Thermal Engineering

, Volume 65, Issue 4, pp 189–199 | Cite as

Experience in Use of Remote Access and Predictive Analytics for Power Equipment’s Condition

  • S. A. Naumov
  • A. V. Krymskii
  • M. A. Lipatov
  • D. N. Skrabatun
Automation and Heat Control in Energy
  • 6 Downloads

Abstract

Digital technologies, software of predictive analytics, and advanced equipment will make it possible to improve economy, reliability, and safety of electricity generation. The industrial Internet begins with the introduction of systems based on mutual penetration of information technologies and automation devices of manufacturing equipment, such as the systems of remote monitoring and diagnostics. One of the inspection methods of the equipment’s condition is its continuous monitoring, which is a necessary condition for the transition to a service system on the operating condition. Using traditional modeling methods, it is possible to obtain only approximate data about the behavior of industrial systems and objects even in the cases when all factors influencing their work and operating condition are known, owing to the necessity to solve complex mathematical problems to carry out this modeling. For this reason, to monitor the operating condition of industrial systems, the statistical modeling of such systems based on empirical regulations defined by the samples of values of technological parameters recorded in the object operation period, which is considered by reference, found application in recent decades. The statistical methods of monitoring makes it possible to detect the changes in the operating condition of the system at early stages as well as to reveal the most important factors influencing them. The work presents a review of Russian systems of predictive analytics and mathematical methods on which they are based and also the PRANA system of prediction and remote monitoring that is implemented at the gas-turbine plant of V 94.2 Siemens type installed in the Perm TPP-9 (thermal power plant), the Vladimir TPP-2, the Izhevsk TPP-1, and the Kirov TPP-3, which are branches of PAO T Plyus. The efficiency of PRANA to detect the negative change of operating conditions before actual fault events was shown, which makes it possible to determine the residual life of a product and its components, schedule the optimal terms, the duration of equipment stop and preparation for its repair, and evaluate the quality of fulfilled repairs. The condition of the industrial Internet in Russian power engineering and the problems delaying its development are considered.

Keywords

digital technologies cyber-physical systems industrial Internet predictive analytics prediction residual life 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    E. V. Gromak, S. A. Naumov, and V. A. Shishov, “The remote monitoring system of JSC ‘ROTEK’ as element of power generation safety,” Nov. Ross. Elektroenerg., No. 6, 36–46 (2016).Google Scholar
  2. 2.
    N. Zavaljevski and K. C. Gross, “Sensor fault detection in nuclear power plants using multivariate state estimation technique and support vector machines,” in Proc. Third Int. Conf. of the Yugoslav Nuclear Society, Belgrade, Yugoslavia, July 17–18, 2000 (Argonne National Laboratory, 2000), pp. 1–8.Google Scholar
  3. 3.
    A. D. Trukhnii, Combined-Cycle Plants of Power Stations (Mosk. Energ. Inst., Moscow, 2015) [in Russian].Google Scholar
  4. 4.
    A. G. Kostyuk, Dynamics and Durability of Turbomachines (Mosk. Energ. Inst., Moscow, 2007) [in Russian].Google Scholar
  5. 5.
    3.0-0040. Gas Turbine GTE-160 Manual (Leningr. Met. Zavod, St. Petersburg, 2008) [in Russian].Google Scholar
  6. 6.
    RD 26.260.004-91. Procedural Guidelines. Prediction of Remaining Service Life of Equipment According to the Change in Its Operational Parameters during Operation (1992).Google Scholar
  7. 7.
    GOST R ISO 13381-1-2011. Condition Monitoring and Diagnostics of Machines. Machine Condition Prognosis. Part 1. General Guidelines (Standartinform, Moscow, 2012).Google Scholar
  8. 8.
    GOST 27.002-89. Industrial Product Dependability. General Principles. Terms and Definitions (Izd. Standartov, Moscow, 1990).Google Scholar
  9. 9.
    GOST R ISO 13374-1-2011. Condition Monitoring and Diagnostics of Machines. Data Processing, Communication and Presentation. Part 1. General Guidelines (Standartinform, Moscow, 2012).Google Scholar
  10. 10.
    GOST 27.302-86. Industrial Product Dependability. Evaluation Methods of Admissible State-Deviation and Prognosis of the Residual Machine Components (Izd. Standartov, Moscow, 1987).Google Scholar
  11. 11.
    RD 09-102-95. Procedural Guidelines for Determining Remaining Service Life for Potentially Hazardous Facilities under the Jurisdiction of Gosgorterkhnadzor of Russia (1995).Google Scholar
  12. 12.
    V. F. Veksel’berg, “Digital economy. Is it possible to survive in XXI century while staying in the ‘raw resource boat’,” Zh. Peterb. Mezhdunarodnogo Ekon. Foruma, No. 1, 61–63 (2017).Google Scholar

Copyright information

© Pleiades Publishing, Inc. 2018

Authors and Affiliations

  • S. A. Naumov
    • 1
  • A. V. Krymskii
    • 1
  • M. A. Lipatov
    • 1
  • D. N. Skrabatun
    • 1
  1. 1.AO ROTECMoscowRussia

Personalised recommendations