Open AccessOrthogonal genetic algorithm based power system restoration path optimization
Author(s) -
Xie Yunyun,
Song Kunlong,
Wu Qiuwei,
Zhou Qian
Publication year - 2018
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2630
Subject(s) - crossover , mathematical optimization , genetic algorithm , blackout , path (computing) , algorithm , benchmark (surveying) , computer science , electric power system , grid , population , power (physics) , mathematics , artificial intelligence , physics , geometry , geodesy , demography , quantum mechanics , sociology , programming language , geography
Summary Optimizing the power system restoration path is a key issue for the system restoration after a blackout. Because the optimization is a complex nonlinear programming problem, artificial intelligent algorithms are widely employed to solve this problem due to its modeling flexibility and strong optimization capability. However, because the dimension of restoration path optimization is very high especially for large‐scale systems, artificial intelligent algorithms in current works are easy to be trapped in the local optima. To improve the optimal solution from the artificial intelligence algorithms, an orthogonal genetic algorithm is employed in this paper to optimize the restoration path, which can search the solution space in a statistically sound manner. First, the experimental design method was employed to generate orthogonal array as the initial population which was scattered uniformly over the feasible solution space. Then, the orthogonal crossover operator based on the orthogonal experimental design was employed to generate a small but representative feasible solution as the potential offspring. Finally, the proposed method is validated by using the IEEE 118‐bus test system and part of the Jiangsu power grid in China.