IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v308y2022i1d10.1007_s10479-020-03650-4.html
   My bibliography  Save this article

Assets management on electrical grid using Faster-RCNN

Author

Listed:
  • Jules Raymond Kala

    (CIE-Centre de Recherche en Intelligence Artificielle)

  • Didier Michael Kre

    (CIE-Centre de Recherche en Intelligence Artificielle)

  • Armelle N’Guessan Gnassou

    (CIE-Centre de Recherche en Intelligence Artificielle)

  • Jean Robert Kamdjoug Kala

    (Universite Catholique d’Afrique Centrale)

  • Yves Melaine Akpablin Akpablin

    (CIE-Centre de Recherche en Intelligence Artificielle)

  • Tiorna Coulibaly

    (CIE-Centre de Recherche en Intelligence Artificielle)

Abstract

Electrical utility companies around the world are keeping track of all equipment on their distribution grid, because it will help them improve the management and the quality of the services they offer to their customers. Asset management of the electric grid is usually conducted manually, which is expensive, time consuming and the results obtained are often not accurate. In this article an automated asset management system for electricity, transport infrastructures is proposed, it is based on images taken by drones and analysed by Faster Region proposal Convolutional Neural Networks (Faster-RCNN) to generate the inventory. The designs of CNN are inspired from the human brain structures, they have been applied to many fields such as object recognition and crowed counting with promising results that are proven to be better than human observer. In order to evaluate the proposed asset management approach, a sample of images was randomly selected from a given dataset, the inventory results generated by the CNN based model are accurate, faster and cheaper than the previous approach based on human observers and helicopters.

