Orthogonal Genetic Algorithm Based Power System Restoration Path Optimization

Yunyun Xie*, Song Kunlong, Qiuwei Wu

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

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. In order 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. Firstly, 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 using the IEEE 118-bus test system and part of the Jiangsu power grid in China
Original languageEnglish
Article numbere2630
JournalInternational Transactions on Electrical Energy Systems
Volume28
Issue number12
Number of pages16
ISSN1430-144X
DOIs
Publication statusPublished - 2018

Keywords

  • Orthogonal experimental design
  • Orthogonal genetic algorithm
  • Path optimization
  • Power system restoration

Cite this

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title = "Orthogonal Genetic Algorithm Based Power System Restoration Path Optimization",
abstract = "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. In order 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. Firstly, 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 using the IEEE 118-bus test system and part of the Jiangsu power grid in China",
keywords = "Orthogonal experimental design, Orthogonal genetic algorithm, Path optimization, Power system restoration",
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journal = "International Transactions on Electrical Energy Systems",
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}

Orthogonal Genetic Algorithm Based Power System Restoration Path Optimization. / Xie, Yunyun; Kunlong, Song ; Wu, Qiuwei.

In: International Transactions on Electrical Energy Systems, Vol. 28, No. 12, e2630, 2018.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Orthogonal Genetic Algorithm Based Power System Restoration Path Optimization

AU - Xie, Yunyun

AU - Kunlong, Song

AU - Wu, Qiuwei

PY - 2018

Y1 - 2018

N2 - 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. In order 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. Firstly, 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 using the IEEE 118-bus test system and part of the Jiangsu power grid in China

AB - 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. In order 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. Firstly, 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 using the IEEE 118-bus test system and part of the Jiangsu power grid in China

KW - Orthogonal experimental design

KW - Orthogonal genetic algorithm

KW - Path optimization

KW - Power system restoration

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