Advertisement

Analysis of Selected Structures for Model-Based Measuring Methods Using Fuzzy Logic

  • R. Hampel
  • W. Kästner
  • A. Fenske
  • B. Vandreier
  • S. Schefter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 38)

Abstract

Monitoring and diagnosis of safety-related technical processes in nuclear engineering can be improved with the help of intelligent methods of signal processing such as analytical redundancies. This chapter gives an overview about combined methods in form of hybrid models using model based measuring methods (observer) and knowledge-based methods (fuzzy logic). Three variants of hybrid observers (fuzzy-supported observer, hybrid observer with variable gain and hybrid non-linear operating point observer) are explained. As a result of the combination of analytical and fuzzy-based algorithms a new quality of monitoring and diagnosis is achieved. The results will be demonstrated in summery for the example water level estimation within pressure vessels (pressurizer, steam generator, and Boiling Water Reactor) with water-steam mixture during the accidental depressurization.

Keywords

Heat Flux Pressure Vessel Fuzzy Controller State Space Model Gain Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adjallah, K., Non-linear Observers using Fuzzy Gain Adaptation, International Workshop on Fuzzy Technologies in Automation and Control, Duisburg 1994, 73–85Google Scholar
  2. 2.
    Akin, H.L.; Altin, V., Rule-based Fuzzy Logic Controller for a PWR-type nuclear power plant, IEEE Transactions on Nuclear Science, Vol. 38, No. 2, April 1991Google Scholar
  3. 3.
    Berger, M.; Jelali, M., Robust Model-Based Fuzzy Observer for an Inverted Pendulum, IEEE Transactions 1996, 118–122Google Scholar
  4. 4.
    Chaker, N.; Wagenknecht, M.; Fenske, A.; Hampel, R., Fuzzy Controller Structure Transformation, Proceedings of 3rd International FUNS Workshop, Antwerp, Belgium, September, 1998, 99–110Google Scholar
  5. 5.
    DynStar- Ein Simulationsprogramm für Automatisierungstechniker, Programmbeschreibung 12/97, HTWS Zittau/Görlitz (FH), 1997, Regelungstechnik 9, 10, 11/1984Google Scholar
  6. 6.
    Frank, P. M.; Kiupel, H.; Bux, O, Fuzzy Control of Steam Turbines, Fuzzy Sets and Systems 1994 (63), 319Google Scholar
  7. 7.
    Frank, P.M., Fuzzy Supervision - Einsatz der Fuzzy Logic in der Prozeßüberwachung, VDI-Bericht 1113, 181, VDI-Verlag, 1994Google Scholar
  8. 8.
    Hampel, R., Investigation in Utilization of Fuzzy Logic in NPP, ICM on Advanced Control and Instrumentation Systems in NPP, Espoo Helsinki, June 1994Google Scholar
  9. 9.
    Hampel, R., Meß-und Automatisierungstechnik zur Störfall-beherrschung - Methoden der Signalverarbeitung, Simulation und Verifikation, Abschlußbericht zum BMBFProjekt 150 10 15, HTWS Zittau/Görlitz (FH), Januar 1999Google Scholar
  10. 10.
    Handschin, E.; u. a., Einsatz von Fuzzy-Reglern in der Kraftwerks-technik, VDI-Berichte, 151, 1113 VDI-Verlag 1994Google Scholar
  11. 11.
    Holbert, K.E.; Sharif Heger; A.; Nahrul K. Aang-Rashid, Redundant Sensor Validation by Using Fuzzy Logic, Nuclear Science and Engineering 118, 54–64 (1994)Google Scholar
  12. 12.
    Iijima, T.; Nakajima, Y.; Sakurai, N., Fuzzy Logic Control System for Reactor Feedwater Control of The Fugen Nuclear Power Station, International Symposium on Instrumentation and Control, Tokyo, May 1992Google Scholar
  13. 13.
    Iijima, T.; Nakajima, Y.; Nishiwaki, Y., Application of Fuzzy Logic, Control System for Reactor Feed-Water Control, Proc. of 1st International FLINS Workshop, Mol, Belgium, 1994Google Scholar
  14. 14.
    Jung, C.H.; Ham, C.S.; Lee, K.L., A real time self tuning Fuzzy Controller for the steam generator through Scaling Factor Adjustment, Proc. of 1st International FLINS Workshop, Mol, Belgium, 1994Google Scholar
  15. 15.
    Kim, Byung-Kook; et. al., Fuzzy Logic utilization for the diagnosis of metallic loose part impact in nuclear power plant, Proc. of 2nd International FLINS Workshop, Mol, Belgium, 1996Google Scholar
  16. 16.
    King, P.J.; Burnham, K.J.; James, D.J.G., A combined rule-based and model-based adaptive control scheme, Proc. IEE International Conference CONTROL’94, Warwick, 1994Google Scholar
  17. 17.
    Liu, Z.; Ruan, D., Experiments of Fuzzy Logic Control on a Nuclear Research Reactor, Proc. of 2nd International FLINS Workshop, Mol, Belgium, 1996Google Scholar
  18. 18.
    Na, N.; Kwon, K.; Ham, C.; Bien, Z., A study on water level control of PWR steam generator at low power and the self-tuning of ist Fuzzy Controller, Proc. of 1st International FLINS Workshop, Mol, Belgium, 1994Google Scholar
  19. 19.
    Simutis,R.; Havlik,I.; Lübbert, A., A fuzzy-supported Extended Kalman Filter: a new approach to state estimation and prediction exemplified by alcohol formation in beer brewing, Journal of Biotechnology, 24 (1992), 211–234Google Scholar
  20. 20.
    Worlitz, F., Anwendung klassischer Verfahren und Fuzzy-Logik zur Verbesserung der hydrostatischen Höhenstandsmessung, Dissertation, Technische Hochschule Zittau, 1992Google Scholar
  21. 21.
    Yung Joon Hah, Byong-Whi Lee, Fuzzy Power Control Algorithm for a Pressurized Water Reactor, Nuclear Technology, Vol. 106, May 1994Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • R. Hampel
    • 1
  • W. Kästner
    • 1
  • A. Fenske
    • 1
  • B. Vandreier
    • 1
  • S. Schefter
    • 1
  1. 1.Institute for Process Technique, Process Automation and Measuring Technique(IPM) at the University of Applied Sciences Zittau/GörlitzZittauGermany

Personalised recommendations