Transformer substation equipment oil leakage detection method and detection system based on deep learning

A technology of deep learning and detection methods, applied in neural learning methods, optical testing flaws/defects, measuring devices, etc., can solve problems such as being easily affected by environmental factors, low inspection accuracy and work efficiency

Inactive Publication Date: 2020-10-27
CHENGDU UNIVERSITY OF TECHNOLOGY +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a deep learning-based oil leakage detection method and detection system for substation equipment to solve the problem that e

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  • Transformer substation equipment oil leakage detection method and detection system based on deep learning
  • Transformer substation equipment oil leakage detection method and detection system based on deep learning
  • Transformer substation equipment oil leakage detection method and detection system based on deep learning

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Embodiment 1

[0045] see figure 1 , an oil leakage detection method for substation equipment based on deep learning. Deep learning is to learn the internal laws and expression levels of sample data. The information obtained during the learning process is of great help to the interpretation of data such as text, images and sounds. . In this way, the robot has the ability to analyze and learn, and can recognize data such as text, images, and sounds. In this embodiment, the main learning is the ability to analyze images.

[0046] The detection method comprises the following steps:

[0047] Step 1: Train the deep convolutional neural network model to obtain the equipment oil leakage defect recognition model. Establish a good model, and then only use direct comparison recognition in the future, which can speed up the recognition speed.

[0048] Specifically, first obtain more than one photo sample containing oil leakage defects of substation equipment, and classify according to the different ...

Embodiment 2

[0077] A preferred embodiment of the present invention provides an intelligent inspection device for substations. The intelligent inspection device for substations in this embodiment includes: a processor, a memory, and a computer program stored in the memory and operable on the processor, such as a A program for a deep learning-based oil leakage detection method for substation equipment.

[0078] In a non-limiting example, a computer program can be divided into one or more modules, and one or more modules are stored in a memory and executed by a processor to implement the present invention. One or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the substation intelligent inspection device. For example, the computer program can be divided into a photo acquisition module, a photo classification module, a model trainin...

Embodiment 3

[0094] see figure 2 , a substation equipment oil leakage detection system based on deep learning, including an electronic device connected to the terminal equipment in the substation, the electronic device includes a photo acquisition module, a model training module, a photo preprocessing module, an equipment oil leakage defect identification module, a photo Classification module and information sending module.

[0095] The model training module is used to train the deep convolutional neural network model to obtain the equipment oil leakage defect recognition model. The model training module includes a photo sample acquisition sub-module, a photo sample classification sub-module, a photo sample preprocessing word module and a model training sub-module.

[0096] The photo sample acquisition sub-module is used to obtain photo samples containing oil leakage defects of substation equipment, and send them to the photo sample classification sub-module. The photo sample classifica...

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Abstract

The invention discloses a transformer substation equipment oil leakage detection method and system based on deep learning and belongs to the technical field of substation equipment. The system comprises a photo acquisition module, a model training module, a photo preprocessing module, an equipment oil leakage defect identification module and an information sending module, wherein the photo acquisition module acquires to-be-detected equipment images sent by terminal equipment, after being preprocessed in the photo preprocessing module, the images are inputted into an equipment oil leakage defect recognition model pre-trained by a model training module to obtain the equipment oil leakage defect information in the images, and then the equipment oil leakage defect information is sent to the terminal equipment through the information sending module to achieve defect recognition of the power equipment. The system is advantaged in that problems that an existing transformer substation intelligent inspection robot is prone to being affected by environmental factors in the inspection process, and consequently inspection accuracy and working efficiency are low are solved.

Description

technical field [0001] The invention relates to the technical field of substation equipment, in particular to a method and system for detecting oil leakage of substation equipment based on deep learning. Background technique [0002] The inspection of substation equipment has always been the core work of the substation in the operation process. It checks the current operation status of the equipment, so as to find the defects in the operation of the equipment at the first time, and then ensure that the equipment can be safe, reliable, stable operation. However, as far as the current inspection work of most substations is concerned, substation inspections rely too much on manual work and substation equipment is prone to aging in harsh environments, so efficient inspections of substations in harsh environments are particularly important. [0003] Traditional substation inspections have the following problems: First, substations located in high-temperature, high-salt, high-hum...

Claims

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Application Information

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IPC IPC(8): G01N21/88G01M3/38G06N3/04G06N3/08
CPCG01N21/8851G01M3/38G06N3/08G01N2021/8883G01N2021/8887G06N3/045
Inventor 杨强张葛祥王焓丁荣海娜
Owner CHENGDU UNIVERSITY OF TECHNOLOGY
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