Deriving to an Optimum Policy for Designing the Operating Parameters of Mahshahr Gas Turbine Power Plant Using a Self Learning Pareto Strategy
In the last decades, analyzing and optimizing the power plants based on thermodynamic laws and intelligent control techniques absorb an incremental interest of researchers. This is because deriving the efficient operating parameters for designing and optimizing the performance of power plants will lead to an acceptable investment and avoiding from discarding the energy. However, there are a few areas of application of mathematical optimization method. Optimizing the governing equations and designing parameters of power plants simultaneously leads to a multi-objective problem in industry. Some of these objectives are nonlinear, non-convex and multi-modal with different type of real life engineering constraints. In this paper a new method called Synchronous Parallel Shuffling Self Organized Pareto Strategy Algorithm (SPSSOPSA) is presented which synthesized evolutionary computing, swarm intelligence techniques and Time Adaptive Self Organizing Map(TASOM) simultaneously incorporating with a data shuffling behavior. Thereafter it will be applied to verifying the optimum decision making for parameter designing of Mahshahr power plant that produced about 117MW electricity, sited in Iran, as a multi-objective and multi-modal problem. The results show the deep relation of the unit cost on the change of the operating parameters.
Key words: Economic optimizing; Exergetic optimizing; Work output maximization; Evolutionary algorithm; Self organized map; Power plant
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