Unlock instant, AI-driven research and patent intelligence for your innovation.

A non-invasive fault arc monitoring method based on convolutional neural network

A convolutional neural network and fault arc technology, applied in the field of data identification, can solve the problems of high installation cost and complicated installation, and achieve the effects of low cost, high monitoring efficiency and simple installation

Active Publication Date: 2022-07-05
杭州拓深科技有限公司
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The invention solves the problem of high installation cost and complicated installation in the method of fault arc judgment mainly adopting the method of installing fault arc monitoring equipment on the line in the prior art, and provides an optimized non-invasive method based on convolutional neural network Arc Fault Monitoring Method

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A non-invasive fault arc monitoring method based on convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] The present invention will be further described in detail below with reference to the embodiments, but the protection scope of the present invention is not limited thereto.

[0053] The invention relates to a non-invasive fault arc monitoring method based on a convolutional neural network, which comprises the following steps.

[0054] Step 1: Divide the monitoring area and initialize it; start to collect the line video within the monitoring range.

[0055] In the step 1, dividing the monitoring area includes the following steps:

[0056] Step 1.1: Confirm that any monitoring screen capture device monitors all lines in the space; if there is a monitoring blind area, add monitoring screen capture devices or re-divide the monitoring area corresponding to any monitoring screen capture device in the same space;

[0057] Step 1.2: Carry out initial image acquisition for the monitoring area corresponding to any monitoring screen acquisition device, so that the whole is used a...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a non-invasive fault arc monitoring method based on a convolutional neural network. The monitoring area is divided, and after initialization, the line video within the monitoring range starts to be collected. Synchronize and confirm the abnormal area. After confirming the abnormal area of ​​N frames of video images, input all the processed video images into the convolutional neural network to extract and classify the abnormal area. , then an alarm is issued, and the current can be cut off by confirming the position where the power supply corresponding to the current fault arc can be cut off. The invention has low cost, only needs to install a conventional sampling camera, is simple to install, convenient to debug, does not need to use manual selection of features, adopts computer automatic processing, has high processing speed and high monitoring efficiency.

Description

technical field [0001] The invention belongs to the technical fields of data identification; data representation; record carriers; and processing of record carriers, in particular to a non-invasive fault arc monitoring method based on a convolutional neural network. Background technique [0002] The basic components of the electrical fire monitoring system include electrical fire monitoring equipment, residual current electrical fire monitoring detectors and temperature measuring electrical fire monitoring detectors, which can monitor the current, residual current and temperature in the protected line and detect them in time. Electrical fire hazards, prevent electrical fires. [0003] But in fact, many serious fire accidents are simply caused by fault arcs in the line that are lower than the rated current or expected short-circuit current. These dangerous arcs may occur in unreasonably designed or aging power supply lines, electrical plugs and household appliances. Insulati...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/40G06V10/25G01R31/12
CPCG01R31/1218G06V20/40G06V10/25
Inventor 梁昆傅一波张轩铭王利强钱伟
Owner 杭州拓深科技有限公司
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com