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An icing prediction model and method for overhead transmission lines

A technology of overhead transmission lines and prediction models, applied in prediction, circuit devices, AC network circuits, etc., can solve the problem that the amount of ice thickness data cannot meet the needs of deep network training, so as to improve the generalization performance, reduce the demand, The effect of increasing the receptive field

Active Publication Date: 2022-07-19
NANJING NARI GROUP CORP +2
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AI Technical Summary

Problems solved by technology

The deep neural network needs a large amount of training data. Although the line icing online monitoring system has been initially developed under the promotion of smart grid technology, the amount of collected ice thickness data cannot meet the training needs of the deep network.

Method used

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  • An icing prediction model and method for overhead transmission lines
  • An icing prediction model and method for overhead transmission lines
  • An icing prediction model and method for overhead transmission lines

Examples

Experimental program
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Embodiment

[0079] 1. Dataset

[0080] The neural network data set in this example comes from data from a meteorological observation site for about three years, with good continuity and few missing samples. Use the meteorological dataset to train a time series convolutional network to predict the main meteorological factors that affect icing, such as temperature, humidity, wind speed, rainfall, etc. The support vector regression model requires the ice thickness dataset. In order to collect ice thickness data, the transmission line online monitoring device needs to be equipped with ice thickness data acquisition equipment, which uploads the ice thickness data to the database server.

[0081] The model time interval is set to 1 hour, that is, 1 time step of the time series data is 1 hour. The following is a piece of raw temperature time series data (unit: degrees Celsius): 13.3,12.4,11.4,11.2,10.6,10.2,-9999.0,-9999.0,-9999.0,-9999.0,-9999.0,-9999.0,-9999.0,-9999.0, -9999.0,11.6,13.7,14....

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Abstract

The invention discloses an overhead transmission line icing prediction model and method. The model is constructed through the following steps: step 1, obtaining icing related data from a line online monitoring system, screening and processing the obtained data; step 2, Establish and train a time-series convolutional neural network model, which is used to predict icing factors; step 3, establish and train a support vector regression model, which is used to establish a mapping between icing factors and icing thickness, a time-series convolutional neural network The model and the support vector regression model together form the icing prediction model for overhead transmission lines. Using the established model, icing prediction can be made.

Description

technical field [0001] The invention relates to the technical field of machine learning and state monitoring of overhead transmission lines of power systems, in particular to a prediction model and method for icing of overhead transmission lines. Background technique [0002] Overhead high-voltage transmission lines are the arteries of modern society, and their safe and stable operation is crucial to society. Ice disaster is one of the major natural disasters that threaten overhead transmission lines. The icing of power lines can cause flashover and tripping accidents at light level, and serious accidents such as damage to hardware, disconnection, and tower collapse, which seriously threaten the safe and stable operation of the power system and cause huge losses to the society and economy. Due to the vast and complex geographical conditions in our country and the influence of global warming, ice-covering disasters on transmission lines occur frequently. Therefore, it is of...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/20G06K9/62G06Q10/04G06Q50/06H02J3/00
CPCG06Q10/04G06Q50/06H02J3/00G06F30/20H02J2203/20G06F18/214
Inventor 张华鲁路通廖志军邓照飞石杰邓博雅袁明磊
Owner NANJING NARI GROUP CORP
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