Direct adaptive power system stabilizer design using fuzzy wavelet neural network with self-recurrent consequent part |
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| Affiliation: | 1. Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Shahid Fahmideh Street, P.O. Box 65178-38683, Hamedan, Iran;2. Young Researchers and Elite Club, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran;3. Faculty of Electrical Engineering, Babol Nooshirvani University of Technology, Mazandaran, Iran;1. Dept. of CSIE, National Taiwan University of Science and Technology, Taipei, Taiwan;2. Research Center for IT Innovation (CITI), Academia Sinica, Taipei, Taiwan;1. School of Business, Central South University, Changsha 410083, China;2. School of Management, Qingdao Technological University, Qingdao 266520, China;3. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China;1. Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, USA;2. Department of Electrical and Computer Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, USA;1. Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran;2. Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran;3. School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran |
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| Abstract: | This paper aims to propose a stable fuzzy wavelet neural-based adaptive power system stabilizer (SFWNAPSS) for stabilizing the inter-area oscillations in multi-machine power systems. In the proposed approach, a self-recurrent Wavelet Neural Network (SRWNN) is applied with the aim of constructing a self-recurrent consequent part for each fuzzy rule of a Takagi-Sugeno-Kang (TSK) fuzzy model. All parameters 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 an approach based on genetic algorithm (GA). A Lyapunov-based adaptive learning rates (LALRs) algorithm is also proposed in order to speed up the stabilization rate, as well as to guarantee the convergence of the proposed stabilizer. Therefore, due to having a stable powerful adaptation law, there is no requirement to 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 proposed stabilizer. The results are promising and show that the inter-area oscillations are successfully damped by the SFWNAPSS. Furthermore, the superiority of the proposed stabilizer is demonstrated over the IEEE standard multi-band power system stabilizer (MB-PSS), and the conventional PSS. |
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| Keywords: | Lyapunov adaptive learning rates Takagi-Sugeno-Kang fuzzy model Fuzzy wavelet neural network Genetic Algorithm |
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