Skip to main content
SpringerLink
Log in

Assets management on electrical grid using Faster-RCNN

  • S.I.: Artificial Intelligence in Operations Management
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Amadi-Echendu, J. E., Willett, R., Brown, K., Hope, T., Lee, J., Mathew, J., et al. (2010). What is engineering asset management? In Definitions, concepts and scope of engineering asset management (pp. 3–16). London: Springer.

    Google Scholar 

  • Campoy, P., Garcia, P. J., Barrientos, A., del Cerro, J., Aguirre, I., Roa, A., Garcia, R., & Munoz, J. M. (2001). An stereoscopic vision system guiding an autonomous helicopter for overhead power cable inspection. In International workshop on robot vision (pp. 115–124), Springer, Berlin.

  • Chakravorty, G., Awasthi, A., Da Silva, B., & Singhal, M. (2018). Deep learning based global tactical asset allocation.

  • Chen, Y. T., Sun, E. W., & Lin, Y. B. (2019). Coherent quality management for big data systems: A dynamic approach for stochastic time consistency. Annals of Operations Research, 277(1), 3–32.

    Article  Google Scholar 

  • Davidson, I. E. (2005). Utility asset management in the electrical power distribution sector. In 2005 IEEE power engineering society inaugural conference and exposition in Africa (pp. 338–343), IEEE.

  • Fang, K., Uhan, N. A., Zhao, F., & Sutherland, J. W. (2016). Scheduling on a single machine under time-of-use electricity tariffs. Annals of Operations Research, 238(1–2), 199–227.

    Article  Google Scholar 

  • Fathi, A., Korattikara, A., Sun, C., Fischer, I., Huang, J., Murphy, K., Zhu, M., Guadarrama, S., Rathod, V., Song, Y., & Wojna, Z. (2017). Speed and accuracy trade-offs for modern convolutional object detectors.

  • Froger, A., Gendreau, M., Mendoza, J. E., Pinson, E., & Rousseau, L. M. (2016). Maintenance scheduling in the electricity industry: A literature review. European Journal of Operational Research, 251(3), 695–706.

    Article  Google Scholar 

  • Gaivoronski, A. A., & De Lange, P. E. (2000). An asset liability management model for casualty insurers: complexity reduction versus parameterized decision rules. Annals of Operations Research, 99(1–4), 227–250.

    Article  Google Scholar 

  • Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440-448).

  • Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580–587).

  • Guajardo, M., Ronnqvist, M., Halvorsen, A. M., & Kallevik, S. I. (2015). Inventory management of spare parts in an energy company. Journal of the Operational Research Society, 66(2), 331–41.

    Article  Google Scholar 

  • International Organization for Standardization. (2014). ISO 55000: Asset management-overview. International Organization for Standardization: Principles and Terminology.

  • Kaplan, S. M. (2009). Electric power transmission: Background and policy issues. Congressional Research Service: Library of Congress.

  • Khuntia, S. R., Rueda, J. L., Bouwman, S., & van der Meijden, M. A. (2016). A literature survey on asset management in electrical power [transmission and distribution] system. International Transactions on Electrical Energy Systems, 26(10), 2123–2133.

    Article  Google Scholar 

  • Khuntia, S. R., Rueda, J. L., & van der Meijden, M. A. (2019). Smart asset management for electric utilities: Big data and future. In Asset intelligence through integration and interoperability and contemporary vibration engineering technologies (pp. 311–322), Springer, Cham.

  • Kostic, T. (2003). Asset management in electrical utilities: How many facets it actually has. In 2003 IEEE power engineering society general meeting (IEEE Cat. No. 03CH37491) (Vol. 1, pp. 275–281), IEEE.

  • Lu, M., & Wang, Z. (2017). Rebound effects for residential electricity use in urban China: An aggregation analysis based EIO and scenario simulation. Annals of Operations Research, 255(1–2), 525–546.

    Article  Google Scholar 

  • Mavrotas, G., Diakoulaki, D., & Capros, P. (2003). Combined MCDA-IP approach for project selection in the electricity market. Annals of Operations Research, 120(1–4), 159–170.

    Article  Google Scholar 

  • Mulvey, J. M., Madsen, C., & Morin, F. (1999). Linking strategic and tactical planning systemsfor asset and liability management. Annals of Operations Research, 85, 249–266.

    Article  Google Scholar 

  • Nieto, D., Amatti, J. C., & Mombello, E. (2017). Review of asset management in distribution systems of electric energy–implications in the national context and Latin America. CIRED-Open Access Proceedings Journal, 2017(1), 2879–2882.

