Discharge voltage prediction of UHV AC transmission line–tower air gaps by a machine learning model

Full-scale discharge tests of transmission line–tower air gaps are costly and time-consuming, and they cannot exhaustively simulate all the gap configurations in practical engineering. In this paper, a machine learning model established by support vector machine is introduced to predict the switchin...

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Bibliographic Details
Main Authors:	Zhibin Qiu, Jiangjun Ruan, Qi Jin, Xuezong Wang, Shengwen Shu
Format:	Article
Language:	English
Published:	Wiley 2019-02-01
Series:	The Journal of Engineering
Subjects:	support vector machines air gaps finite element analysis power transmission lines poles and towers power overhead lines overhead line conductors learning (artificial intelligence) discharges (electric) power engineering computing feature extraction SVM model engineering gap types discharge voltage prediction UHV AC transmission line–tower air gaps machine learning model full-scale discharge tests gap configurations support vector machine switching impulse discharge voltages ultra-high-voltage AC transmission line–tower air gaps three-dimensional finite element models UHV cup-type tower UHV compact transmission line hypothetic discharge channel gap distances electric field calculation bundled conductor mean absolute percentage errors
Online Access:	https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8486

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https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8486

Discharge voltage prediction of UHV AC transmission line–tower air gaps by a machine learning model

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