Employing adaptive particle swarm optimization algorithm for parameter estimation of an exciter machine / Ahmad Darabi in Transactions of the ASME . Journal of dynamic systems, measurement, and control, Vol. 134 N° 1 (Janvier 2012)  

Public ISBD [article]  inTransactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 134 N° 1 (Janvier 2012) . - 07 p. Titre : Employing adaptive particle swarm optimization algorithm for parameter estimation of an exciter machine Type de document : texte imprimé Auteurs : Ahmad Darabi, Auteur ; Alireza Alfi, Auteur ; Bahare Kiumarsi, Auteur Année de publication : 2012 Article en page(s) : 07 p. Note générale : Dynamic systems Langues : Anglais (eng) Mots-clés : Brushless machines Convergence Electric generators Genetic algorithms Machine windings Particle swarm optimisation Rotors Index. décimale : 553 Géologie économique. Minérographie. Minéraux. Formation et gisements de minerais Résumé : Winding inductances of an exciter machine of brushless generator normally consist of nonsinusoidal terms versus rotor position angle, so evaluations of the inductances necessitate detailed modeling and complicated parameter identification procedures. In this paper, an adaptive particle swarm optimization (APSO), which is a novel heuristic computation technique, is proposed to identify parameters of an exciter machine. The proposed approach evaluates the model parameters just knowing the main field impedance, measured exciter field voltage and current. APSO is employed to solve the optimization problem of minimizing the difference between output quantities (exciter field current) of the model and real systems. Two modifications are incorporated into the conventional particle swarm optimization (PSO) scheme that prevents local convergence and provides excellent quality of final result. Performance of the proposed APSO is compared with those of the real-coded genetic algorithm (GA) and PSO with linearly decreasing inertia weight (LDW-PSO), in terms of the parameter accuracy and convergence speed. Simulation results illustrated in the paper show that the proposed APSO is more successful in comparison with LDW-PSO and GA. DEWEY : 553 ISSN : 0022-434 En ligne : http://www.asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA00013400 [...] [article] Employing adaptive particle swarm optimization algorithm for parameter estimation of an exciter machine [texte imprimé] / Ahmad Darabi, Auteur ; Alireza Alfi, Auteur ; Bahare Kiumarsi, Auteur . - 2012 . - 07 p. Dynamic systems Langues : Anglais (eng) in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 134 N° 1 (Janvier 2012) . - 07 p. Mots-clés : Brushless machines Convergence Electric generators Genetic algorithms Machine windings Particle swarm optimisation Rotors Index. décimale : 553 Géologie économique. Minérographie. Minéraux. Formation et gisements de minerais Résumé : Winding inductances of an exciter machine of brushless generator normally consist of nonsinusoidal terms versus rotor position angle, so evaluations of the inductances necessitate detailed modeling and complicated parameter identification procedures. In this paper, an adaptive particle swarm optimization (APSO), which is a novel heuristic computation technique, is proposed to identify parameters of an exciter machine. The proposed approach evaluates the model parameters just knowing the main field impedance, measured exciter field voltage and current. APSO is employed to solve the optimization problem of minimizing the difference between output quantities (exciter field current) of the model and real systems. Two modifications are incorporated into the conventional particle swarm optimization (PSO) scheme that prevents local convergence and provides excellent quality of final result. Performance of the proposed APSO is compared with those of the real-coded genetic algorithm (GA) and PSO with linearly decreasing inertia weight (LDW-PSO), in terms of the parameter accuracy and convergence speed. Simulation results illustrated in the paper show that the proposed APSO is more successful in comparison with LDW-PSO and GA. DEWEY : 553 ISSN : 0022-434 En ligne : http://www.asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA00013400 [...]

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