Premium
Adaptive hybrid predictive control for a combined cycle power plant optimization
Author(s) -
Sáez D.,
Zúñiga R.,
Cipriano A.
Publication year - 2008
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.988
Subject(s) - model predictive control , control theory (sociology) , combined cycle , controller (irrigation) , turbine , power station , control engineering , mathematical optimization , computer science , engineering , control (management) , mathematics , artificial intelligence , mechanical engineering , agronomy , electrical engineering , biology
The design and development of an adaptive hybrid predictive controller for the optimization of a real combined cycle power plant (CCPP) are presented. The real plant is modeled as a hybrid system, i.e. logical conditions and dynamic behavior are used in one single modeling framework. Start modes, minimum up/down times and other logical features are represented using mixed integer equations, and dynamic behavior is represented using special linear models: adaptive fuzzy models. This approach allows the tackling of special non‐linear characteristics, such as ambient temperature dependence on electrical power production (combined cycle) and gas exhaust temperature (gas turbine) properly to fit into a mixed integer dynamic (MLD) model. After defining the MLD model, an adaptive predictive control strategy is developed in order to economically optimize the operation of a real CCPP of the Central Interconnected System in Chile. The economic results obtained by simulation tests provide a 3% fuel consumption saving compared to conventional strategies at regulatory level. Copyright © 2007 John Wiley & Sons, Ltd.