Multi-objective Optimization of Backbone Power Grid Considering Multi-stage Disaster Resilience

被引：0

作者：

Jin, Weichao ^{[1
]}

Han, Chang ^{[1
]}

Yang, Li ^{[1
]}

Lin, Zhenzhi ^{[1
]}

Gao, Qiang ^{[2
]}

Ying, Guode ^{[2
]}

机构：

[1] College of Electrical Engineering, Zhejiang University, Hangzhou,310027, China

[2] Taizhou Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Taizhou,318000, China

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2020年 / 44卷 / 15期

基金：

中国国家自然科学基金;

关键词：

Multiobjective optimization - Network security - Electric power transmission networks - Learning systems - Principal component analysis - Swarm intelligence - Disasters - Learning algorithms - Topology;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Constructing and differentially strengthening anti-disaster backbone power grids can guarantee the power grid security operation and power supply for important loads in extreme disasters. This paper proposes a multi-objective optimization model of backbone power grid considering multi-stage disaster resilience. In this model, the topology and operation importance of nodes and lines in power grids are evaluated quantitatively, and a comprehensive evaluation method based on kernel principal component analysis (KPCA) is proposed. The anti-disaster backbone power grid is optimized for maximizing the survivability, invulnerability and restorability of the power grids within investment constraints. Then, the graph repair strategy and the files learning strategy are embedded in the improved comprehensive learning particle swarm optimization (ICLPSO) algorithm to solve the optimization model, which expands the feasible solution space. Finally, the effectiveness of the proposed model is verified by the simulation results of a regional power grid. © 2020 Automation of Electric Power Systems Press.

引用

页码：52 / 61

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