Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Power terminal monitoring method and device based on neural network fault prediction

A power terminal and fault prediction technology, applied in neural learning methods, biological neural network models, measurement devices, etc., can solve problems such as threats to the safe and stable operation of power primary terminal equipment and decline in the reliability of secondary system terminals.

Pending Publication Date: 2020-06-26
STATE GRID ZHEJIANG ELECTRIC POWER CO LTD JIAXING POWER SUPPLY CO +3
View PDF7 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the production and operation of electric power, factors such as equipment quality, human misoperation, and natural disasters inevitably lead to a decrease in the reliability of the terminal operation of the secondary system, which directly threatens the safe and stable operation of the primary terminal equipment of the electric power.

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
  • Power terminal monitoring method and device based on neural network fault prediction
  • Power terminal monitoring method and device based on neural network fault prediction
  • Power terminal monitoring method and device based on neural network fault prediction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0030] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present invention shall have the usual meanings understood by those skilled in the art to which the present disclosure belongs. "First", "second" and similar words used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Comprising" or "comprising" and similar words mean that the elements or items appearing before the word include the elements or items listed after the word and their equivalents, without excluding other elements or items. Words such as "connected" or "connected" are not limited to physical or mechan...

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 discloses a power terminal monitoring method and device based on neural network fault prediction. The method comprises the following steps: inputting state information of each power terminal in a power private network into a convolutional neural network; if it is judged that the prediction result, output by the convolutional neural network, of the power terminal is an automatic recovery type fault, controlling the power terminal to be automatically reset to prevent the fault; if it is judged that the prediction result, output by the convolutional neural network, of the power terminal is a 'failure of an unrecoverable type ' , judging that the power terminal is a failure terminal to be dispatched for maintenance, wherein the convolutional neural network is obtained by pre-training historical state information of each power terminal. By applying the method, the power terminal can respond to the fault timely and quickly when abnormal.

Description

technical field [0001] The invention relates to the technical field of electric power terminal monitoring, in particular to a method and device for electric power terminal monitoring based on neural network fault prediction. Background technique [0002] In the long-term operation of the power grid system, it is inevitable that the performance will gradually decline, the reliability will decrease, and the failure rate will increase, which will endanger the safe operation of the system. In order to ensure the safe and stable operation of the power grid, it is often necessary to monitor the power terminals within the jurisdiction to ensure the normal operation of the power terminals. [0003] The application of new smart grid technology and the standardization and integration of grid equipment management have greatly improved the operation level of grid equipment. In order to assist the operation of primary equipment in the power system, domestic and foreign power companies ha...

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
IPC IPC(8): G06Q10/06G06Q10/00G06Q50/06G06N3/04G06N3/08G01R31/00
CPCG06N3/08G06Q10/06311G06Q10/20G06Q50/06G01R31/00G06N3/045Y04S10/50
Inventor 郑伟军邵炜平陈鼎方景辉吴国庆唐锦江刘哲刘军雨邓伟
Owner STATE GRID ZHEJIANG ELECTRIC POWER CO LTD JIAXING POWER SUPPLY CO
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com