Suggested Citation

  • Jules Raymond Kala & Didier Michael Kre & Armelle N’Guessan Gnassou & Jean Robert Kamdjoug Kala & Yves Melaine Akpablin Akpablin & Tiorna Coulibaly, 2022. "Assets management on electrical grid using Faster-RCNN," Annals of Operations Research, Springer, vol. 308(1), pages 307-320, January.
    • Handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03650-4
    DOI: 10.1007/s10479-020-03650-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-020-03650-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-020-03650-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ke Wang & Chia-Yen Lee & Jieming Zhang & Yi-Ming Wei, 2018. "Operational performance management of the power industry: a distinguishing analysis between effectiveness and efficiency," Annals of Operations Research, Springer, vol. 268(1), pages 513-537, September.
    2. John Mulvey & Chris Madsen & François Morin, 1999. "Linking strategic and tactical planning systemsfor asset and liability management," Annals of Operations Research, Springer, vol. 85(0), pages 249-266, January.
    3. Froger, Aurélien & Gendreau, Michel & Mendoza, Jorge E. & Pinson, Éric & Rousseau, Louis-Martin, 2016. "Maintenance scheduling in the electricity industry: A literature review," European Journal of Operational Research, Elsevier, vol. 251(3), pages 695-706.
    4. Panos Xidonas & Christis Hassapis & George Mavrotas & Christos Staikouras & Constantin Zopounidis, 2018. "Multiobjective portfolio optimization: bridging mathematical theory with asset management practice," Annals of Operations Research, Springer, vol. 267(1), pages 585-606, August.
    5. Kan Fang & Nelson A. Uhan & Fu Zhao & John W. Sutherland, 2016. "Scheduling on a single machine under time-of-use electricity tariffs," Annals of Operations Research, Springer, vol. 238(1), pages 199-227, March.
    6. Kan Fang & Nelson Uhan & Fu Zhao & John Sutherland, 2016. "Scheduling on a single machine under time-of-use electricity tariffs," Annals of Operations Research, Springer, vol. 238(1), pages 199-227, March.
    7. Pierre-Olivier Pineau & Pauli Murto, 2003. "An Oligopolistic Investment Model of the Finnish Electricity Market," Annals of Operations Research, Springer, vol. 121(1), pages 123-148, July.
    8. Alexei Gaivoronski & Petter de Lange, 2000. "An Asset Liability Management Model for Casualty Insurers: Complexity Reduction vs. Parameterized Decision Rules," Annals of Operations Research, Springer, vol. 99(1), pages 227-250, December.
    9. G. Mavrotas & D. Diakoulaki & P. Capros, 2003. "Combined MCDA–IP Approach for Project Selection in the Electricity Market," Annals of Operations Research, Springer, vol. 120(1), pages 159-170, April.
    10. Mario Guajardo & Mikael Rönnqvist & Ann Mari Halvorsen & Svein Inge Kallevik, 2015. "Inventory management of spare parts in an energy company," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(2), pages 331-341, February.
    11. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2019. "Coherent quality management for big data systems: a dynamic approach for stochastic time consistency," Annals of Operations Research, Springer, vol. 277(1), pages 3-32, June.
    12. Milin Lu & Zhaohua Wang, 2017. "Rebound effects for residential electricity use in urban China: an aggregation analysis based E-I-O and scenario simulation," Annals of Operations Research, Springer, vol. 255(1), pages 525-546, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.
    2. Chen, Bo & Zhang, Xiandong, 2019. "Scheduling with time-of-use costs," European Journal of Operational Research, Elsevier, vol. 274(3), pages 900-908.
    3. Ghorbanzadeh, Masoumeh & Ranjbar, Mohammad, 2023. "Energy-aware production scheduling in the flow shop environment under sequence-dependent setup times, group scheduling and renewable energy constraints," European Journal of Operational Research, Elsevier, vol. 307(2), pages 519-537.
    4. Shun Jia & Yang Yang & Shuyu Li & Shang Wang & Anbang Li & Wei Cai & Yang Liu & Jian Hao & Luoke Hu, 2024. "The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    5. Michal Penn & Tal Raviv, 2021. "Complexity and algorithms for min cost and max profit scheduling under time-of-use electricity tariffs," Journal of Scheduling, Springer, vol. 24(1), pages 83-102, February.
    6. Seokgi Lee & Mona Issabakhsh & Hyun Woo Jeon & Seong Wook Hwang & Byung Chung, 2020. "Idle time and capacity control for a single machine scheduling problem with dynamic electricity pricing," Operations Management Research, Springer, vol. 13(3), pages 197-217, December.
    7. Xiangxin An & Guojin Si & Tangbin Xia & Qinming Liu & Yaping Li & Rui Miao, 2022. "Operation and Maintenance Optimization for Manufacturing Systems with Energy Management," Energies, MDPI, vol. 15(19), pages 1-19, October.
    8. Shen, Liji & Dauzère-Pérès, Stéphane & Maecker, Söhnke, 2023. "Energy cost efficient scheduling in flexible job-shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 310(3), pages 992-1016.
    9. Wu, Xueqi & Che, Ada, 2020. "Energy-efficient no-wait permutation flow shop scheduling by adaptive multi-objective variable neighborhood search," Omega, Elsevier, vol. 94(C).
    10. Wu, Xueqi & Che, Ada, 2019. "A memetic differential evolution algorithm for energy-efficient parallel machine scheduling," Omega, Elsevier, vol. 82(C), pages 155-165.
    11. Tian, Zheng & Zheng, Li, 2024. "Single machine parallel-batch scheduling under time-of-use electricity prices: New formulations and optimisation approaches," European Journal of Operational Research, Elsevier, vol. 312(2), pages 512-524.
    12. Ruilin Pan & Qiong Wang & Zhenghong Li & Jianhua Cao & Yongjin Zhang, 2022. "Steelmaking-continuous casting scheduling problem with multi-position refining furnaces under time-of-use tariffs," Annals of Operations Research, Springer, vol. 310(1), pages 119-151, March.
    13. Aghelinejad, MohammadMohsen & Ouazene, Yassine & Yalaoui, Alice, 2019. "Complexity analysis of energy-efficient single machine scheduling problems," Operations Research Perspectives, Elsevier, vol. 6(C).
    14. Lin Chen & Nicole Megow & Roman Rischke & Leen Stougie & José Verschae, 2021. "Optimal algorithms for scheduling under time-of-use tariffs," Annals of Operations Research, Springer, vol. 304(1), pages 85-107, September.
    15. Gaggero, Mauro & Paolucci, Massimo & Ronco, Roberto, 2023. "Exact and heuristic solution approaches for energy-efficient identical parallel machine scheduling with time-of-use costs," European Journal of Operational Research, Elsevier, vol. 311(3), pages 845-866.
    16. Peng Wu & Junheng Cheng & Feng Chu, 2021. "Large-scale energy-conscious bi-objective single-machine batch scheduling under time-of-use electricity tariffs via effective iterative heuristics," Annals of Operations Research, Springer, vol. 296(1), pages 471-494, January.
    17. Heydar, Mojtaba & Mardaneh, Elham & Loxton, Ryan, 2022. "Approximate dynamic programming for an energy-efficient parallel machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 302(1), pages 363-380.
    18. ManMohan S. Sodhi, 2005. "LP Modeling for Asset-Liability Management: A Survey of Choices and Simplifications," Operations Research, INFORMS, vol. 53(2), pages 181-196, April.
    19. Hongliang Zhang & Yujuan Wu & Ruilin Pan & Gongjie Xu, 2021. "Two-stage parallel speed-scaling machine scheduling under time-of-use tariffs," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 91-112, January.
    20. Panos Xidonas & Haris Doukas & George Mavrotas & Olena Pechak, 2016. "Environmental corporate responsibility for investments evaluation: an alternative multi-objective programming model," Annals of Operations Research, Springer, vol. 247(2), pages 395-413, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03650-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.