1. Discharge voltage prediction of UHV AC transmission line–tower air gaps by a machine learning model
- Author
-
Jiangjun Ruan, Jin Qi, Xuezong Wang, Shengwen Shu, and Zhibin Qiu
- Subjects
full-scale discharge tests ,mean absolute percentage errors ,finite element analysis ,02 engineering and technology ,Impulse (physics) ,computer.software_genre ,01 natural sciences ,support vector machines ,UHV AC transmission line–tower air gaps ,gap configurations ,0202 electrical engineering, electronic engineering, information engineering ,hypothetic discharge channel ,three-dimensional finite element models ,010302 applied physics ,engineering gap types ,feature extraction ,General Engineering ,gap distances ,overhead line conductors ,Finite element method ,Conductor ,Electric power transmission ,bundled conductor ,machine learning model ,SVM model ,Materials science ,power overhead lines ,power transmission lines ,020209 energy ,ultra-high-voltage AC transmission line–tower air gaps ,Energy Engineering and Power Technology ,Machine learning ,air gaps ,Transmission line ,Electric field ,0103 physical sciences ,support vector machine ,electric field calculation ,UHV compact transmission line ,power engineering computing ,UHV cup-type tower ,business.industry ,switching impulse discharge voltages ,poles and towers ,Support vector machine ,discharges (electric) ,lcsh:TA1-2040 ,discharge voltage prediction ,learning (artificial intelligence) ,Artificial intelligence ,lcsh:Engineering (General). Civil engineering (General) ,business ,computer ,Software ,Voltage - Abstract
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 switching impulse discharge voltages of the ultra-high-voltage (UHV) AC transmission line–tower air gaps. The three-dimensional finite element models of a UHV cup-type tower and a UHV compact transmission line were established for electric field calculation, and some features were extracted from the hypothetic discharge channel and the shortest path between the bundled conductor and the tower. These features under a given voltage were normalised and input to the SVM model, while the output is two binary values, respectively, representing gap withstanding or breakdown. Trained by experimental data of one type of the UHV transmission line–tower gaps, the SVM model is able to predict the discharge voltages of another gap type. The mean absolute percentage errors of the two engineering gap types, under different gap distances, are 8.31 and 4.86%, respectively, which are acceptable for engineering applications. The results provide a possible way to obtain the discharge voltages of complicated engineering gaps by mathematical calculations.
- Published
- 2019