Evaluation of Power Transmission Lines Hardening Scenarios Using a Machine Learning Approach
Evaluation of Power Transmission Lines Hardening Scenarios Using a Machine Learning Approach
复制标题
使用机器学习方法评估输电线路加固场景
DOI：
10.1115/1.4063012
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Jensen, Michael P.
中科院分区：
文献类型：
--
作者：
Montoya-Rincon, Juan P.;Gonzalez-Cruz, Jorge E.;Jensen, Michael P.
输电基础设施易受极端天气事件的影响，特别是飓风和热带风暴。最近的一个例子是2017年9月飓风玛丽亚（H-Maria）在波多黎各群岛造成的破坏，输电基础设施的重大故障导致全面停电。已经进行了大量的研究来检查加强输电系统的策略，包括将电力线埋在地下或增加树木修剪的频率。然而，很少有研究集中在输电塔的直接硬化，以实现弹性的增加。这项基于机器学习的研究通过分析三种直接强化场景并确定这些变化在H-Maria环境中的有效性来满足这一需求。本文提出了一种输电塔损伤评估的方法，并分析了用弹性较大的结构替换失效率较高的结构的影响。我们发现，钢自支撑杆是最好的替代方案的塔与高故障率。此外，在第三种加固方案中，所有木柱都被更换，单线损坏塔的最大减少率为66%，而每条线路损坏塔的平均数量减少了10%。
The power transmission infrastructure is vulnerable to extreme weather events, particularly hurricanes and tropical storms. A recent example is the damage caused by Hurricane Maria (H-Maria) in the archipelago of Puerto Rico in September 2017, where major failures in the transmission infrastructure led to a total blackout. Numerous studies have been conducted to examine strategies to strengthen the transmission system, including burying the power lines underground or increasing the frequency of tree trimming. However, few studies focus on the direct hardening of the transmission towers to accomplish an increase in resiliency. This machine learning-based study fills this need by analyzing three direct hardening scenarios and determining the effectiveness of these changes in the context of H-Maria. A methodology for estimating transmission tower damage is presented here as well as an analysis of impact of replacing structures with a high failure rate with more resilient ones. We found the steel self-support-pole to be the best replacement option for the towers with high failure rate. Furthermore, the third hardening scenario, where all wooden poles were replaced, exhibited a maximum reduction in damaged towers in a single line of 66% while lowering the mean number of damaged towers per line by 10%.