Applications of wavelets and neural networks for classification of power system dynamics events

Authors : Samir AVDAKOVIC, Amir NUHANOVIC, Mirza KUSLJUGIC

Pages : 327-340

Doi:10.3906/elk-1206-116

View : 17 | Download : 12

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :This paper investigates the possibility of classifying power system dynamics events using discrete wavelet transform insert ignore into journalissuearticles values(DWT); and a neural network insert ignore into journalissuearticles values(NN); by analyzing one variable at a single network bus. Following a disturbance in the power system, it will propagate through the system in the form of low-frequency electromechanical oscillations insert ignore into journalissuearticles values(LFEOs); in a frequency range of up to 5 Hz. DWT allows the identification of components of the LFEO, their frequencies, and magnitudes. After determining the energy components` share of the analyzed signal using DWT and Parseval`s theorem, the input data for the classification process using a NN are obtained. A total of 5 classes of disturbances, 3 different wavelet functions, and 2 different variables are tested. Simulation results show that the proposed approach can classify different power disturbance types efficiently, regardless of the choice of variable or wavelet function.
Keywords : Power system dynamics, low frequency electromechanical oscillations, wavelet transform, neural network, disturbance