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[1] Digital Transformation of Industries. Electricity Industry. World Economic Forum White Paper, 2016, https://www.accenture.com/_acnmedia/Accenture/Conversion- Assets/WEF/PDF/Accenture-Electricity-Industry.pdf (accessed 04 June 2019).
[2] The Digital Energy Transformation, https://www.ge.com/content/dam/gepower-pw/ global/en_US/documents/hybrid/des/GE_Digital_Transformation.PDF (accessed 04 June 2019).
[3] Butler, C., Parkhill, K.A. & Luzecka, P., Rethinking energy demand governance: Exploring impact beyond ‘energy’ policy. Technological Energy Research & Social Science, 36, pp. 70–78, 2018. [Crossref]
[4] Chebotareva, G.S., Impact of state support mechanisms on the cost of renewable energy projects: The case of developing countries. WIT Transactions on Ecology and the Environment, 217, pp. 881–891, 2018. [Crossref]
[5] Goldbach, K., Rotaru, A.M., Reichert, S., Stiff, G. & Gölz, S., Which digital energy ser- vices improve energy efficiency? A multi-criteria investigation with European experts, Energy Policy, 115, pp. 239–248, 2018. [Crossref]
[6] Morley, J., Widdicks, K. & Hazas, M., Digitalisation, energy and data demand: The impact of Internet traffic on overall and peak electricity consumption. Energy Research & Social Science, 38, pp. 128–137, 2018. [Crossref]
[7] Baev, I., Dzyuba, A., Solovyeva, I. & Kuzmina, N., Improving the efficiency of using small-distributed generation systems through mechanisms of demand management for electricity and gas. International Journal of Energy Production and Management, 3(4), pp. 277–291, 2018. [Crossref]
[8] Wissner, M., The Smart Grid – A saucerful of secrets? Applied Energy, 88(7), pp. 2509–2518, 2011. [Crossref]
[9] Johnstone, P. & Kivimaa, P., Multiple dimensions of disruption, energy transitions and industrial policy. Energy Research & Social Science, 37, pp. 260–265, 2018. https://doi. org/10.1016/j.erss.2017.10.027
[10] Gitelman, L., Magaril, E., Kozhevnikov, M. & Rada, E.C., Rational Behavior of an Enterprise in the Energy Market in a Circular Economy, Resources, 8(2), p. 73, 2019. [Crossref]
[11] Larsen, P.H., A method to estimate the costs and benefits of undergrounding electricity transmission and distribution lines. Energy Economics, 60, pp. 47–61, 2016. https://doi. org/10.1016/j.eneco.2016.09.011
[12] Xie, Y., Li, C., Lv, Y. & Yu, C., Predicting lightning outages of transmission lines using generalized regression neural network. Applied Soft Computing, 78, pp. 438–446, 2019. [Crossref]
[13] Kishore, T.S. & Singal, S.K., Optimal economic planning of power transmission lines: A review. Renewable and Sustainable Energy Reviews, 39, pp. 949–974. https://doi. org/10.1016/j.asoc.2018.09.042
[14] Mesić, M. & Plavšić, T., The contribution of failure analyses to transmission network maintenance preferentials. Engineering Failure Analysis, 35, pp. 262–271, 2013. [Crossref]
[15] Meenakshi Devi, M., Geethanjali, M. & Rama Devi, A., Fault localization for transmis- sion lines with optimal Phasor Measurement Units. Computers & Electrical Engineering, 70, pp. 163–178, 2018. [Crossref]
[16] Menéndez, O., Pérez, M. & Cheein, F.A., Visual-Based Positioning of Aerial Mainte- nance Platforms on Overhead Transmission Lines, Applied Sciences, 9(1), p. 165, 2019. [Crossref]
[17] da Silva, P.R.N., Negrão, M.M.L.C., Vieira Junior, P. & Sanz-Bobi, M.A., A new meth- odology of fault location for predictive maintenance of transmission lines. International Journal of Electrical Power & Energy Systems, 42(1), pp. 568–574, 2012. https://doi. org/10.1016/j.ijepes.2012.04.057
