Search

In this paper, the wavelet phase difference (WPD) approach is applied for the identification of power system areas with coherent generator groups. This approach allows observation, at different frequency bands, of movement of low frequency electromechanical oscillations (LFEO), identified at different parts of the power system and the identification of the inter-area components that move or do not move together. An illustration of the applied approach was performed on the New England (NE) 39-bus test system. The interesting results of WPD application are also presented in a real wide-area measurement data from European interconnected power system. By using the discrete wavelet transform (DWT) and Hilbert-Huang transform (HHT), the validation of results from WPD approach is also given.

In this paper, the results of correlations between air temperature and electricity demand by linear regression and Wavelet Coherence (WTC) approach for three different European countries are presented. The results show a very close relationship between air temperature and electricity demand for the selected power systems, however, the WTC approach presents interesting dynamics of correlations between air temperature and electricity demand at different time-frequency space and provide useful information for a more complete understanding of the related consumption. Key words: power system, electricity demand, air temperature, linear regression, wave-let coherence 1. Introduction The electric power system is a very complex system. It is composed of a large number of different elements such as generators, lines, transformers, etc., but it also has a lot of different consumption categories such as household, industrial, transportation, and others. Short, me-dium or long-term planning requires thorough understanding of consumption characteristics which are defined by their load curves. The load curves represent characteristics of the power system’s load variability as a function of time, and their analysis can be performed for some customer groups, some geographic areas or the power system as a whole, where it is very important to identify the main factors affecting the consumption of electricity, such as growth and structure of Gross Domestic Product (GDP), demographic change, housing standard, the

This paper discusses the problem of finding the optimal network topological configuration by changing the feeder status. The reconfiguration problem is considered as a multiobjective problem aiming to minimize power losses and total interruptions costs subject to the system constraints: the network radiality voltage limits and feeder capability limits. Due to its complexity, the metaheuristic methods can be applied to solve the problem and often the choice is genetic algorithm. NSGA II is used to solve the multiobjective optimization problem in order to get Pareto optimal set with possible solutions. The proposed method has been tested on real 35 kV distribution network. The numerical results are presented to illustrate the feasibility of the proposed genetic algorithm.

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.

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

[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.