Supercharge Your Innovation With Domain-Expert AI Agents!

A fault diagnosis and classification evaluation method for intelligent distribution network lines

A technology for intelligent distribution network and line faults. It is applied to fault locations, fault detection by conductor type, and electricity measurement. It can solve problems such as the inability to guarantee the safety and reliability of the power grid, false alarms and omissions, and complex alarm information content.

Active Publication Date: 2021-09-14
HEFEI UNIV OF TECH
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to meet the needs of the people for the power grid, some automation-based equipment and devices have been widely used. When a fault occurs, the corresponding circuit breaker and other devices will respond immediately and give a series of alarm information, but the content of these alarm information is complicated. Or there are misstatements and omissions, resulting in the safety and reliability of the power grid being unable to be guaranteed

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 fault diagnosis and classification evaluation method for intelligent distribution network lines
  • A fault diagnosis and classification evaluation method for intelligent distribution network lines
  • A fault diagnosis and classification evaluation method for intelligent distribution network lines

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] In this example, if figure 1 As shown, a smart distribution network line fault diagnosis and classification evaluation method includes:

[0037] Step 1, such as Figure 4 As shown, the voltage signal is transmitted to the capacitive voltage divider 2 through the high-voltage current-limiting fuse 1 on the distribution network, and then connected to the high-precision sampling controller 3 to convert the three-phase voltage U of the power grid a (t), U b (t), U c (t) and sent to the signal processing device 4;

[0038] Step 2. For the three-phase voltage data U a (t), U b (t), U c (t) Carry out discrete wavelet transform, use db5 mother wave to carry out 5-layer decomposition, and the obtained detail signal is d5, which is used to reconstruct the three-phase voltage characteristic signal u a (t), u b (t), u c (t);

[0039] Step 3. For the three-phase voltage characteristic signal u a (t), u b (t), u c (t) Perform Fourier transform to obtain the phase informa...

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 fault diagnosis and classification evaluation method of an intelligent distribution network line. The steps include: 1. Obtaining the three-phase voltage signal U of the power grid online a (t), U b (t), U c (t); 2. The three-phase voltage signal is decomposed into 5 layers by discrete wavelet, and the a5 signal is used to reconstruct the three-phase voltage characteristic signal u a (t), u b (t), u c (t); 3. Fourier transform, calculate the phase information of the three-phase voltage 4. Draw a polar coordinate diagram, and obtain a matrix Z of n*n size through grayscale binary conversion a ,Z b ,Z c ; 5. Calculate the characteristic matrix C, D and the characteristic parameter V to obtain the characteristic coefficient E of the three-phase voltage a ,E b ,E c ; 5. According to the characteristic coefficient E a ,E b ,E c Determine the status of the smart distribution network line. The invention can accurately judge the fault type, is beneficial to fault phase selection and accident analysis, thereby improving the power supply reliability of the power grid and ensuring safe operation and maintenance efficiency of the power grid.

Description

technical field [0001] The invention relates to the field of line fault detection of an intelligent distribution network, in particular to a detection and evaluation method for a short circuit fault of a power grid. Background technique [0002] The power grid is an efficient and fast energy transmission channel and an optimized configuration platform. It is a key link in the sustainable development of energy and power. It plays an important pivotal role in the modern energy supply system and is related to national energy security. With the comprehensive construction of intelligent, digital, and information technology power grids, the data in the power industry has shown an explosive growth trend. The power system in operation generates a large amount of information, and the incidence of faults in the increasingly complex power grid system also increases. Naturally, Or the impact of human factors on the power grid is becoming more and more obvious, causing frequent failures,...

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): G01R31/52G01R31/08
CPCG01R31/086G01R31/088G01R31/52Y04S10/52
Inventor 张国荣张宇汤彬
Owner HEFEI UNIV OF TECH
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