    Article  Google Scholar 

  • Oberweger, M., Wendel, A., & Bischof, H. (2014). Visual recognition and fault detection for power line insulators. In 19th Computer vision winter workshop (pp. 1–8).

  • Omonfoman, O. (2016). Electricity Distribution Companies—The challenges and way forward. premiumtimesng. com: http://opinion.premiumtimesng.com/2016/01/04/electricity-distribution-companies-the-challenges-and-way-forward-by-odion-omonfoman/.

  • PAS, B. (2008). 55-1: Asset management. Part 1: Specification for the optimized management of physical assets. British Standards Institution.

  • Pagnano, A., Hopf, M., & Teti, R. (2013). A roadmap for automated power line inspection. Maintenance and repair. Procedia Cirp, 12, 234–239.

    Article  Google Scholar 

  • Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.

    Article  Google Scholar 

  • Pineau, P. O., & Murto, P. (2003). An oligopolistic investment model of the Finnish electricity market. Annals of Operations Research, 121(1–4), 123–148.

    Article  Google Scholar 

  • Ren, S., He, K., Girshick, R. S., & Jun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91–99).

  • Scala, N. M., Rajgopal, J., & Needy, K. L. (2009). Risk and spare parts inventory in electric utilities. In Proceedings of the industrial and systems engineering research conference, Institute of Industrial Engineers.

  • Vanier, D. D. (2001). Why industry needs asset management tools. Journal of Computing in Civil Engineering, 15(1), 35–43.

    Article  Google Scholar 

  • Velasquez-Contreras, J. L., Sanz-Bobi, M. A., & Arellano, S. G. (2011). General asset management model in the context of an electric utility: Application to power transformers. Electric Power Systems Research, 81(11), 2015–2037.

    Article  Google Scholar 

  • Wang, T., Chen, Y., Qiao, M., & Snoussi, H. (2018b). A fast and robust convolutional neural network-based defect detection model in product quality control. The International Journal of Advanced Manufacturing Technology, 94(9–12), 3465–3471.

    Article  Google Scholar 

  • Wang, K., Lee, C. Y., Zhang, J., & Wei, Y. M. (2018a). Operational performance management of the power industry: A distinguishing analysis between effectiveness and efficiency. Annals of Operations Research, 268(1–2), 513–537.

    Article  Google Scholar 

  • Xidonas, P., Hassapis, C., Mavrotas, G., Staikouras, C., & Zopounidis, C. (2018). Multiobjective portfolio optimization: Bridging mathematical theory with asset management practice. Annals of Operations Research, 267(1–2), 585–606.

    Article  Google Scholar 

  • Yoon, A. S., Lee, T., Lim, Y., Jung, D., Kang, P., Kim, D., Park, K., & Choi, Y. (2017). Semi-supervised learning with deep generative models for asset failure prediction. arXiv preprint arXiv:1709.00845.

  • Zhang, D., Han, X., & Deng, C. (2018). Review on the research and practice of deep learning and reinforcement learning in smart grids. CSEE Journal of Power and Energy Systems, 4(3), 362–370.

    Article  Google Scholar 

  • Zhao, G., Zhang, G., Ge, Q., & Liu, X. (2016). Research advances in fault diagnosis and prognostic based on deep learning. In 2016 Prognostics and system health management conference (PHM-Chengdu) (pp. 1–6), IEEE.

Download references

Acknowledgements

The funding was provided by CIE (Companie Ivoiriene d’Electricite) (Grand No. 01).

Author information

Authors and Affiliations

  1. CIE-Centre de Recherche en Intelligence Artificielle, Abidjan, Côte d’Ivoire

    Jules Raymond Kala, Didier Michael Kre, Armelle N’Guessan Gnassou, Yves Melaine Akpablin Akpablin & Tiorna Coulibaly

  2. Universite Catholique d’Afrique Centrale, Yaoundé, Cameroon

    Jean Robert Kamdjoug Kala

Authors
  1. Jules Raymond Kala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Didier Michael Kre
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Armelle N’Guessan Gnassou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jean Robert Kamdjoug Kala
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yves Melaine Akpablin Akpablin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tiorna Coulibaly
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jules Raymond Kala.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kala, J.R., Kre, D.M., Gnassou, A.N. et al. Assets management on electrical grid using Faster-RCNN. Ann Oper Res 308, 307–320 (2022). https://doi.org/10.1007/s10479-020-03650-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-020-03650-4

Keywords

Search

Navigation