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

Distribution network high-fault region identification method

An identification method and big data technology, applied in data processing applications, structured data retrieval, electrical digital data processing, etc., can solve problems such as inability to assist decision-making in distribution network dispatching, inspection, coarse granularity, etc.

Active Publication Date: 2017-02-01
STATE GRID JIANGSU ELECTRIC POWER CO ELECTRIC POWER RES INST +1
View PDF3 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The granularity of these statistical results is relatively coarse, which cannot provide effective auxiliary decision-making for distribution network dispatching and inspection.

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
  • Distribution network high-fault region identification method
  • Distribution network high-fault region identification method
  • Distribution network high-fault region identification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0060] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0061] see figure 1 , based on the identification method of high-incidence areas of distribution network faults based on big data, fuzzy positioning of distribution network faults is carried out, and the power outage loss caused by faults is accurately evaluated; on this basis, distribution network faults are mapped with geographic grids to realize The fault loss value of the geographical grid is quantified cumulatively, and the geographical grid is rendered by the color spot map for prompting, providing accurate and customized auxiliary decision-making support for upgrading, reforming, strengthening special patrols, etc. for dispatching, transportation inspection and other departments.

[0062] In order to use this method effectively, it should b...

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 distribution network high-fault region identification method. The method comprises the following steps: performing fuzzy identification on distribution network fault positions on the basis of multisource data; based on historical load data and meteorological monitoring data, performing distribution network fault power failure load loss evaluation; performing grid division on a geological view and realizing mapping between faults and geological grids; calculating a fault influence statistical index of each geological grid in a given time period and quantifying fault influences in each grid area; according to each fault influence statistical index, performing coloring so as to generate a fault influence color spot graph; and bringing forward corresponding auxiliary decision-making advice to a high-fault region. According to the invention, power failure loss quantification and fuzzy positioning are performed on vast distribution network faults, the mapping is performed with the geological grids, the fault influences of the geological grids are statistically analyzed, and the fault influence color spot graph is formed, such that refined and customized auxiliary decision-making advance is provided for distribution network scheduling, operation and maintenance.

Description

technical field [0001] The invention relates to a big data-based identification method for high-occurrence areas of distribution network faults, which belongs to the field of distribution network security and reliability analysis. Background technique [0002] In the traditional distribution network fault management work, it is often only for a single fault to be judged, and on this basis, the number of distribution network faults in the regional dimension (province, city, county) and time dimension (year, month, day) is counted. The granularity of these statistical results is relatively coarse, which cannot provide effective auxiliary decision-making for distribution network dispatching and inspection. [0003] The occurrence of a single power grid failure has a certain degree of contingency. However, over a long period of time, the failure rate of the area with a weak distribution network frame or backward operation and maintenance is higher than the average level, and it ...

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): G06Q50/06G06F17/50G06F17/30
CPCG06F16/29G06F30/20G06Q50/06
Inventor 陈锦铭黄强贾萌萌朱道华郭雅娟罗珊珊张小易崔晋利李斌杨雄李岩姜海涛
Owner STATE GRID JIANGSU ELECTRIC POWER CO ELECTRIC POWER RES INST
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