[18] Ferreira, E.F. & Barros, J.D., Faults monitoring system in the electric power grid of medium voltage. Procedia Computer Science, 130, pp. 696–703, 2018. https://doi. org/10.1016/j.procs.2018.04.123
[19] Samadi, M., Seifi, H. & Haghifam, M.R., Midterm system level maintenance schedul- ing of transmission equipment using inspection based model. International Journal of Electrical Power & Energy Systems, 110, pp. 467–476, 2019. ijepes.2019.03.050 [Crossref]
[20] Nagarajan, B., Li, Y., Sun, Z. & Qin, R., A routing algorithm for inspecting grid trans- mission system using suspended robot: Enhancing cost-effective and energy efficient infrastructure maintenance. Journal of Cleaner Production, 219, pp. 622–638, 2019. [Crossref]
[21] Daim, T.U., Yoon, B.S., Lindenberg, J., Grizzi, R., Estep, J. & Oliver, T., Strategic roadmapping of robotics technologies for the power industry: A multicriteria technol- ogy assessment. Technological Forecasting and Social Change, 131, pp. 49–66, 2018. https://doi.org/ [Crossref]
[22] Tavares, L. & Sequeira, J.S., RIOL – Robotic Inspection Over Power Lines, IFAC Proceedings Volumes, 40(15), pp. 108–113, 2007. 3-FR-2921.00021 [Crossref]
[23] Ben-Daya, M., Duffuaa, S., Raouf, A., Knezevic, J. & Ait-Kadi, I., Handbook of Main- tenance Management and Engineering, London: Springer-Verlag, p. 746, 2009.
[24] Schneider J., Gaul A., Neumann C., Hogräfer J., Wellbow W., Schwan M. & Schnettlek A., Asset management techniques. International Journal of Electrical Power & Energy Systems, 28(9), pp. 643–654, 2006. [Crossref]
[25] Hoff, G. & Kranz, H.G., Condition based maintenance of PE/XLPE-insulated medium voltage cable networks-verification of the IRC-analysis to determine the cable age. Conference Record of the 2002 IEEE International Symposium on Electrical Insula- tion, 2002. [Crossref]
[26] de la Fuente, A., González-Prida, V., Crespo, A., Gómez, J.F. & Guillén, A., Advanced Techniques for Assets Maintenance Management. IFAC-PapersOnLine, 51(11), pp. 205–210, 2018. [Crossref]
[27] Heo, J.H., Kim, M.K. & Lyu, J.K., Implementation of Reliability-Centered Mainte- nance for transmission components using Particle Swarm Optimization. International Journal of Electrical Power & Energy Systems, 55, pp. 238–245, 2014. https://doi. org/10.1016/j.ijepes.2013.09.005
[28] Aracil, R., Pinto, E. & Ferre, M., Robots for live-power lines: maintenance and inspection tasks. IFAC Proceedings Volumes, 35(1), pp. 13–18, 2002. https://doi. org/10.3182/20020721-6-ES-1901.00814
[29] LineScout, A robot for inspecting live transmission lines, http://www.hydroquebec. com/innovation/en/pdf/2010G080-02A_LineScout.pdf (accessed 04 June 2019).
[30] EXPLINER—Inspection Robot for High-Voltage Transmission Lines, https://www. jsme.or.jp/jsme/uploads/2016/11/awardu12-4.pdf (accessed 04 June 2019).
[31] Project T&D 2014. Transmission and distribution of the electric energy, http://www2. rse-web.it/progetti/progetto/649 (accessed 04 June 2019).
[32] CABLEWALKER official website, http://cablewalker.com/en/ (accessed 04 June 2019).
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Acadlore takes over the publication of IJEPM from 2025 Vol. 10, No. 3. The preceding volumes were published under a CC BY 4.0 license by the previous owner, and displayed here as agreed between Acadlore and the previous owner. ✯ : This issue/volume is not published by Acadlore.

