Özet:

The problem of optimal operation of electrical energy production systems is a difficult problem to solve with numerical methods in terms of the limitations it contains. A variety of optimization algorithms are often used to acceptable solutions to such problems in shorter periods of time. In recent years, such algorithms have been developed quite commonly in order to more stable and better solutions. In this study, the differential development algorithm (DE), which was previously successfully applied to solving many problems, integrated the concept of opposition-based learning in two different ways. Two different problem solutions have been made to test the performance and stability of the developed algorithms. The first of these problems is the optimization of the six multimodal test functions, and the second is the solution to the problem of optimal power distribution. The IEEE 30 bar with 6 generators system has been selected as an example for problem solving. Transmission line losses are calculated by making AC load flow using the Newton Raphson method. Finally, the values obtained from the solutions of both problems were compared with the performance of algorithms and the effects of the opposition-based learning concept on optimization algorithms were discussed.

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