Resource optimization and image processing for vegetation management programs in power distribution networks

Olga Jumbo and Ramin Moghaddass

Applied Energy, 2022, vol. 319, issue C, No S0306261922005967

Abstract: Vegetation management programs are some of the largest budget items for utility companies in their efforts to prevent power outages. Our work focuses on developing techniques to streamline vegetation management operations through detection of at-risk locations in a power distribution network. We have trained deep convolutional neural networks to segment vegetation and estimate the risk of outage using high resolution aerial imagery. The output of these networks helps in the optimization of resource allocation and vegetation management planning. Experimental results on the Bay Area Synthetic Network demonstrate high accuracy in segmentation and risk prediction, thus minimizing costs for vegetation management programs.

Keywords: Power distribution; Vegetation management; Deep learning; Semantic segmentation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922005967
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:319:y:2022:i:c:s0306261922005967

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.119234

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().