Open Access
Research article

Asset Management in Grid Companies Using Integrated Diagnostic Devices

l.d. gitelman1,
m.v. kozhevnikov1,
d.d. kaplin2
1
Department of Energy and Industrial Management Systems, Ural Federal University, Russia
2
JSC “Rosseti Ural”, Russia
International Journal of Energy Production and Management
|
Volume 4, Issue 3, 2019
|
Pages 230-243
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: N/A
View Full Article|Download PDF

Abstract:

The digitization of power grids envisages a transition to new models of fault diagnosis, repair and maintenance of electric power grid equipment. The most promising tools for implementing advanced production asset management strategies are integrated technologies that are based on robotic diagnostic platforms, various hardware–software instruments and smart data analysis systems. The article analyzes other countries’ experience of developing robotic methods of fault diagnosis and mainte- nance of overhead power transmission lines, which present a major challenge in terms of monitoring, failure prediction and localized repairs. The Cablewalker robotic system was used as an example for identifying the advantages of integrated diagnostic hardware systems as opposed to traditional meth- ods of power grid equipment maintenance and overhaul. Recommendations are given for adopting the technology in grid companies. During trials of the technology on a 2.34-km section of a power transmission line 112 defects were detected versus three that were identified by means of ‘manual’ inspection. A digital twin of the transmission line was created to manage its technical condition with regard to various risks.

Keywords: Asset management, Robotic diagnosis, Grids, Overhead transmission lines, Maintenance strategies, Digital twin

