Optimal Decision-making Method of Wind-proof and Disaster-resistant Reinforcement Measures for Distribution Network

Xin GAO; Fei TANG; Tongyan ZHANG; Yu LI

doi:10.12096/j.2096-4528.pgt.20126

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Optimal Decision-making Method of Wind-proof and Disaster-resistant Reinforcement Measures for Distribution Network

Power System Planning | 更新时间：2025-07-04

- Optimal Decision-making Method of Wind-proof and Disaster-resistant Reinforcement Measures for Distribution Network
- Power Generation Technology Vol. 42, Issue 1, Pages: 78-85(2021)
- 作者机构：
  
  武汉大学电气与自动化学院, 湖北省武汉市 430072
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51977157)
- DOI：10.12096/j.2096-4528.pgt.20126
  CLC： TK01;TM715
- Received：03 January 2021，
  
  Published：28 February 2021
- 稿件说明：
移动端阅览
Xin GAO, Fei TANG, Tongyan ZHANG, et al. Optimal Decision-making Method of Wind-proof and Disaster-resistant Reinforcement Measures for Distribution Network[J]. Power Generation Technology, 2021, 42(1): 78-85.
DOI：

Xin GAO, Fei TANG, Tongyan ZHANG, et al. Optimal Decision-making Method of Wind-proof and Disaster-resistant Reinforcement Measures for Distribution Network[J]. Power Generation Technology, 2021, 42(1): 78-85. DOI： 10.12096/j.2096-4528.pgt.20126.

摘要

台风是常见的自然灾害之一，会严重影响配电网基础设施的正常运行。通过加固系统元件（如导线和电杆）提升其抗灾强度，是降低由极端天气事件所造成损失的一种有效方法。为此，提出了考虑经济性约束的配电网基础设施灾前加固策略。首先对极端天气自然灾害发生、元件脆弱性、灾害对配电网影响进行建模，并利用蒙特卡罗仿真方法评估配电网的抗灾能力。然后，将配电网加固策略问题作为优化问题，综合考虑总加固预算约束、负荷的重要度和配电网元件脆弱性，并通过粒子群优化算法求解最优加固策略。最后，将所提出策略应用于改进的18节点配电系统以验证其有效性。

Abstract

Typhoon is one of the common natural disasters

which will seriously affect the normal operation of the distribution network infrastructure. Reinforcing system infrastructure (such as wires and poles) to improve their disaster resilience is an effective way to reduce losses caused by extreme weather events. Therefore

this paper proposes a pre-disaster reinforcement strategy for the distribution network infrastructure considering economic constraints. Firstly

the models of occurrence of extreme weather

component fragility and the disaster response process were created. Then

the disaster resilience of distribution network was evaluated via Monte Carlo simulation. The reinforcement strategy was further optimized through the particle swarm optimization algorithm

considering the total reinforcement budget

the load importance and the component fragility. Finally

the proposed strategy was applied to an improved 18-bus distribution system to verify its effectiveness.

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references

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