| کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن | 
|---|---|---|---|---|
| 6859701 | 1438733 | 2015 | 7 صفحه PDF | دانلود رایگان | 
عنوان انگلیسی مقاله ISI
												Constructing core backbone network based on survivability of power grid
												
											ترجمه فارسی عنوان
													ساخت شبکه اصلی ستون فقرات بر اساس پایداری شبکه برق 
													
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																																												کلمات کلیدی
												
											موضوعات مرتبط
												
													مهندسی و علوم پایه
													مهندسی کامپیوتر
													هوش مصنوعی
												
											چکیده انگلیسی
												Constructing core backbone network is beneficial to strengthen the construction of grid structure, raise the ability of withstanding natural disasters, as well as realize power grid's differentiation planning reasonably and scientifically. Based on the index system of survivability, a method of constructing core backbone network with the target of the smallest line total length and the largest integrated survivability index is put forward with constraint conditions of network connectivity and power grid safe operation. The cosine migration model, the premature judgment mechanism, and the mutative scale of mutation strategy by Chaos and Cauchy optimization are introduced into the improved biogeography-based optimization algorithm (BBO) to search for the optimal solution of the core backbone network. Comparison with the traditional BBO algorithm, particle swarm optimization (PSO), binary ant colony algorithm (BACA), genetic algorithm (GA) shows that the proposed method is accurate and effective, and it has advantages in fast convergence speed and high convergence precision.
											ناشر
												Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Electrical Power & Energy Systems - Volume 67, May 2015, Pages 161-167
											Journal: International Journal of Electrical Power & Energy Systems - Volume 67, May 2015, Pages 161-167
نویسندگان
												Feifei Dong, Dichen Liu, Jun Wu, Lina Ke, Chunli Song, Haolei Wang, Zhenshan Zhu, 
											