Increasing resilience of power systems using intentional islanding; a comparison of Binary genetic algorithm and deep learning based method

被引：2

作者：

Paradell, Pol ^{[1
]}

Spyridis, Yannis ^{[2
]}

Colet, Alba ^{[1
]}

Ivanova, Anzhelika ^{[1
]}

Luis Dominguez -Garcia, Jose ^{[1
]}

Sesis, Achilleas ^{[2
]}

Efstathopoulos, Georgios ^{[1
]}

机构：

[1] Inst Energy Res Catalonia IREC, Elect & Power Elect Dept, Barcelona, Spain

[2] 0 Infin Ltd, Imperial Off, London E6 2JG, England

来源：

PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND RESILIENCE (IEEE CSR) | 2021年

关键词：

intentional islanding; binary genetic algorithm; deep learning; graph convolutional networks; power system; OPTIMIZATION;

D O I：

10.1109/CSR51186.2021.9527918

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Several algorithms combining qualitative and quantitative components are currently used for splitting a large interconnected power grid into islands as a measure to provide the best reconfiguration option when a fault appears. The aim of this article is to compare the clustering results of a binary genetic algorithm and a deep learning based method in order to identify the differences and to find in which cases it is rather better applicable each of the techniques.

引用

页码：491 / 496

页数：6

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