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MaskRCNN-based substation equipment anomaly recognition and positioning method and system

A technology for substation equipment and abnormal recognition, which is applied in the field of image recognition and power system, can solve the problems that the industrial application level cannot be reached, and the accuracy rate of traditional recognition methods is not high, so as to improve fault tolerance, accuracy and positioning recognition Effect

Pending Publication Date: 2021-02-26
SHANGHAI HENGNENGTAI ENTERPRISE MANAGEMENT CO LTD PUNENG ELECTRIC POWER TECH BRANCH +1
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AI Technical Summary

Problems solved by technology

[0002] Traditional image recognition methods perform well on large sample data sets, but at present there are few image samples of power equipment, the accuracy rate of traditional recognition methods is not high, and it cannot reach the level of industrial application, but power transformation equipment has rich correlations, and specific types of The probability of equipment appearing at the same time is high. If this correlation is introduced at the same time as image recognition, the recognition accuracy can be greatly improved, making industrial applications possible.

Method used

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  • MaskRCNN-based substation equipment anomaly recognition and positioning method and system
  • MaskRCNN-based substation equipment anomaly recognition and positioning method and system
  • MaskRCNN-based substation equipment anomaly recognition and positioning method and system

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Embodiment 1

[0030] refer to figure 1 and figure 2 , which is the first embodiment of the present invention, provides a MarkRCNN-based abnormal identification and positioning method for substation equipment, including:

[0031] S1: Collect real-time data of power equipment for preprocessing.

[0032] S2: Use the iForest algorithm to identify the outliers of the preprocessed operating data, and combine the outliers marked by the K-means clustering strategy. What needs to be explained in this step is that the identification of marked outliers includes:

[0033] Random sampling of running data;

[0034] Define the division dimension and place the running data in the dimension smaller than the division point on the left side of the current node, and the operation data greater than the division point on the right side;

[0035] Iterate in a loop until the running data can no longer be divided;

[0036] Use the K-means clustering strategy to select k points as the initial centroid and calc...

Embodiment 2

[0075] refer to image 3, which is the second embodiment of the present invention. This embodiment is different from the first embodiment in that it provides a MarkRCNN-based abnormal identification and positioning system for substation equipment, including:

[0076] The identification collection module 100 is used to collect image information and correlation information between substation equipment, and obtain historical operation data and real-time operation data of the substation equipment.

[0077] The data processing center module 200 is connected to the collection module 100, and is used for receiving, calculating, storing, and outputting data information to be processed. It includes a computing unit 201, a database 202, and an input-output management unit 203. The computing unit 201 is connected to the collection module 100 , used to receive the data information acquired by the information collection module 100 to perform identification, positioning, operation processin...

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Abstract

The invention discloses a MarkRCNN-based substation equipment anomaly recognition and positioning method and system. The method comprises the steps: collecting real-time data of power equipment, and carrying out the preprocessing; recognizing an abnormal value of the preprocessed operation data by using an iForest algorithm, and marking the abnormal value in combination with a Kmeans clustering strategy; constructing a Mask RCNN target recognition network model based on a convolutional neural network; inputting the marked abnormal value into the Mask RCNN target recognition network model for preliminary recognition, and outputting a target recognition result; training and parameter optimization are carried out on the LSSVM, precision requirements and threshold values are set, and a positioning model is output after training is completed; and importing the target recognition result into the positioning model to obtain abnormal position information of the power equipment. According to the invention, while the image recognition accuracy of the power transformation equipment is greatly improved, the positioning recognition of the abnormal position of the equipment is improved, and thefault-tolerant capability, the positioning efficiency and the accuracy of the fault positioning information are improved.

Description

technical field [0001] The present invention relates to the technical field of electric power system and image recognition, in particular to a MarkRCNN-based abnormal identification and positioning method and system for substation equipment. Background technique [0002] Traditional image recognition methods perform well on large sample data sets, but at present there are few image samples of power equipment, the accuracy rate of traditional recognition methods is not high, and it cannot reach the level of industrial application, but power transformation equipment has rich correlations, and specific types of The probability of devices appearing at the same time is high. If this correlation is introduced at the same time as image recognition, the recognition accuracy can be greatly improved, making industrial applications possible. [0003] There are mainly two types of location methods based on fault information collected by field devices: a. Matrix method: a unified matrix ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/20G06K9/62G06N3/04
CPCG06V10/22G06N3/045G06F18/23213G06F18/214
Inventor 罗小山朱骏陆爽朱亚吴斌周宇星费晓亮
Owner SHANGHAI HENGNENGTAI ENTERPRISE MANAGEMENT CO LTD PUNENG ELECTRIC POWER TECH BRANCH
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