Preliminary analysis of the effectiveness of the use of artificial neural networks for modelling time-voltage and time-current signals of the combination wave generator
Authors:
- Marek Dudzik,
- Ryszard Mielnik,
- Zofia Wróbel
Abstract
Currently, scientific research shows an increasing use of artificial intelligence technology to model various types of phenomena. One of such issues is the modeling of the output signals from special electric surge generator. The authors in earlier work presented the model of the output voltage signal from such a generator. This model used the technology of artificial neural networks. This article presents the model of the output current signal from the surge generator. This model also uses an artificial neural network. Both of them, with suboptimal structure, the above models have 1 hidden layer and 4 neurons. The simulation tests of these models showed their high accuracy. Having the model of the output voltage and current from the surge generator available, it is possible to simulate the influence of such signals on various elements of electrical circuits. This can be used, for example, for testing of projected surge protection systems.
- Record ID
- CUTacd041bc447d4e82bd711fccd9216a6b
- Publication categories
- ; ;
- Author
- Pages
- 1095-1100
- Other elements of collation
- schem.; wykr.; Bibliografia (na s.) - 1099-1100; Bibliografia (liczba pozycji) - 16; Oznaczenie streszczenia - Abstr.
- Book
- 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 2018, [S.l.], Institute of Electrical and Electronics Engineers, IEEE, ISBN 978-1-5386-4941-1 e-ISBN
- Keywords in English
- artificial neural network, combination wave generator, voltage and current waves modelling
- DOI
- DOI:10.1109/SPEEDAM.2018.8445277 Opening in a new tab
- URL
- https://ieeexplore.ieee.org/document/8445277 Opening in a new tab
- Related project
- Metody i algorytmy monitoringu i diagnostyki maszyn elektrycznych. . Project leader at PK: , ,
Działalność statutowa - Language
- eng (en) English
- Score (nominal)
- 20
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTacd041bc447d4e82bd711fccd9216a6b/
- URN
urn:pkr-prod:CUTacd041bc447d4e82bd711fccd9216a6b
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.