{"title":"The Techniques for Achieving High Power Equipment Reliability with Distributed Informational System","authors":"Arakelian Edik Koyrunovich, Sultanov Makhsud Mansurovich, Evseev Kirill Viktorovich","doi":"10.11648/J.EPES.20190806.13","DOIUrl":null,"url":null,"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.","PeriodicalId":125088,"journal":{"name":"American Journal of Electrical Power and Energy Systems","volume":"62 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Electrical Power and Energy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11648/J.EPES.20190806.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.