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Simulation of Power Assets Management Process

  • Oleg Protalinsky
  • Anna Khanova
  • Ivan ShcherbatovEmail author
Conference paper
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 199)

Abstract

Implementation of the Industry 4.0 concept leads to in-depth end-to-end automation of all activities of an integrated power grid and requires fundamentally new technologies that change conventional business models. Operational assets of power grid companies are characterized by semantic, syntactical, structural and systematic heterogeneity, which hinders the interaction at all management levels aimed at accident prevention and performance improvement. A cognitive double-level ontological model was developed as an aggregate of the conceptual confinement model and a set of hierarchic confinement models of processes of technical condition diagnostics, repair program development and optimization, as well as optimization of logistic processes as the repair program is being implemented in a power grid company. A structural process scheme of distribution zone repair management in Q-scheme symbolism, generalized and detailed modeling algorithm schemes were developed. An interaction graph for components of the power grid repair management process was developed. Elementary components of the stochastic process of interaction of the repair management process elements were detailed by their sets of states. The sub-model “Consumption of consumable materials and resources”, realized in the Arena simulation package, was detailed. Examples were provided regarding the application of intelligent techniques at the strategical and operational levels of power grid management for structural synthesis of a balance scorecard system and identification of apparent defects of process equipment.

Keywords

Power grid companies Industry 4.0 Ontology Confinement model Management Simulation model Artificial neural network Internet of things 

References

  1. 1.
    Program: Digital economics of the Russian Federation. Approved by Decree of the RF government No. 1632-p, 28 July 2017. http://government.ru/docs/28653. Accessed 18 Oct 2018
  2. 2.
    Hermann, M., Pentek, T., Otto, B.: Design principles for Industrie 4.0 scenarios. In: 49th Hawaii International Conference on System Sciences, pp. 3928–3937. IEEE, Koloa (2016).  https://doi.org/10.1109/hicss.2016.488
  3. 3.
    Energy strategy of Russia till 2030. Institute of energy strategy, Moscow (2010)Google Scholar
  4. 4.
    Lu, Y.: Industry 4.0: a survey on technologies, applications and open research issues. J. Ind. Inf. Integr. 6, 1–10 (2017).  https://doi.org/10.1016/j.jii.2017.04.005CrossRefGoogle Scholar
  5. 5.
    Khanova, A., Protalinskiy, O., Averianova, K.: The elaboration of strategic decisions in the socio-economic systems. J. Inf. Organ. Sci. 41(1), 57–67 (2017)Google Scholar
  6. 6.
    Zhou, K., Liu, T., Zhou, L.: Industry 4.0: towards future industrial opportunities and challenges. In: Conference on Fuzzy Systems and Knowledge Discovery, pp. 2147–2152. IEEE, Zhangjiajie (2015).  https://doi.org/10.1109/fskd.2015.7382284
  7. 7.
    Massel, L.: Problems of transition to intelligent and digital power engineering from point of information technologies. In: Critical Infrastructures: Contingency Management, Intelligent, Agent-Based, Cloud Computing and Cyber Security, pp. 13–14. Atlantis Press, Irkutk (2018)Google Scholar
  8. 8.
    Sitthithanasakul, S., Choosri, N.: Using ontology to enhance requirement engineering in agile software process. In: 10th International Conference on Software, Knowledge, Information Management & Applications, pp. 181–186. IEEE, Chengdu (2016).  https://doi.org/10.1109/skima.2016.7916218
  9. 9.
    Suarez-Figueroa, M.C., Gomez-Perez, A., Motta, E., Gangemi, A. (eds.): Ontology Engineering in a Networked World. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-24794-1Google Scholar
  10. 10.
    Khalyasmaa, A., Dmitriev, S., Kokin, S., Eroshenko, S.: Fuzzy neural networks’ application for substation integral state assessment. WIT Trans. Ecol. Environ. 190, 599–605 (2014).  https://doi.org/10.2495/EQ140581CrossRefGoogle Scholar
  11. 11.
    Protalinsky, O., Shcherbatov, I., Stepanov, P.: Identification of the actual state and entity availability forecasting in power engineering using neural-network technologies. J. Phys: Conf. Ser. 891(1), 1–6 (2017).  https://doi.org/10.1088/1742-6596/891/1/012289CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Moscow Energy InstituteMoscowRussia
  2. 2.Astrakhan State Engineering InstituteAstrakhanRussia

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