| Title |
Determining the Best Fitness Function of Genetic Algorithm for Improved Fault Recovery in Substation |
| Authors |
이경민(Kyung-Min Lee) ; 홍재영(Jae-Young Hong) ; 채창기(Chang-Gi Chae) ; 강태원(Tae-Won Kang) ; 박철원(Chul-Won Park) |
| DOI |
https://doi.org/10.5370/KIEE.2020.69.6.745 |
| Keywords |
Artificial intelligence technology; Constraints; Fitness function; Genetic algorithm; Optimization problems; Substation fault recovery |
| Abstract |
This article, as a part of the research for intelligent fault recovery in substation, studied the determination of Genetic Algorithm (GA)'s the best fitness function for improved fault recovery of substation. First, in order to apply GA for improved fault recovery, constraints and fitness functions were chosen in consideration of Main Transformer (M.Tr) overload brake, blackout area minimizing.
A GA-based fault recovery support system is designed through the selected constraints and fitness functions. The initial settings for the proposed GA system were population, survival rate, maximum number of generations, and mutation probability. In order to determine the optimal fitness function, the simulation is performed by varying the weights of the proposed three fitness functions from 10 to 50. Finally, as a result of the simulation, an the best fitness function is determined, and through this, an optimal recovery path is searched for substation fault recovery. |