have been successfully applied to power patrol work. However, due to the lack of a matched intelligent processing technology, the video images obtained by inspection still need to be manually analyzed, the method is not only low in efficiency, but also low in accuracy, easily causes missing inspection and false inspection, and cannot deal with increasingly severe operation and maintenance pressure.
At present, most transformer substations acquire the equipment state by acquiring the sensor information of the equipment state, and the method has certain false detection and easily causes electric power hidden trouble. The method for analyzing the equipment state of the transformer substation power equipment images shot by the camera by adopting an artificial intelligence analysis method is developed in some transformer substations, but the analysis result is not comprehensively distinguished from the analysis result of the intelligent power grid dispatching control system D5000.
Disclosure of Invention
The invention provides a switch state detection method for a scheduling service scene, which is used for carrying out double judgment and analysis on the state of routing inspection equipment and improving the accuracy of equipment state judgment.
In order to solve the technical problems, the technical scheme provided by the invention is as follows: a method for detecting the state of a switch facing a scheduling service scene comprises the following steps:
step S1, at the station end, the sensor corresponding to the disconnecting link transmits the switch state corresponding to the current disconnecting link to the intelligent power grid dispatching control system at the main station end through the network;
step S2, the station side acquires a disconnecting link image through a camera, then analyzes the current disconnecting link state through a method based on a deep neural network and deployed on an intelligent analysis device, and uploads the disconnecting link image and the current disconnecting link state obtained through analysis to a unified video monitoring platform of the main station side through a network;
step S3, comparing the switch state of the disconnecting link obtained by the intelligent power grid dispatching control system with the current disconnecting link state obtained by the unified video monitoring platform, if the two states are consistent, returning the current state to the intelligent power grid dispatching control system and displaying; and if the two states are not consistent, manually checking the disconnecting link image, confirming the state of the disconnecting link, and returning the state of the disconnecting link to the intelligent power grid dispatching control system for displaying.
The scheme is further improved in that: the method for using the deep neural network in the step S2 includes the following steps:
step 1, collecting a power inspection video image of power equipment; the power equipment comprises a single-arm folding type isolating switch, a double-column rotary type isolating switch and a three-column type isolating switch; a power patrol video image at least comprises a power device;
step 2, labeling the power patrol video image, wherein the labeling information comprises a target frame and a category label;
step 3, preparing a training data set and a testing data set before training the target detection network model; randomly dividing a training data set and a testing data set;
step 4, preprocessing the training data set; normalizing the size, and acquiring target images at different angles by adopting a data augmentation mode;
step 7, training a neural network to obtain a target detection network model;
step 8, testing the detection performance of the target detection network model through the test data set, and if the detection performance requirement is not met, continuing training on the training data set until the requirement is met; the detection performance takes the mAP value as a measurement index, and takes 0.6 as a qualified standard.
The scheme is further improved in that: the target detection network model is a YOLOV3 target detection network model.
The scheme is further improved in that: the size normalization is a uniform normalization of the images in the training dataset to 300 x 300 pixels.
The scheme is further improved in that: the data augmentation is by rotating the images in the training dataset 90 °, 180 ° and 270 ° clockwise.
The scheme is further improved in that: the intelligent power grid dispatching control system is a D5000 system.
The method for detecting the state of the disconnecting link facing the scheduling service scene, provided by the invention, is used for detecting and identifying typical power equipment in a power inspection video image by applying a deep learning theory and utilizing a deep convolutional neural network with better performance in the industry at present, and can effectively solve the problems of low detection performance, low operation efficiency and the like of other target detection methods; the state of the disconnecting link is comprehensively analyzed by adopting the disconnecting link state detection based on video analysis and the disconnecting link state detection based on sensor information, so that the state of the disconnecting link is dualized and judged in a scheduling service scene; the method has the advantages of high correct recognition rate, relatively low calculation complexity, high operation efficiency and low requirement on computer hardware, so that the method can be rapidly and massively deployed in the power industry, and the intelligent level of power inspection work can be effectively improved.
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The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a schematic view of a preferred embodiment of the present invention.
Detailed Description
Examples
As shown in fig. 1, the method for detecting a state of a switch facing a scheduling service scenario, provided by this embodiment, includes the following steps:
step S1, at the station end, the sensor corresponding to the disconnecting link transmits the switch state corresponding to the current disconnecting link to the intelligent power grid dispatching control system at the main station end, namely a D5000 system, through the network;
step S2, the station side acquires a disconnecting link image through a camera, then analyzes the current disconnecting link state through a method based on a deep neural network and deployed on an intelligent analysis device, and uploads the disconnecting link image and the current disconnecting link state obtained through analysis to a unified video monitoring platform of the main station side through a network;
step S3, comparing the switch state of the disconnecting link obtained by the intelligent power grid dispatching control system with the current disconnecting link state obtained by the unified video monitoring platform, if the two states are consistent, returning the current state to the intelligent power grid dispatching control system and displaying; and if the two states are not consistent, manually checking the disconnecting link image, confirming the state of the disconnecting link, and returning the state of the disconnecting link to the intelligent power grid dispatching control system for displaying.
