Transformer substation equipment detection method and device based on deep learning

A technology of equipment detection and deep learning, applied in the field of image processing, can solve problems such as heavy workload, long time consumption, and low recognition accuracy

Pending Publication Date: 2020-05-29
ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD +1
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AI Technical Summary

Problems solved by technology

[0006] The invention provides a detection method and device for substation equipment based on deep learning, which solves the problems of using traditional image processing technology

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  • Transformer substation equipment detection method and device based on deep learning
  • Transformer substation equipment detection method and device based on deep learning

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Embodiment Construction

[0038] In order to make the purpose, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the following The described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of the invention. The terms used herein in the description of the present invention are for the purpose of describing specific embodiments only, and are not intended t...

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Abstract

The invention discloses a transformer substation equipment detection method and device based on deep learning. The method comprises the following steps: collecting a plurality of images comprising thephysical opening and closing positions of transformer substation equipment, carrying out the labeling and preprocessing of the images, making a standard data set, and dividing the data set into a training set, a verification set and a test set in proportion; and training a substation equipment detection neural network model by using a py master rcnn deep convolutional network, and testing by using the trained network model to obtain an equipment category and a physical opening and closing position recognition result of the substation equipment. According to the transformer substation equipment detection method and device based on deep learning, the transformer substation equipment category and the physical opening and closing state can be efficiently and automatically detected, the recognition result accuracy is high, and the practicability and operability are high.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method and device for detecting substation equipment based on deep learning. Background technique [0002] In a substation, the status of equipment is one of the key concerns of substation operators in their daily work, and it is closely related to the safe and stable operation of the entire power grid and even the power system. The primary equipment of the substation is a physical carrier that carries high voltage and allows large current to pass through. Its physical opening and closing position is the direct basis for reflecting the working conditions of the connection part of the live primary equipment, mainly including the closing of the outdoor isolating switch and its auxiliary grounding switch. and disconnect position. [0003] In the past operation work, the substation operators mainly checked the position signal uploaded by the auxiliary node of the secondar...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24
Inventor 刘介玮许爱东李烨阳徐传懋
Owner ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD
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