M.Sc. Tezi Görüntüleme

      Computers have gained the ability to act like human beings in decision making as a result of studies on artificial intelligence for over thirty years. With the help of algorithms, the computers with simple logical techniques solve very complex mathematical calculations. Genetic algorithms (GA), as being one of these techniques, have become very popular to solve optimization problems. Genetic algorithms use a derivative free approach that is similar to the process of evaluation in nature. The system eliminates weaker rules, and the stronger ones compete among themselves. Genetic Algorithms have the ability to simultaneously search several solutions and combine the best of these to come up with a progressively better solution. GAs do not require an exact mathematical modeling of every problem to be solved, because some problems don't even contain a clear mathematical model.

      GAs are used widely in power systems especially in optimization problems such as optimizing input costs to the generating units. The input costs of thermal generating units in an interconnected power system are optimized in this thesis using GA. The effects of crossover, mutation and population size, which are used by GA as a part of the solution algorithm, are explained in detail with numerical examples. The results obtained using GAs are compared with those of classical methods for validation. It is shown that GAs can be used as an alternative method in the economic dispatch problem in power systems.

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