Izvestia Vysshikh Uchebnykh Zawedeniy. Yadernaya Energetika

The peer-reviewed scientific and technology journal. ISSN: 0204-3327

Analisys of VVER-1000 main circulation pump condition under operation

12/25/2016 2016 - #04 Nuclear power plants

Leskin S.T. Slobodchuk V.I. Shelegov A.S.

DOI: https://doi.org/10.26583/npe.2016.4.02

UDC: 621.039; 62-932.2

A method and algorithms are presented for detecting the abnormal condition of the main circulation pumps based on their on-power testing results. The methodological basis of the algorithms is the presentation of the nuclear power plant equipment as a complicated system described by the N-dimensional vector in the condition space. Let us represent the condition of certain equipment by the vector X = {xi} in the N-dimensional space, where N is the number of technological parameters measured for this equipment.

A definition of an informative set of technological parameters of all N measured parameters (directly or indirectly related to the accident process) is necessary for possible interpretation of the results, for analysis of the reasons of the accident condition formation, and for the accumulation of the statistics which is necessary for improvement of the diagnostics model.

Thus, the diagnostics algorithm should include: – selection of the informative set of parameters, – at each time interval presentation of the condition of simultaneously operating equipment in a form convenient for the analysis, – identification of abnormalities.

The informative vector should be presented in a convenient form to make decisions based on the analysis of its behavior in time and also to determine whether the parameters of this vector describe an abnormal process of the system. We use the Karhunen-Loeve transform which is known as the space linear transformation (principal components method).

The MCP behavior in time is described by a projection of the informative vector on the eigenvector C1 of the correlation matrix having the maximal eigenvalue yik = (C1⋅ Xik), where the parenthesis denotes a scalar product of the vectors.

The efficiency of these algorithms has been demonstrated by their application for detecting abnormalities in the main circulation pump operation at the Novovoronezh and Kalinin nuclear power plants.


  1. Ujita Hiroshi. A probabilistic analysis method of evaluate the effect of human factors on plant safety. Nucl. Tehnol., 1986, v.76, no. 3, pp. 370-376.
  2. Fault diagnosis in dynamic systems. Theory and applications. Edited by Patton R., Frank P., Clarc R. Prentice Hall Inc., Englewood Cliffs, NY, 1989.
  3. Willsky A.S. A Survey of design methods for failure detection in dynamic systems. Automatica. 1976, v. 12, pp. 601-611.
  4. Iserman R. Process fault detection based on modeling end estimation methods – a survey. Automatica. 1984, v.20, no.4, pp. 387-404.
  5. Basseville M. Detecting changes in signal and systems – a survey. Automatica. 1988, v. 24, no. 3, pp. 309-326.
  6. Frank P.M. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy – a survey and some new results. Automatica.1990, v. 26, no. 3, pp. 459-474.
  7. Reisen C., Marshall E. Evaluating operator support system in realistic conditions at HAMMLAB. Nuclear Engineering International. 1988, v. 33, no. 402, pp. 39-41.
  8. Abagyan A.A., Dmitriev V.M, Klebanov L.A., Kroshilin A.E., Larin E.P., Morozov S.K. Monitoring and diagnostics systems for nuclear power plant operating regimes. Atomnaya energiya, 1987, v.63, pp. 311-315 (in Russian).
  9. Long A. Computerized operator decision aids. Nuclear Safety, 1984, v. 25, no. 4, pp. 512-524.
  10. Herbert M.R. A review of on-line diagnostic aids for nuclear power plant operators. Nucl. Energy. 1984, v. 23, no. 4, pp. 259-264.
  11. Pavelko V.I. A review of application of expert system methodology in nuclear power engineering. Atomnaya energiya, 1990, v.11, pp. 1-8 (in Russian).
  12. Weiss S., Reagan W., Roe J. Experience with operator aids for nuclear power plants in the USA. In: Proc. Intern. Conf. on Man-Machine Interface in Nuclear Industry. Tokyo, 15-19.02.1988, Vienna, 1988, pp. 323-329.
  13. Urig Robert E. Potential application of nuclear networks to nuclear power plants. Proc. Amer. Power Conf.. Vol. 53. Pt. 2 53rd. Ann. Meet., Chicago, III., Apr. 29-May 1. 1991, pp.946-951.
  14. Aivasyan S.A., Bukhshtaber V.M., Enyukov I.S., Meshalkin L.D. Applied Statistics: Classification and Dimensionality Reduction. Moscow. Finansy I Statistika Publ., 1989 (in Russian).
  15. Fukunaga K. Introduction to statistical pattern recognition. Academic press, New York and London, 1972.
  16. Classification and Clustering. Ed. J. van Ryzin. Moscow. Mir Publ., 1980 (in Russian).
  17. Tao Gu, Tou J.T. A new criterion for optimal classification. Pattern Recognition, 1982, v. 2, pp. 1063-1065.
  18. Leskin S. Algorithm development for abnormality detection of NPP equipment conditions based on technological testing results. Izvestiya vusov. Yadernaya Energetika, 1997, no. 4, pp. 4-12 (in Russian).
  19. Vapnik V., Chervoninkis A. Pattern Recognition Theory. Moscow. Nauka Publ., 1974 (in Russian).
  20. Tu J., Gonsales R. Pattern Recognition Principles. Moscow. Mir Publ., 1978 (in Russian).

main circulation pumps (MCP) operation and diagnostics MCP abnormal condition set of informative criteria Karhunen-Loeve transform