Further, the method using the deep neural network in step S2 includes the following steps:
step 1, collecting a power inspection video image of power equipment; when the power patrol video image is collected, the image is kept clear and the power equipment is highlighted; the power equipment comprises a single-arm folding type isolating switch, a double-column rotary type isolating switch and a three-column type isolating switch; a power patrol video image at least comprises a power device;
step 2, labeling the power patrol video image, wherein the labeling information comprises a target frame and a category label;
step 3, preparing a training data set and a testing data set before training a YOLOV3 target detection network model; randomly dividing a training data set and a testing data set; the images contained in the two images are independent of each other and the quantity ratio is 17: 3;
step 4, preprocessing the training data set; size normalization, wherein the images are uniformly normalized to 300 × 300 pixels; data augmentation, including generating copies of the original image after clockwise rotation by 90 °, 180 ° and 270 °;
step 7, training a neural network to obtain a target detection network model; the Yolov3 target detection network model is obtained based on an electric power map database established by screening electric power inspection video images and a Yolov3 neural network training, and specifically comprises the following steps:
firstly, initializing each parameter of a YOLOV3 target detection network;
thirdly, training the network by adopting a channel sparse regularization method on the BN layer;
then, cutting the training network channel through a small-size factor;
and finally, fine-tuning the cut network to obtain a trained network model. Repeating the above steps for multiple times until the trained network model reaches a set compression threshold value;
step 8, testing the detection performance of the target detection network model through the test data set, and if the detection performance requirement is not met, continuing training on the training data set until the requirement is met; the detection performance takes the mAP value as a measurement index, and takes 0.6 as a qualified standard. That is, a value of 0.6 or more is acceptable.
In conclusion, the invention aims at the intelligent identification problem of the routing inspection video image in the power scene, establishes a high-quality power picture database, and trains and obtains a target detection model capable of identifying a typical power device in an input picture by taking a YOLOV3 network as a core on the basis. The experimental result on the test set shows that the method has higher recognition rate for the typical power device, can be applied to the field of power inspection video analysis, and has strong feasibility and practicability.
The present invention is not limited to the above embodiments, and any technical solutions formed by equivalent substitutions fall within the scope of the claims of the present invention.
Claims (6)
1. A method for detecting the state of a switch facing a scheduling service scene is characterized by comprising the following steps:
step S1, at the station end, the sensor corresponding to the disconnecting link transmits the switch state corresponding to the current disconnecting link to the intelligent power grid dispatching control system at the main station end through the network;
step S2, the station side acquires a disconnecting link image through a camera, then analyzes the current disconnecting link state through a method based on a deep neural network and deployed on an intelligent analysis device, and uploads the disconnecting link image and the current disconnecting link state obtained through analysis to a unified video monitoring platform of the main station side through a network;
step S3, comparing the switch state of the disconnecting link obtained by the intelligent power grid dispatching control system with the current disconnecting link state obtained by the unified video monitoring platform, if the two states are consistent, returning the current state to the intelligent power grid dispatching control system and displaying; and if the two states are not consistent, manually checking the disconnecting link image, confirming the state of the disconnecting link, and returning the state of the disconnecting link to the intelligent power grid dispatching control system for displaying.
2. The method for detecting the state of the switch facing the scheduling service scenario according to claim 1, wherein: the method for using the deep neural network in the step S2 includes the following steps:
step 1, collecting a power inspection video image of power equipment; the power equipment comprises a single-arm folding type isolating switch, a double-column rotary type isolating switch and a three-column type isolating switch; a power patrol video image at least comprises a power device;
step 2, labeling the power patrol video image, wherein the labeling information comprises a target frame and a category label;
step 3, preparing a training data set and a testing data set before training the target detection network model; randomly dividing a training data set and a testing data set;
step 4, preprocessing the training data set; normalizing the size, and acquiring target images at different angles by adopting a data augmentation mode;
step 7, training a neural network to obtain a target detection network model;
step 8, testing the detection performance of the target detection network model through the test data set, and if the detection performance requirement is not met, continuing training on the training data set until the requirement is met; the detection performance takes the mAP value as a measurement index, and takes 0.6 as a qualified standard.
3. The method for detecting the state of the switch facing the scheduling service scenario according to claim 2, wherein: the target detection network model is a YOLOV3 target detection network model.
4. The method for detecting the state of the switch facing the scheduling service scenario according to claim 2, wherein: the size normalization is a uniform normalization of the images in the training dataset to 300 x 300 pixels.
5. The method for detecting the state of the switch facing the scheduling service scenario according to claim 2, wherein: the data augmentation is by rotating the images in the training dataset 90 °, 180 ° and 270 ° clockwise.
6. The method for detecting the state of the switch facing the scheduling service scenario according to claim 1, wherein: the intelligent power grid dispatching control system is a D5000 system.
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