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Power transmission line galloping early warning method based on Bayes-Adaboost improved algorithm

A transmission line and improved algorithm technology, applied in prediction, calculation, computer components, etc., can solve the problems of low practicality and accuracy of transmission line galloping early warning, inaccurate physical model, difficult measurement and acquisition, etc.

Active Publication Date: 2021-08-10
STATE GRID HENAN ELECTRIC POWER ELECTRIC POWER SCI RES INST +2
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  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Because the physical model of existing transmission line galloping is not accurate enough, and some parameters in the model are difficult to obtain through measurement on the actual line, the practicability and accuracy of using physical models for transmission line galloping warning are low.

Method used

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  • Power transmission line galloping early warning method based on Bayes-Adaboost improved algorithm
  • Power transmission line galloping early warning method based on Bayes-Adaboost improved algorithm
  • Power transmission line galloping early warning method based on Bayes-Adaboost improved algorithm

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Embodiment Construction

[0083] The application will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, but not to limit the protection scope of the present application.

[0084] Such as figure 1 Shown, a kind of transmission line galloping early warning method based on Bayes-Adaboost improved algorithm of the present invention comprises the following steps:

[0085] Step 1: Classify and combine the transmission lines according to the internal cause that affects the galloping excitation of the transmission line, that is, the conductor parameters, to form several line combinations;

[0086] Those skilled in the art can arbitrarily select the types and quantities of wire parameters to classify and combine transmission lines. A preferred but non-limiting embodiment is to select three wire parameters, which are wire structure, wire cross-sectional area and span.

[008...

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Abstract

The invention discloses a power transmission line galloping early warning method based on a Bayes-Adaboost improved algorithm, and the method comprises the steps: training a historical galloping fault training set, forming a classifier through employing an Adaboost integrated learning method, and obtaining a power transmission line galloping early warning result through the classifier according to real-time prediction meteorological information and different parameter information of a power transmission line; for a newly added galloping fault sample, according to a Bayes formula, correcting a model, and adding related influence parameters of energy accumulation on galloping into the model, so as to reflect the parameters by a temperature change rate and a humidity change rate, and form a galloping early warning method based on a Bayes-Adaboost improved method. According to the invention, calculation processing of related data such as forecast information of power transmission line galloping meteorological characteristic factors and structural parameters of the power transmission line can be realized, and a power transmission line galloping disaster early warning analysis result of the area is obtained.

Description

technical field [0001] The invention belongs to the technical field of transmission line galloping early warning, and relates to a transmission line galloping early warning method based on an improved Bayes-Adaboost algorithm. Background technique [0002] Because the physical model of existing transmission line galloping is not accurate enough, and some parameters in the model are difficult to obtain through measurement on the actual line, the practicability and accuracy of using physical models for transmission line galloping warning are low. [0003] Machine learning is based on past observations to obtain more accurate predictions. It provides a method to obtain laws that cannot be obtained through principle analysis from observational data, and then use these laws to predict future data. Therefore, machine learning theory can be well applied to the early warning method of galloping in transmission lines. Contents of the invention [0004] In order to solve the defici...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06K9/62
CPCG06Q10/04G06Q50/06G06F18/2148G06F18/2415G01W2203/00G01W1/10G06N20/20G06N7/01G06N5/01G06F18/214
Inventor 刘善峰郭志民李哲王超梁允姚德贵苑司坤杨磊李帅刘莘昱吕中宾卢明王津宇高阳崔晶晶张宇鹏高超耿俊成张小斐袁少光毛万登田杨阳
Owner STATE GRID HENAN ELECTRIC POWER ELECTRIC POWER SCI RES INST
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