Home Technology Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant
Article
Licensed
Unlicensed Requires Authentication

Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant

  • Yea-Kuang Chan and Yu-Ching Tsai
Published/Copyright: April 18, 2017
Become an author with De Gruyter Brill

Abstract

The objective of this study is to develop a turbine cycle model using the multiple regression approach to estimate the turbine-generator output for the Chinshan Nuclear Power Plant (NPP). The plant operating data was verified using a linear regression model with a corresponding 95 percnt; confidence interval for the operating data. In this study, the key parameters were selected as inputs for the multiple regression based turbine cycle model. The proposed model was used to estimate the turbine-generator output. The effectiveness of the proposed turbine cycle model was demonstrated by using plant operating data obtained from the Chinshan NPP Unit 2. The results show that this multiple regression based turbine cycle model can be used to accurately estimate the turbine-generator output. In addition, this study also provides an alternative approach with simple and easy features to evaluate the thermal performance for nuclear power plants.

Kurzfassung

Ziel dieser Studie ist es, mit Hilfe der multiplen Regressionsanalyse ein Turbinenzyklusmodell zur Bestimmung der Turbinengeneratorleistung des Chinshan-Kernkraftwerks zu entwickeln. Die Betriebsdaten wurden mit Hilfe eines linearen Regressionsmodells mit 95 percnt; Konfidenzintervall verifiziert. In dieser Studie wurden die wichtigsten Parameter als Input für das Turbinenzyklusmodell ausgewählt. Das vorgestellte Modell wurde zur Bestimmung der Turbinengeneratorleistung verwendet. Die Leistungsfähigkeit des vorgeschlagenen Turbinenzyklusmodells wurde mit Hilfe der Betriebsdaten der Einheit 2 des Chinshan-Kernkraftwerks nachgewiesen. Die Ergebnisse zeigen, dass das Turbinenzyklusmodell zur genauen Bestimmung der Turbinengeneratorleistung verwendet werden kann. Die Studie liefert auch einen alternativen Ansatz mit einfachen Merkmalen zur Bewertung der thermischen Leistung von Kernkraftwerken.

References

1 Electric Power Research Institute (EPRI): Megawatt improvement casebook and guideline. Final report. EPRI TR-101867, Project No. 2407–05 1992Search in Google Scholar

2 Electric Power Research Institute (EPRI): Main turbine performance upgrade guideline. Final report. EPRI TR-106230, Project No. 3186-461997Search in Google Scholar

3 Minner, G. L., et al. 2001. PEPSE user input description. Idaho Falls, Idaho: Scientech Inc.Search in Google Scholar

4 Chang, C. J., et al.: Development of a thermal efficiency monitoring system for Kuosheng nuclear power plant. Proc. 6th Int. Conf. on nuclear thermal-hydraulics, operations and safety, Nara, Japan, October 4–8, 2004Search in Google Scholar

5 Heo, G.; Chang, S. H.; Choi, S. S.: Development of a need-oriented steam turbine cycle simulation toolbox. IEEE transactions on energy conversion20 (2005) 85986910.1109/TEC.2005.845528Search in Google Scholar

6 Kim, S. and Choi, K.: PERUPS (PERformance UPgrade System) for on-line performance analysis of a nuclear power plant turbine cycle. Nuclear engineering and technology37 (2005) 167–176Search in Google Scholar

7 Nakao, Y.; Koda, E.; Takahashi, T.: Development of general-purpose software to analyze the statistic thermal characteristic of nuclear power plant. Journal of power and energy systems3 (2009) 2–11Search in Google Scholar

8 Ubeyli, E. D.: Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents. Expert systems with applications36 (2009) 9031–9038 10.1016/j.eswa.2008.12.019Search in Google Scholar

9 Jang, J. S. R.: ANFIS: adaptive network-based fuzzy inference system. IEEE transactions on systems, man and cybernetics23 (1993) 66568510.1109/21.256541Search in Google Scholar

