The usual data of commercial ferrite grades are given for sinusoidal waveforms, although the voltage in the typical applications in power electronics resemble to square waves. Firstly, an accurate two wire, oscilloscope power measurement is presented. Special wide frequency current and voltage transducers were designed, with a very low phase difference up to 5OMHz. Secondly, a ferrite loss model named Natural Steinmetz Extension (NSEJ is presented. The model is checked with measurements on two different ferrite grades, with square waves with a large variation in duty ratio. The proposed model is compared with Modified Steinmetz Equation (MSE). The two methods with a different mathematical formulation give comparable but different results.

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Under the current conditions of electricity market liberalization, it is often necessary to rapidly perform considerable changes in the power flows of cross-border lines or between isolated subsystems inside the country. For this purpose, in most cases, it is possible to use a simple connection / disconnection of a connection, which leads to a change in the topology of two subsystems. In such a situation it is quite reasonable to consider an issue regarding the change of the stability assessment for the generators on either side of the boundary interconnection before and after switching. In this paper, the authors have studied how the matrix eigenvalues of the coefficients of the two subsystems change after being combined into a single matrix; also, they have derived the conditions at which rapid assessment of the stability of the newly formed interconnected system can be performed according to preliminary estimates of the two subsystems before their interconnection. It has been indicated how and for what purposes this approach can be used when studying the stability of complex electrical systems.

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In this paper the structures of Adaptive Neuro-fuzzy interface system (ANFIS) are studied for noise identification. The system's structures are analyzed for different types of membership functions applied for input variables with root mean square errors variation. Hybrid algorithm and back propagation algorithm are applied. The input data are obtained through system simulation based on LabVIEW system design platform and development environment. The choice of ANFIS structure is based on the training results and minimum RMSE for identification of the signals with uniform white and inverse F Noises. Therefore, "gbellmf" membership function for input data variables is chosen. The accuracy classification is obtained at 100 %.

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