Evaluation of Power Transmission Lines Hardening Scenarios Using a Machine Learning Approach

Montoya-Rincon, Juan P.; Gonzalez-Cruz, Jorge E.; Jensen, Michael P.

doi:10.1115/1.4063012

Title: Evaluation of Power Transmission Lines Hardening Scenarios Using a Machine Learning Approach

Journal Article · 03 August 2023 · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering

DOI: https://doi.org/10.1115/1.4063012 · OSTI ID:1996623

Montoya-Rincon, Juan P. ^[1];

^[2]; Jensen, Michael P. ^[3]

City College of New York, NY (United States)
University at Albany, NJ (United States)
Brookhaven National Laboratory (BNL), Upton, NY (United States)

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 in this study 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%.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: SC0012704

OSTI ID:: 1996623

Report Number(s):: BNL-224677-2023-JAAM; GRP-ACM-36

Journal Information:: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering, Journal Name: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering Journal Issue: 3 Vol. 9; ISSN 2332-9017

Publisher:: ASMECopyright Statement

Country of Publication:: United States

Language:: English

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References (19)

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