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چکیده
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tThis paper aims to propose a stable fuzzy wavelet neural-based adaptive power system stabilizer (SFW-NAPSS) for stabilizing the inter-area oscillations in multi-machine power systems. In the proposedapproach, a self-recurrent Wavelet Neural Network (SRWNN) is applied with the aim of constructinga self-recurrent consequent part for each fuzzy rule of a Takagi-Sugeno-Kang (TSK) fuzzy model. Allparameters of the consequent parts are updated online based on Direct Adaptive Control Theory (DACT)and employing a back-propagation-based approach. The stabilizer initialization is performed using anapproach based on genetic algorithm (GA). A Lyapunov-based adaptive learning rates (LALRs) algorithmis also proposed in order to speed up the stabilization rate, as well as to guarantee the convergence of theproposed stabilizer. Therefore, due to having a stable powerful adaptation law, there is no requirementto use any identification process. Kundur’s four-machine two-area benchmark power system and six-machine three-area power system are used with the aim of assessing the effectiveness of the proposedstabilizer. The results are promising and show that the inter-area oscillations are successfully dampedby the SFWNAPSS. Furthermore, the superiority of the proposed stabilizer is demonstrated over the IEEEstandard multi-band power system stabilizer (MB-PSS), and the conventional PSS.
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