In this paper, the simulation of the disturbance propagation through a large power system is performed on the WSCC 127 bus test system. The signal frequency analysis from several parts of the power system is performed by applying the Wavelet Transf orm (WT). The results show that this approach provides the system operators with some useful information regarding the identification of the power system low-frequency electromechanical oscillations, the identification of the coherent groups of generators and the insight into the speed retardation of some parts of the power system. The ability to localize the disturbance is based on the disturbance propagation through the power system and the time-frequency analysis performed by using the WT is presented along with detailed physical interpretation of the used approach.

Power system is a complex, dynamic system, composed of a large number of interrelated elements. Its primary mission is to provide a safe and reliable production, transmission and distribution of electrical energy to final consumers, extending over a large geographic area. It comprises of a large number of individual elements which jointly constitute a unique and highly complex dynamic system. Some elements are merely the system's components while others affect the whole system (Machowski, 1997). Securing necessary level of safety is of great importance for economic and reliable operation of modern electric power systems.

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

In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (delta, theta, alpha, beta and gamma) and the Parsevals theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.