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 to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Pineau, P. O., & Murto, P. (2003). An oligopolistic investment model of the Finnish electricity market. Annals of Operations Research, 121(1–4), 123–148.
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.
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.
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.
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.
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.
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.
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.
The funding was provided by CIE (Companie Ivoiriene d’Electricite) (Grand No. 01).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
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 (2020). https://doi.org/10.1007/s10479-020-03650-4
- Convolutional neural network