Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics

Classifying Dynamics Events Using Neural Network and Wavelets for Current Power Systems

Authors
Yingli Shu
Corresponding Author
Yingli Shu
Available Online June 2015.
DOI
https://doi.org/10.2991/icecee-15.2015.44How to use a DOI?
Keywords
Dynamics Events; Power System; Neural Network; Wavelet Analysis
Abstract
In this paper, we propose a novel methodology that classify the power system dynamics events patterns using neural network and wavelet through studying one single variable at a network bus. DWT allows the identification of components of the LFEO (low-frequency electromechanical oscillations), their frequencies, and magnitudes. Following the determination of the energy components' share of the studied signal using Parseval’s theory and discrete wavelet transform, we get the input data. A total of five classes of disturbances, three different wavelet functions, and two different variables are tested. The experimental results emostrates that our methodology could classify different power disturbance types efficiently.
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Proceedings
2015 2nd International Conference on Electrical, Computer Engineering and Electronics
Part of series
Advances in Computer Science Research
Publication Date
June 2015
ISBN
978-94-62520-81-3
ISSN
2352-538X
DOI
https://doi.org/10.2991/icecee-15.2015.44How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yingli Shu
PY  - 2015/06
DA  - 2015/06
TI  - Classifying Dynamics Events Using Neural Network and Wavelets for Current Power Systems
BT  - 2015 2nd International Conference on Electrical, Computer Engineering and Electronics
PB  - Atlantis Press
SP  - 195
EP  - 198
SN  - 2352-538X
UR  - https://doi.org/10.2991/icecee-15.2015.44
DO  - https://doi.org/10.2991/icecee-15.2015.44
ID  - Shu2015/06
ER  -