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A multiagent-system-based intelligent reference governor for multiobjective optimal power plant operation / Heo, J.S. in IEEE transactions on energy conversion, Vol. 23 n°4 (Décembre 2008) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 23 n°4 (Décembre 2008) . - pp. 1082 - 1092 Titre : A multiagent-system-based intelligent reference governor for multiobjective optimal power plant operation Type de document : texte imprimé Auteurs : Heo, J.S., Auteur ; K. Y. Lee, Auteur Année de publication : 2009 Article en page(s) : pp. 1082 - 1092 Note générale : Energy conversion Langues : Anglais (eng) Mots-clés : Large-scale  systems;  multi-agent  systems;  nonlinear  functions;  particle  swarm  optimisation;  power  engineering  computing;  thermal  power  stations Résumé : A large-scale power plant requires optimal set points, namely references, in several control loops for multiobjective optimal operation. In a 600-MW oil-fired drum-type boiler power unit, the set points considered are for the main steam pressure and reheater/superheater steam temperatures. The set points should be mapped with the varying unit load demand and satisfy the conflicting requirements in power plant operation. In practice, the set points are obtained using fixed nonlinear functions in the unit master control in a plant, which are designed for the single objective of load tracking with heat balance. However, it does not allow for process optimization under the multitude of conflicting objectives, which may be newly introduced and different from the initial design objective. This paper presents a methodology, multiagent-system-based intelligent reference governor (MAS-IRG), to realize the optimal mapping by searching for the best solution to the multiobjective optimization problem that tackles conflicting requirements. In searching for the optimal set points, a heuristic optimization tool, particle swarm optimization, is utilized to solve the multiobjective optimization problem. The IRG is designed based on the proposed MAS to operate at a higher level of automation, to execute asynchronous computations, and to reduce the computational complexity. The approach provides the means to specify optimal set points for controllers under a diverse operating scenarios online. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4675799&sortType%3Das [...] [article] A multiagent-system-based intelligent reference governor for multiobjective optimal power plant operation [texte imprimé] / Heo, J.S., Auteur ; K. Y. Lee, Auteur . - 2009 . - pp. 1082 - 1092. Energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 23 n°4 (Décembre 2008) . - pp. 1082 - 1092 Mots-clés : Large-scale  systems;  multi-agent  systems;  nonlinear  functions;  particle  swarm  optimisation;  power  engineering  computing;  thermal  power  stations Résumé : A large-scale power plant requires optimal set points, namely references, in several control loops for multiobjective optimal operation. In a 600-MW oil-fired drum-type boiler power unit, the set points considered are for the main steam pressure and reheater/superheater steam temperatures. The set points should be mapped with the varying unit load demand and satisfy the conflicting requirements in power plant operation. In practice, the set points are obtained using fixed nonlinear functions in the unit master control in a plant, which are designed for the single objective of load tracking with heat balance. However, it does not allow for process optimization under the multitude of conflicting objectives, which may be newly introduced and different from the initial design objective. This paper presents a methodology, multiagent-system-based intelligent reference governor (MAS-IRG), to realize the optimal mapping by searching for the best solution to the multiobjective optimization problem that tackles conflicting requirements. In searching for the optimal set points, a heuristic optimization tool, particle swarm optimization, is utilized to solve the multiobjective optimization problem. The IRG is designed based on the proposed MAS to operate at a higher level of automation, to execute asynchronous computations, and to reduce the computational complexity. The approach provides the means to specify optimal set points for controllers under a diverse operating scenarios online. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4675799&sortType%3Das [...]