1. Introduction

2. Main Approaches to Grid Asset Management

3. Experience of Creating Robotic Diagnosis Technologies for OTL

4. Testing and Experimental Results

5. Conclusion

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Acknowledgments

The work was supported by Act 211 of the Government of the Russian Federation, contract № 02.A03.21.0006.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References
[1] Digital Transformation of Industries. Electricity Industry. World Economic Forum White Paper, 2016, https://www.accenture.com/_acnmedia/Accenture/Conversion- Assets/WEF/PDF/Accenture-Electricity-Industry.pdf (accessed 04 June 2019).
[2] The Digital Energy Transformation, https://www.ge.com/content/dam/gepower-pw/ global/en_US/documents/hybrid/des/GE_Digital_Transformation.PDF (accessed 04 June 2019).
[3] Butler, C., Parkhill, K.A. & Luzecka, P., Rethinking energy demand governance: Exploring impact beyond ‘energy’ policy. Technological Energy Research & Social Science, 36, pp. 70–78, 2018. [Crossref]
[4] Chebotareva, G.S., Impact of state support mechanisms on the cost of renewable energy projects: The case of developing countries. WIT Transactions on Ecology and the Environment, 217, pp. 881–891, 2018. [Crossref]
[5] Goldbach, K., Rotaru, A.M., Reichert, S., Stiff, G. & Gölz, S., Which digital energy ser- vices improve energy efficiency? A multi-criteria investigation with European experts, Energy Policy, 115, pp. 239–248, 2018. [Crossref]
[6] Morley, J., Widdicks, K. & Hazas, M., Digitalisation, energy and data demand: The impact of Internet traffic on overall and peak electricity consumption. Energy Research & Social Science, 38, pp. 128–137, 2018. [Crossref]
[7] Baev, I., Dzyuba, A., Solovyeva, I. & Kuzmina, N., Improving the efficiency of using small-distributed generation systems through mechanisms of demand management for electricity and gas. International Journal of Energy Production and Management, 3(4), pp. 277–291, 2018. [Crossref]
[8] Wissner, M., The Smart Grid – A saucerful of secrets? Applied Energy, 88(7), pp. 2509–2518, 2011. [Crossref]
[9] Johnstone, P. & Kivimaa, P., Multiple dimensions of disruption, energy transitions and industrial policy. Energy Research & Social Science, 37, pp. 260–265, 2018. https://doi. org/10.1016/j.erss.2017.10.027
[10] Gitelman, L., Magaril, E., Kozhevnikov, M. & Rada, E.C., Rational Behavior of an Enterprise in the Energy Market in a Circular Economy, Resources, 8(2), p. 73, 2019. [Crossref]
[11] Larsen, P.H., A method to estimate the costs and benefits of undergrounding electricity transmission and distribution lines. Energy Economics, 60, pp. 47–61, 2016. https://doi. org/10.1016/j.eneco.2016.09.011
[12] Xie, Y., Li, C., Lv, Y. & Yu, C., Predicting lightning outages of transmission lines using generalized regression neural network. Applied Soft Computing, 78, pp. 438–446, 2019. [Crossref]
[13] Kishore, T.S. & Singal, S.K., Optimal economic planning of power transmission lines: A review. Renewable and Sustainable Energy Reviews, 39, pp. 949–974. https://doi. org/10.1016/j.asoc.2018.09.042
[14] Mesić, M. & Plavšić, T., The contribution of failure analyses to transmission network maintenance preferentials. Engineering Failure Analysis, 35, pp. 262–271, 2013. [Crossref]
[15] Meenakshi Devi, M., Geethanjali, M. & Rama Devi, A., Fault localization for transmis- sion lines with optimal Phasor Measurement Units. Computers & Electrical Engineering, 70, pp. 163–178, 2018. [Crossref]
[16] Menéndez, O., Pérez, M. & Cheein, F.A., Visual-Based Positioning of Aerial Mainte- nance Platforms on Overhead Transmission Lines, Applied Sciences, 9(1), p. 165, 2019. [Crossref]
[17] da Silva, P.R.N., Negrão, M.M.L.C., Vieira Junior, P. & Sanz-Bobi, M.A., A new meth- odology of fault location for predictive maintenance of transmission lines. International Journal of Electrical Power & Energy Systems, 42(1), pp. 568–574, 2012. https://doi. org/10.1016/j.ijepes.2012.04.057
[18] Ferreira, E.F. & Barros, J.D., Faults monitoring system in the electric power grid of medium voltage. Procedia Computer Science, 130, pp. 696–703, 2018. https://doi. org/10.1016/j.procs.2018.04.123
[19] Samadi, M., Seifi, H. & Haghifam, M.R., Midterm system level maintenance schedul- ing of transmission equipment using inspection based model. International Journal of Electrical Power & Energy Systems, 110, pp. 467–476, 2019. ijepes.2019.03.050 [Crossref]
