In simulation studies, the precision of fuel cell models has a vital role in the quality of results. Unfortunately, due to the shortage of manufacturer data given in the datasheets, several unknown parameters should be defined to establish the fuel cell model for further precise analysis. This research addresses a novel application of the atom search optimization (ASO) algorithm to generate these unknown parameters of the fuel cell model and in particular of the polymer exchange membrane (PEM) type. The objective of this study is to establish an accurate model of the PEM fuel cells, which will provide accurate results of modeling and simulation in a steady-state condition. Simulations and further demonstrations were performed under MATLAB/SIMULINK. The viability of the proposed models was appraised by comparing its simulation results with the experimental results of number of commercial PEM fuel cells. In the same context, the obtained numerical results by the proposed ASO-based method were compared to other challenging optimization methods-based results. Finally, parametric tests were made which indicated the robustness of the ASO results as well. It can be stated here that ASO performs well and has a good capability to extract the unknown parameters with lesser errors.
SUMMARYThe switching frequency of a conventional direct torque control (DTC) strategy which is based on hysteresis controllers results in a variable switching frequency which depends on the mechanical speed, stator flux, stator voltage, and the hysteresis band of the comparator. In this paper, a DTC scheme using space vector modulator (SVM) and fuzzy logic controller (FLC) is suggested. A comparison with the classical DTC is presented. The suggested control strategy guarantees very good dynamic and steady state characteristics with low sampling rate and a constant switching frequency. The experimental results are presented to validate the scheme.
This paper presents a procedure to coordinated design of Power System Stabilizers (PSSs) and Static VAR Compensators (SVCs) in a multimachine power system. The aims of the proposed method are to find the best location and the optimal parameters of these compensators in order to improve the steady state and transient performances and also to increase the system damping over a wide range of operating conditions. The objective function of the Genetic Algorithm (GA) allows the selection of the PSSs and SVCs to shift critical closed loop eigenvalues to the left side in the complex s-plane. The multimachine power system considered in this study consists of nine buses, three generating units (steam, hydro and nuclear) and three static loads. Digital simulation studies show that the proposed design procedure provides good damping for the power system at different operating conditions, and moreover improves steady-state and transient performance of the system.
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