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Discharge voltage prediction of UHV AC transmission line–tower air gaps by a machine learning model
- Source :
- The Journal of Engineering (2019)
- Publication Year :
- 2019
- Publisher :
- Institution of Engineering and Technology (IET), 2019.
-
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.
- 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
Subjects
Details
- ISSN :
- 20513305
- Volume :
- 2019
- Database :
- OpenAIRE
- Journal :
- The Journal of Engineering
- Accession number :
- edsair.doi.dedup.....2ec7738b1d1635210b1fc1edd05a0332
- Full Text :
- https://doi.org/10.1049/joe.2018.8486