10 Jang, J. S. R.; Sun, C. T.; Mizutani, E.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Upper Saddle River, New Jersey: Prentice-Hall, 1997Search in Google Scholar

11 Chen, C. S.; Lai, Y. H.: Rotor fault diagnosis system based on individual neural networks and fuzzy synthesized engine. Journal of the Chinese institute of engineers33 (2010) 975–986 10.1080/02533839.2010.9671686Search in Google Scholar

12 Hasiloglu, A., et al.: Adaptive neuro-fuzzy modeling of transient heat transfer in circular duct air flow. International journal of thermal sciences43 (2004) 1075–1090 10.1016/j.ijthermalsci.2004.01.010Search in Google Scholar

13 Awadallah, M. A., et al.: A neuro-fuzzy approach to automatic diagnosis and location of stator inter-turn faults in CSI-fed PM brushless DC motors. IEEE transactions on energy conversion20 (2005) 25325910.1109/TEC.2005.847976Search in Google Scholar

14 Guimaraes, A. C. F.; Lapa, C. M. F.: Adaptive fuzzy system for fuel rod cladding failure in nuclear power plant. Annals of nuclear energy34 (2007) 233–240 10.1016/j.anucene.2006.11.012Search in Google Scholar

15 Mellit, A.; Kalogirou, S. A.: ANFIS-based modelling for photovoltaic power supply system: a case study. Renewable energy36 (2011) 250–258 10.1016/j.renene.2010.06.028Search in Google Scholar

16 Guo, Z.; Uhrig,R. E.: Nuclear power plant performance study by using neural networks. IEEE transactions on nuclear science39 (1992) 91591810.1109/23.159732Search in Google Scholar

17 Chang, C. J., et al.: Measurement uncertainty recapture power uprates in Taiwan. Proc. 8th Int. Conf. on nuclear thermal-hydraulics, operations and safety, Shanghai, China, October 10–14, 2010Search in Google Scholar

18 Taiwan power company: Final safety analysis report, Chinshan Nuclear Power Station Units 1 & 2, Amendment No. 19, 2013Search in Google Scholar

19 Bartolini, C. M., et al.: Application of artificial neural networks to micro gas turbines. Energy conversion and management52 (2011) 781–788 10.1016/j.enconman.2010.08.003Search in Google Scholar

Received: 2016-11-08
Published Online: 2017-04-18
Published in Print: 2017-03-16

© 2017, Carl Hanser Verlag, München

Articles in the same Issue

  1. Contents/Inhalt
  2. Contents
  3. Summaries/Kurzfassungen
  4. Summaries
  5. Technical Contributions/Fachbeiträge
  6. CANDU pressure tube leak detection by annulus gas dew point measurement: a critical review
  7. Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant
  8. 10.3139/124.110675
  9. Development of a parallel processing couple for calculations of control rod worth in terms of burn-up in a WWER-1000 reactor
  10. Simulation of protected and unprotected loss of flow transients in a WWER-1000 reactor based on the Drift-Flux Model
  11. Sensitivity analysis for CORSOR models simulating fission product release in LOFT-LP-FP-2 severe accident experiment
  12. Analysis of the optimal fuel composition for the Indonesian experimental power reactor
  13. Radiogenic lead from poly-metallic thorium ores as a valuable material for advanced nuclear facilities
  14. The effects of applying silicon carbide coating on core reactivity of pebble-bed HTR in water ingress accident
  15. Font Attributes based Text Steganographic algorithm (FATS) for communicating images: A nuclear power plant perspective
  16. Size control synthesis and characterization of ZnO nanoparticles and its application as ZnO-water based nanofluid in heat transfer enhancement in light water nuclear reactor
  17. Nuclear characteristics of epoxy resin as a space environment neutron shielding
  18. Exact solution of the neutron transport equation in spherical geometry
  19. Technical Notes/Technische Mitteilungen
  20. Determination of self-attenuation correction factor for lichen samples by using gamma-ray spectrometry
Downloaded on 31.12.2025 from https://www.degruyterbrill.com/document/doi/10.3139/124.110647/html
Scroll to top button