Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Artificial intelligence fault identification system and method based on transient waveform of power transmission line

A technology of transmission lines and transient waveforms, applied in the direction of fault location, reasoning method, neural learning method, etc., can solve the problems of inconsistent experience and difficulty in ensuring the timeliness of fault identification, so as to reduce communication transmission, improve accuracy, reduce The effect of labor

Pending Publication Date: 2021-06-25
WUHAN NARI LIABILITY OF STATE GRID ELECTRIC POWER RES INST +4
View PDF10 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to propose an artificial intelligence fault identification system and method based on the transient waveform of the transmission line, aiming to solve the above-mentioned problems such as difficulty in ensuring the timeliness of fault identification by human experts and inconsistent experience

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
  • Artificial intelligence fault identification system and method based on transient waveform of power transmission line
  • Artificial intelligence fault identification system and method based on transient waveform of power transmission line
  • Artificial intelligence fault identification system and method based on transient waveform of power transmission line

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0021] The present invention will be further detailed below with reference to the accompanying drawings and specific embodiments:

[0022] An artificial intelligence fault identification method based on transmission line transient waveforms proposed by the present invention, such as figure 2 As shown, it includes the following steps:

[0023] S1, a real-time transmission line transient waveform signal preprocessing based on the sliding window method, obtains the corresponding real-time transmission line transient waveform sequence image data;

[0024] S2, input real-time transmission line transient waveform sequence image data into the depth learning model, characteristic extraction and forward reasoning of sequence image data by depth learning model, and finally obtain real-time transmission line transient waveform signals corresponding to each transmission The confidence of the line fault type is obtained from the transmission line fault type; the depth learning model is based o...

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 an artificial intelligence fault identification system and method of a power transmission line transient waveform, and the system comprises a signal preprocessing module, a manual marking module, a waveform identification module, a training and tuning module, and a performance test and optimization module. The method for realizing fault identification based on the system mainly comprises the following steps: performing fault type manual labeling on original waveform signals to generate data labels, and establishing a power transmission line transient waveform fault sample library; preprocessing the transient waveform signal of the power transmission line based on a sliding window method to obtain corresponding sequence image data; building a deep learning model, and achieving the transient waveform recognition of the power transmission line; performing parameter training and tuning on the deep learning model; and performing performance testing on the deep learning model, and completing targeted optimization to improve the performance. Through testing, the method can quickly realize the transient waveform identification of the power transmission line, the identification accuracy reaches 92.67%, and the method can replace human experts to carry out the work in the aspect.

Description

Technical field [0001] The present invention relates to the field of power system fault identification, and more particularly to artificial intelligence fault identification systems and methods based on transient waveforms of transmission lines. Background technique [0002] The geographical conditions of the transmission line are complex and diverse, and there is a trip accident in terms of lightning, filthy, animal and plant, wind, and ice-free, and other natural factors. The route trip has a great impact on system security, equipment security, and power supply reliability. my country's power grid operation procedures require the failure position and fault type in the shortest time after trip. Therefore, fast, accurate, reliable troubleshooting and positioning techniques are particularly important for transmission line safe operation. [0003] At present, my country's power transmission line is installed with a distributed fault line wave detection device, which can measure tra...

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): G06Q10/00G06Q50/06G01R31/08G06K9/00G06N3/04G06N3/08G06N5/04
CPCG06Q10/20G06Q50/06G06N3/04G06N3/08G06N5/04G01R31/085G01R31/088G06F2218/00Y04S10/52
Inventor 王宇朱太云李健刘宇舜白冰洁陶汉涛周展科高雅玙黎炎韩冬
Owner WUHAN NARI LIABILITY OF STATE GRID ELECTRIC POWER RES INST
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products