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This paper proposes the use of the discrete wavelet transform (DWT) to analyze and locate the low frequency oscillations of power systems, which can result in a loss of stability or in a black-out. The proposed DWT applied to active power signal is tested and compared with more conventional approaches of Prony and Eigenvalue analyses. The DWT also enables estimation of the total active power imbalance of a system. Estimation performances of different wavelet families are tested. Based on the proposed DWT of electromechanical transient oscillations, the DWT is also applied to identify the network nodes in the neighbourhood of the original disturbance. The performance of the proposed DWT is evaluated on the New England (NE) 39-bus test system.

This paper investigates the possibility of classifying power system dynamics events using discrete wavelet transform (DWT) and a neural network (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 (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.

Ibrahim Omerhodzic1, Samir Avdakovic2, Amir Nuhanovic3, Kemal Dizdarevic1 and Kresimir Rotim4 1Clinical Center University of Sarajevo, Department of Neurosurgery, Sarajevo 2EPC Elektroprivreda of Bosnia and Herzegovina, Sarajevo 3Faculty of Electrical Engineering, University of Tuzla, Tuzla 4University Hospital “Sisters of Charity”, Department of Neurosurgery, Zagreb 1,2,3Bosnia and Herzegovina 4Croatia

[1 ]H. He, J.A. Starzyk, A self-organizing learning array system for power quality classification based on wavelet transform, IEEE Transactions on Power Delivery 21 (1) (2006) 286–295. [2 ]P. O’Shea, E. Palmer, G. Frazer, Power system disturbance monitoring using spectral analysis techniques, IEEE Int. Conference on Acoustics, Speech and Signal Processing (1995) 2731–2734. [3 ]K. Mei, S.M. Rovnyak, C-M. Ong, Dynamic event detection using wavelet analysis, IEEE PES General Meeting (2006) 1–7.