[20] Nagarajan, B., Li, Y., Sun, Z. & Qin, R., A routing algorithm for inspecting grid trans- mission system using suspended robot: Enhancing cost-effective and energy efficient infrastructure maintenance. Journal of Cleaner Production, 219, pp. 622–638, 2019. [Crossref]
[21] Daim, T.U., Yoon, B.S., Lindenberg, J., Grizzi, R., Estep, J. & Oliver, T., Strategic roadmapping of robotics technologies for the power industry: A multicriteria technol- ogy assessment. Technological Forecasting and Social Change, 131, pp. 49–66, 2018. https://doi.org/ [Crossref]
[22] Tavares, L. & Sequeira, J.S., RIOL – Robotic Inspection Over Power Lines, IFAC Proceedings Volumes, 40(15), pp. 108–113, 2007. 3-FR-2921.00021 [Crossref]
[23] Ben-Daya, M., Duffuaa, S., Raouf, A., Knezevic, J. & Ait-Kadi, I., Handbook of Main- tenance Management and Engineering, London: Springer-Verlag, p. 746, 2009.
[24] Schneider J., Gaul A., Neumann C., Hogräfer J., Wellbow W., Schwan M. & Schnettlek A., Asset management techniques. International Journal of Electrical Power & Energy Systems, 28(9), pp. 643–654, 2006. [Crossref]
[25] Hoff, G. & Kranz, H.G., Condition based maintenance of PE/XLPE-insulated medium voltage cable networks-verification of the IRC-analysis to determine the cable age. Conference Record of the 2002 IEEE International Symposium on Electrical Insula- tion, 2002. [Crossref]
[26] de la Fuente, A., González-Prida, V., Crespo, A., Gómez, J.F. & Guillén, A., Advanced Techniques for Assets Maintenance Management. IFAC-PapersOnLine, 51(11), pp. 205–210, 2018. [Crossref]
[27] Heo, J.H., Kim, M.K. & Lyu, J.K., Implementation of Reliability-Centered Mainte- nance for transmission components using Particle Swarm Optimization. International Journal of Electrical Power & Energy Systems, 55, pp. 238–245, 2014. https://doi. org/10.1016/j.ijepes.2013.09.005
[28] Aracil, R., Pinto, E. & Ferre, M., Robots for live-power lines: maintenance and inspection tasks. IFAC Proceedings Volumes, 35(1), pp. 13–18, 2002. https://doi. org/10.3182/20020721-6-ES-1901.00814
[29] LineScout, A robot for inspecting live transmission lines, http://www.hydroquebec. com/innovation/en/pdf/2010G080-02A_LineScout.pdf (accessed 04 June 2019).
[30] EXPLINER—Inspection Robot for High-Voltage Transmission Lines, https://www. jsme.or.jp/jsme/uploads/2016/11/awardu12-4.pdf (accessed 04 June 2019).
[31] Project T&D 2014. Transmission and distribution of the electric energy, http://www2. rse-web.it/progetti/progetto/649 (accessed 04 June 2019).
[32] CABLEWALKER official website, http://cablewalker.com/en/ (accessed 04 June 2019).
Cite this:
APA Style
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BibTex Style
MLA Style
Chicago Style
GB-T-7714-2015
Gitelman, L., Kozhevnikov, M., & Kaplin, D. (2019). Asset Management in Grid Companies Using Integrated Diagnostic Devices. Int. J. Energy Prod. Manag., 4(3), 230-243. https://doi.org/10.2495/EQ-V4-N3-230-243
L. Gitelman, M. Kozhevnikov, and D. Kaplin, "Asset Management in Grid Companies Using Integrated Diagnostic Devices," Int. J. Energy Prod. Manag., vol. 4, no. 3, pp. 230-243, 2019. https://doi.org/10.2495/EQ-V4-N3-230-243
@research-article{Gitelman2019AssetMI,
title={Asset Management in Grid Companies Using Integrated Diagnostic Devices},
author={L.D. Gitelman and M.V. Kozhevnikov and D.D. Kaplin},
journal={International Journal of Energy Production and Management},
year={2019},
page={230-243},
doi={https://doi.org/10.2495/EQ-V4-N3-230-243}
}
L.D. Gitelman, et al. "Asset Management in Grid Companies Using Integrated Diagnostic Devices." International Journal of Energy Production and Management, v 4, pp 230-243. doi: https://doi.org/10.2495/EQ-V4-N3-230-243
L.D. Gitelman, M.V. Kozhevnikov and D.D. Kaplin. "Asset Management in Grid Companies Using Integrated Diagnostic Devices." International Journal of Energy Production and Management, 4, (2019): 230-243. doi: https://doi.org/10.2495/EQ-V4-N3-230-243
Gitelman L.， Kozhevnikov M.， Kaplin D.. Asset Management in Grid Companies Using Integrated Diagnostic Devices[J]. International Journal of Energy Production and Management， 2019， 4(3)： 230-243. https://doi.org/10.2495/EQ-V4-N3-230-243