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Overhead transmission line icing prediction model and method

A technology of overhead transmission lines and forecasting models, applied in forecasting, 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

Active Publication Date: 2019-08-30
NANJING NARI GROUP CORP +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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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  • Overhead transmission line icing prediction model and method
  • Overhead transmission line icing prediction model and method
  • Overhead transmission line icing prediction model and method

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Experimental program
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Embodiment

[0079] 1. Dataset

[0080] The neural network data set in this embodiment comes from a meteorological observation site for more than three years, with good continuity and few missing samples. Use meteorological datasets to train time-series convolutional networks to predict the main meteorological factors that affect icing, such as temperature, humidity, wind speed, rainfall, etc. The Support Vector Regression model requires an 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, and the ice thickness data acquisition equipment 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 series data is 1 hour. The following is a piece of raw temperature time series data (unit: 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, -99...

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Abstract

The invention discloses an overhead transmission line icing prediction model and method, and the model is constructed through the following steps of 1, obtaining the icing related data from a line online monitoring system, and screening and processing the obtained data; 2, establishing and training a time sequence convolutional neural network model, wherein the model is used for predicting the icing factors; and 3, establishing and training a support vector regression model which is used for establishing mapping between the icing factors and the icing thickness, and the sequential convolutional neural network model and the support vector regression model jointly form an overhead transmission line icing prediction model. The icing prediction can be performed by using the established models.

Description

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

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06K9/62G06Q10/04G06Q50/06H02J3/00
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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