Supercharge Your Innovation With Domain-Expert AI Agents!

A machine learning-based distribution network small-current grounding automatic pulling circuit control device

A low-current grounding, machine learning technology, applied in circuit devices, emergency protection circuit devices, electrical components, etc., can solve the problems of safe operation of the power grid, difficulty in optimizing the order of pulling, and long grounding duration, etc. Pull circuit opening and closing efficiency, reducing duration, and realizing the effect of pull circuit priority strategy

Pending Publication Date: 2019-01-18
南京我的电气科技有限公司
View PDF5 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When determining the order of pulling the lines, on the one hand, it is necessary to determine the grounding line as quickly as possible; Electricity, historical grounding conditions and many other factors, it is difficult to quickly optimize the routing sequence by relying on the experience of dispatchers alone
At the same time, for the sake of safety when performing remote control pulling operation, traditionally, the pulling operation is mainly carried out manually one by one, resulting in a long duration of grounding and serious harm to the safe operation of the power grid.

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 machine learning-based distribution network small-current grounding automatic pulling circuit control device
  • A machine learning-based distribution network small-current grounding automatic pulling circuit control device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0022] Such as figure 1 As shown, a small-current grounding automatic circuit-pull control device for distribution network based on machine learning includes a data acquisition device 101, a small-current grounding diagnosis device 102, an automatic circuit-pull sequence generation device 103, and a sequence remote control device 104 connected in sequence. The sequence remote control device 104 is connected to the dispatching automation system, and the dispatching automation system is connected to the data acquisition device 101, and the automatic routing sequence generation device 103 is connected to the machine learning device 105, and the machine learning device 105 is connected to the historical routing recording storage device 106; 101, used to obtain the real-time operating conditions of the power grid; the low-current grounding diagnostic device 102, used to automatically determine the 10kV busbar with a small-current grounding fault according to the real-time operating ...

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 provides a machine learning-based distribution network small-current grounding automatic pulling circuit control device, which comprises a data acquisition device, a low-current ground diagnostic device, a pull sequence automatic generation device and a sequence remote control device connected in turn, wherein that sequence remote control device is connected to a dispatching automation system, the dispatch automation system is connected with a data acquisition device, the pull sequence automatic generation device is connected with a machine learning device, and the machine learning device is connected with a history pull record storage device. The invention discloses a distribution network low-current grounding automatic circuit-pulling control device based on machine learning, which utilizes neural network and history circuit-pulling recording device to carry out training and self-learning, and realizes continuous optimization of circuit breaker circuit-pulling prioritystrategy.

Description

technical field [0001] The invention belongs to the technical field of power grid dispatching, and in particular relates to a small-current grounding automatic circuit pulling control device for distribution network based on machine learning. Background technique [0002] At present, the grounding method of my country's 10kV power system mainly adopts the small current grounding method. In the case of single-phase grounding on the 10kV line, because there is no short-circuit current in theory, the protection cannot operate, and it can continue to run for 2 hours according to the regulations. During this period, dispatchers need to pull the 10kV lines one by one to determine the grounded line. Traditionally, dispatchers mainly determine the order of road pulling operations through manual experience, and use the remote control function of the dispatch automation system to manually perform road pulling operations. When determining the order of pulling the lines, on the one 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): H02H7/26H02J13/00
CPCH02H7/26H02J13/00Y04S10/20Y02E60/00
Inventor 许先锋
Owner 南京我的电气科技有限公司
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