Designing an adaptive fuzzy controller for maximum wind energy extraction / Galdi, V. in IEEE transactions on energy conversion, Vol. 23 n°2 (Juin 2008)  

Public ISBD [article]  inIEEE transactions on energy conversion > Vol. 23 n°2 (Juin 2008) . - pp. 559 - 569 Titre : Designing an adaptive fuzzy controller for maximum wind energy extraction Type de document : texte imprimé Auteurs : Galdi, V., Auteur ; Piccolo, A., Auteur ; Siano, P., Auteur Année de publication : 2008 Article en page(s) : pp. 559 - 569 Note générale : Energy conversion Langues : Anglais (eng) Mots-clés : Adaptive control  control engineering computing  fuzzy genetic algorithms  least squares approximations  power system wind turbines Résumé : The wind power production spreading, also aided by the transition from constant to variable speed operation, involves the development of efficient control systems to improve the effectiveness of power production systems. This paper presents a data-driven design methodology able to generate a Takagi-Sugeno-Kang (TSK) fuzzy model for maximum energy extraction from variable speed wind turbines. In order to obtain the TSK model, fuzzy clustering methods for partitioning the input-output space, combined with genetic algorithms, and recursive least-squares optimization methods for model parameter adaptation are used. The implemented TSK fuzzy model, as confirmed by some simulation results on a doubly fed induction generator connected to a power system, exhibits high speed of computation, low memory occupancy, fault tolerance, and learning capability. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4458230&sortType%3Das [...] [article] Designing an adaptive fuzzy controller for maximum wind energy extraction [texte imprimé] / Galdi, V., Auteur ; Piccolo, A., Auteur ; Siano, P., Auteur . - 2008 . - pp. 559 - 569. Energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 23 n°2 (Juin 2008) . - pp. 559 - 569 Mots-clés : Adaptive control  control engineering computing  fuzzy genetic algorithms  least squares approximations  power system wind turbines Résumé : The wind power production spreading, also aided by the transition from constant to variable speed operation, involves the development of efficient control systems to improve the effectiveness of power production systems. This paper presents a data-driven design methodology able to generate a Takagi-Sugeno-Kang (TSK) fuzzy model for maximum energy extraction from variable speed wind turbines. In order to obtain the TSK model, fuzzy clustering methods for partitioning the input-output space, combined with genetic algorithms, and recursive least-squares optimization methods for model parameter adaptation are used. The implemented TSK fuzzy model, as confirmed by some simulation results on a doubly fed induction generator connected to a power system, exhibits high speed of computation, low memory occupancy, fault tolerance, and learning capability. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4458230&sortType%3Das [...]

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