Classifying Dynamics Events Using Neural Network and Wavelets for Current Power Systems
Available Online June 2015.
- https://doi.org/10.2991/icecee-15.2015.44How to use a DOI?
- Dynamics Events; Power System; Neural Network; Wavelet Analysis
- 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.
- 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 - Proceedings of the 2015 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 -