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

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  • Jumbo, Olga
  • Moghaddass, Ramin

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.

Suggested Citation

  • Jumbo, Olga & Moghaddass, Ramin, 2022. "Resource optimization and image processing for vegetation management programs in power distribution networks," Applied Energy, Elsevier, vol. 319(C).
    • Handle: RePEc:eee:appene:v:319:y:2022:i:c:s0306261922005967
    DOI: 10.1016/j.apenergy.2022.119234
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    References listed on IDEAS

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    Cited by:

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    2. Zhang, Jinlai & Yang, Wenjie & Chen, Yumei & Ding, Mingkang & Huang, Huiling & Wang, Bingkun & Gao, Kai & Chen, Shuhan & Du, Ronghua, 2024. "Fast object detection of anomaly photovoltaic (PV) cells using deep neural networks," Applied Energy, Elsevier, vol. 372(C).

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