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A parameter tuning for dynamic simulation of power plants using genetic algorithms
Author(s) -
Miyamoto Yuichi,
Miyatake Tatsuya,
Kurosaka Soh,
Mori Yoshinobu
Publication year - 1995
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.4391150110
Subject(s) - fitness function , genetic algorithm , selection (genetic algorithm) , power (physics) , function (biology) , dynamic simulation , power station , algorithm , mathematical optimization , computer science , engineering , mathematics , simulation , artificial intelligence , physics , quantum mechanics , evolutionary biology , electrical engineering , biology
This paper reports a method of dynamic simulation parameter tuning for a coal‐fired power plant using genetic algorithms (GA). GA is a search algorithm based on the mechanics of natural selection and natural genetics. GA also is one of the effective methods for optimization problems and it requires a formalization of problems and a fitness function definition. Because the dynamic simulation can be repeated and the error between real data and simulation results is defined by a fitness function, it is reasonable to consider genetic operations. To satisfy environmental laws and regulations, the control of NO x emission is important for the operation of power plants. Therefore, an NO x analytical model is designed and used for the dynamic simulation at the change of loads, the kind of coal, and so on. In this paper, the optimal parameters for the simulation are determined by GA.