Power transmission line icing thickness predicting method based on LSTM artificial neural network

An artificial neural network and transmission line technology, applied in the field of ice thickness prediction of transmission lines based on LSTM artificial neural network, can solve problems such as hidden dangers to personnel safety, large resource consumption of monitoring equipment, poor reliability of camera equipment, etc., to improve reliability. sexual effect

Inactive Publication Date: 2018-03-09
YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST +1
View PDF5 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] At present, there are very limited methods available for data collection of icing on transmission lines: artificial ice observation will consume a lot of manpower and material resources, and will cause hidden dangers to personnel safety, and the effectiveness of the data is not enough to achieve icing. Disaster prediction requirements; real-time cameras require a large amount of real-time monitoring

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 transmission line icing thickness predicting method based on LSTM artificial neural network
  • Power transmission line icing thickness predicting method based on LSTM artificial neural network
  • Power transmission line icing thickness predicting method based on LSTM artificial neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0042] 1) Use the on-line monitoring device to obtain the data of the ice thickness of the transmission line at fixed intervals (such as 2 hours), the monitoring data of the tension terminal set on the transmission tower, the real-time voltage monitoring terminal data and the meteorological terminal monitoring data (temperature, air humidity) , as the basic data of the training sample;

[0043] 2) Use the Lagrange interpolation method to process outliers and missing values ​​of the training sample data. Obtain reliable data that can be input into the LSTM artificial neural network algorithm for calculation, including ice thickness, wire voltage, wire tension, temperature, and air humidity;

[0044] 3) Input the sample data into the LSTM artificial neural network for cyclic recursive calculation until the error value reaches the minimum;

[0045] 4) Use the trained LSTM neural network model as the core algorithm model for predicting the ice thickness of transmission lines in t...

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

A power transmission line icing thickness predicting method based on an LSTM artificial neural network adopts historical icing thickness data of power transmission line poles and towers, data monitored by tensile force terminals arranged on the power transmission line poles and towers, data from real-time voltage monitoring terminal and data monitored by weather terminals, utilizes an LSTM artificial neural network algorithm technology to dynamically obtain the icing thickness on a power transmission line to be predicted according to real-time change of weather through data fusion, combines with designed borne icing thickness of the power transmission line to conduct early warning on the safety of iced poles and towers. Compared with a traditional artificial ice observing method, labor andmaterial resources are saved, better timeliness is also obtained, the fund input is smaller and the maintenance cost is lower compared with monitoring based on cameras, and power transmission line poles and towers having potential safety hazards can be found more timely.

Description

technical field [0001] The invention relates to a method for integrating transmission line conductors and ground wire ice thickness data, tension terminal monitoring data set on transmission towers, real-time voltage monitoring terminal data and meteorological terminal monitoring data in a power grid. Time series modeling of ice thickness data, and a machine learning method for predicting future ice thickness using the trained model. Background technique [0002] At present, there are very limited methods available for data collection of icing on transmission lines: artificial ice observation will consume a lot of manpower and material resources, and will cause hidden dangers to personnel safety, and the effectiveness of the data is not enough to achieve icing. Disaster prediction requirements; real-time cameras require a large amount of real-time monitoring equipment in the early stage. Since the power grid is an asset-intensive enterprise, there are too many transmission l...

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): G06Q10/04G06Q50/06G06N3/04
CPCG06N3/04G06Q10/04G06Q50/06Y04S10/50
Inventor 黄绪勇李晓帆王艳涛
Owner YUNNAN POWER GRID CO LTD 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
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap