Volume 8, Issue 6, November 2019, Page: 158-164
The Techniques for Achieving High Power Equipment Reliability with Distributed Informational System
Arakelian Edik Koyrunovich, Department of Automation and Process Operation, National Research University “Moscow Power Engineering Institute”, Moscow, Russia
Sultanov Makhsud Mansurovich, Department of Heat Power Engineering, National Research University «Moscow Power Engineering Institute», Volzhsky, Russia
Evseev Kirill Viktorovich, Department of Heat Power Engineering, National Research University «Moscow Power Engineering Institute», Volzhsky, Russia
Received: Aug. 1, 2019;       Accepted: Nov. 26, 2019;       Published: Dec. 24, 2019
DOI: 10.11648/j.epes.20190806.13      View  48      Downloads  25
Abstract
The problem of power generating equipment reliability and safety of thermal (TPP), hydro (HPP) and nuclear (NPP) power plants at each its life cycle stage is set and the solution approach is proposed. The blockchain distributed data storage system to unite different members of the energy system is described. It is shown that the suggested technology allows decentralized data storage reliability increase due to the data exists till its last participant leaves and safety is ensured by the electric market participant consensus algorithm. The algorithm for the decentralized blockchain system is developed. The new technique for reliability design calculation based on using both constant and time-varying failure rate is introduced. It is suggested to use control action method expressed as the design, technological and operational parameters from the normative documents. The generalized model of reliability design calculation representing product of three components of failure free operation probability is developed. It is shown that developing new algorithms of statistical data obtaining and designing full repair processes allow planning equipment repair, obtaining and analyzing the corresponding reliability indeces when the equipment is in operation and then choose the most fitting repair time and amount optimization, operation mode selection and power plant long term equipment time in operation forecasting solutions. The technique for the power equipment condition forecasting by archival data stored in the distributed system, the data can be used to predict equipment failures and decide whether it should be repaired. It is shown that the desired prediction accuracy can be achived by using neural network due to its feature to reveal complex relations between input and output values.
Keywords
Power Equipment Reliability, Blockchain, Machine Learning
To cite this article
Arakelian Edik Koyrunovich, Sultanov Makhsud Mansurovich, Evseev Kirill Viktorovich, The Techniques for Achieving High Power Equipment Reliability with Distributed Informational System, American Journal of Electrical Power and Energy Systems. Vol. 8, No. 6, 2019, pp. 158-164. doi: 10.11648/j.epes.20190806.13
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
M. M. Sultanov, V. M. Truhanov, EH. K. Arakelyan, M. A. Kulikova Metody dostizheniya i obespecheniya vysokogo urovnya nadezhnosti i bezopasnosti ehnergeticheskogo oborudovaniya TEHS, GEHS, AEHS na vsekh ehtapah zhiznennogo cikla / «Novoe v Rossijskoj EHlektroehnergetike» - Ezhemesyachnyj nauchno-tekhnicheskij ehlektronnyj zhurnal; № 3, mart 2018 g; 6-15 cc.; ISSN 2312-055X
[2]
Sultanov M. M., truhanov V. M. Estimation technique of corrective effects for forecasting of reliability of the designed and operated objects of the generating systems // Applied Physics Letters. – 2017. Conf. Series 891. p 1–12.
[3]
Mamontova M. Y. Blokchejn i vozmozhnosti ego primeneniya v ehnergetike //Informacionnye tekhnologii v nauke, upravlenii, social'noj sfere i me. – 2017. – 417 p.
[4]
Melanie Swan, Blockchain: Blueprint for a New Economy – O’Reilly, 2015.– 152 p.
[5]
«Sovremennaya rynochnaya ehnergetika Rossiyskoy federacii». – M.: AHO «Uchebnyj centr NP «Sovet Rynka», - 368 p.ISBN 978-5-4253-0343-1
[6]
A. V. Mashkov Ekonomika kombinirovannogo proizvodstva teplovoy i elektricheskoy energii: uchebnoe posobie: M-vo obrazovaniya i nauki Rossiyskoy Federacii, Fil. federal'nogo gos. byudzhetnogo obrazovatel'nogo uchrezhdeniya vyssh. prof. obrazovaniya "Nac. issledovatel'skiy un-t "MEI" v g. Volzhskom, Kaf. "Teplovye elektricheskie stancii". - Volzhskiy: Fil. MEI v g. Volzhskom, 2012. – 125 s.; ISBN 978-5-94721-074-3
[7]
Vaskov A. Structure and Parameter Optimization of Renewable-Based Hybrid Power Complexes /A. Vaskov, M. Tyagunov, T. Shestopalova, G. Deryugina, I. Ishchenko //Handbook of Research on Renewable Energy and Electric Resources for Sustainable Rural Development. /Ed. V. Kharchenko (Russia) and P. Vasant (Malaysia), - Hershey, Pennsylvania, IGI Global, 2018 P. 352-382
[8]
Salomon D., Motta G. Handbook of data compression. – Springer Science & Business Media, 2010
[9]
Pravila organizacii tekhnicheskogo obsluzhivaniya i remonta oborudovaniya, zdanij i sooruzhenij ehlektrostancij i setej. SO 34.04.181-2003.-M.: OAO «CKB Energoremont»
[10]
Trukhanov V. M. Nadezhnost' v tekhnike. – 2-e izd., pererab. i dop. – M.: OOO Izdatel'skiy dom «Spektr», 2017. – 656 p.
[11]
Druzhinin G. V. Nadezhnost' sistem avtomatiki. – Izd. 2-e, pererab. i dop. – M.: Energiya, 1967. – 528 p.
[12]
Kozlov B. A., Ushakov I. A. Spravochnik po raschetu nadezhnosti apparatury radioelektroniki i avtomatiki. – M.: Sovetskoe radio, 1975. – 430 p.
[13]
Kapur K., Lamberson L. Nadezhnost' i proektirovanie sistem: Per. s angl. / Pod red. I. A. Ushakova. – M.: Mir, 1980. – 604 p.
[14]
Truhanov V. M. Novyy podhod k obespecheniyu nadezhnosti slozhnyh sistem. – M.: Izdatel'skiy dom «Spektr», 2010. – 247 p.
[15]
Truhanov V. M., Matveenko A. M. Nadezhnost' slozhnyh sistem na vsekh etapah zhiznennogo cikla.– M.: Izdatel'skij dom «Spektr», 2016. The 2nd issue.– 664 p.
[16]
Sultanov M. M. Ocenka nadezhnosti, prodlenie resursa i optimizaciya remonta oborudovaniya TES i energeticheskih sistem: ucheb. posobie. – Volzhskiy: Filial FGBOU VO «NIU «MEI» v g Volzhskom, 2016. – 100 p.
[17]
Andryushin A. V., ArakelyanE. K., Cykunova S. Yu., Chernyaev A. N. Nadezhnost' sistem upravleniya. Osnovy teorii nadezhnosti: Ucheb.posobie.- Publisher INE NNRU MePHI, 2017, 88p.
[18]
Simon Haykin. Neural Networks and Learning Machines.– Pearson, 2008, 936 p.
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