Document Type : Research Paper
Authors
Department of Energy Engineering, Sharif University of Technolog, P.O.Box: 14515-8639, Tehran - Iran
Abstract
This study uses real data of Bushehr nuclear power plant (BNPP), and by soft computing methods and without using the data of self-powered neutron detectors (SPNDs), the maximum linear heat rate of BNPP is estimated. The efficient learning algorithms of artificial neural network (ANN), including Levenberg-Marquardt (LM) and Bayesian regularization (BR) in combination with different features selection techniques including Pearson, Spearman, and Kendall’s tau, are employed to estimate the target parameter. Results show that the proposed method is appropriate for estimating the maximum linear heat rate. Given the importance of this parameter in terms of safety and the fact that its excessive increase actuates the shutdown signal of the reactor, the use of the appropriated approaches such as the present study can increase the safety of the plant and improve Defense-In-Depth (DID).
Highlights
1. Final Safety Analysis Report (FSAR) for BNPP Accident Analysis, Atomic Energy Organization of Iran (AEOI), (2007).
2. Souza, Rose Mary GP, and Joao ML Moreira, Power peak factor for protection systems–experimental data for developing a correlation, Annals of Nuclear Energy, 33.7, 609-621 (2006).
3. Kh. Moshkbar-Bakhshayesh, M. Ghanbari, M.B. Ghofrani, Development of a new features selection algorithm for estimation of NPPs operating parameters, Annals of Nuclear Energy, 146, 107667 (2020).
4. Kh. Moshkbar-Bakhshayesh, Prediction of unmeasurable parameters of NPPs using different model-free methods based on cross-correlation detection of measurable/unmeasurable parameters: A comparative study, Annals of Nuclear Energy, 139, 107232 (2020).
5. C. Lv, et al., Levenberg-Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of A Safety Critical Cyber-Physical System, IEEE Transactions on Industrial Informatics, 14(8), 3436-3446 (2018).
6. R.E. Uhrig, Potential application of neural networks to operation of nuclear power plants, Nuclear Safety, 32(1), 68-78 (1991).
7. F.S.M. Desterro, et al., Development of a Deep Rectifier Neural Network for dose prediction in nuclear emergencies with radioactive material releases, Prog. Nucl. Energy, 118, 103110 (2020).
8. Y.D. Koo, et al., Nuclear reactor vessel water level prediction during severe accidents using deep neural networks, Nucl Eng Technol, 51, 723-730 (2019).
9. M. Saghafi, M.B. Ghofrani, Real-time estimation of break sizes during LOCA in nuclear power plants using NARX neural network, Nucl Eng Technol, 51, 702-708 (2019).
10. R.A. Saleem, M.I. Radaideh, T. Kozlowski, Application of deep neural networks for high-dimensional large BWR core neutronics, Nucl Eng Technol, Article In Press (2020).
11. Bae, In Ho, et al., Calculation of the power peaking factor in a nuclear reactor using support vector regression models, Annals of Nuclear Energy, 35.12, 2200-2205 (2008).
12. A. Pirouzmand, M. Kazem Dehdashti, Estimation of relative power distribution and power peaking factor in a VVER-1000 reactor core using artificial neural networks, Progress in Nuclear Energy, 85, 17-27 (2015).
13. Lee, Wanno, et al., A study on the sensitivity of self-powered neutron detector (SPND), 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference, (1999).
14. A.Yu. Kurchenkov, Burnup of rhodium SPND in VVER-1000: Method for determination of linear energy release by SPND readings, Physics of Atomic Nuclei, 74.14, 1884-1890 (2011).
15. R.E. Uhrig, L.H. Tsoukalas, Soft computing technologies in nuclear engineering applications, Progress in Nuclear Energy, 34, 13-75 (1999).
16. Kh. Moshkbar-Bakhshayesh, M.B. Ghofrani, Transient identification in nuclear power plants: a review, Progress in Nuclear Energy, 67, 23-32 (2013).
17. Dash, Manoranjan, Huan Liu, Dimensionality reduction, Wiley Encyclopedia of Computer Science and Engineering (2007).
18. L.A.U.R.E.N.E. Fausett, V. 1994. Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Englewood Cliffs, New Jersey: Prentice Hall (1994).
19. Foresee, F. Dan, and Martin T. Hagan, Gauss-Newton approximation to Bayesian learning, Proceedings of International Conference on Neural Networks (ICNN'97). Vol. 3. IEEE, (1997).
20. Yu, Hao, and Bogdan M. Wilamowski, Levenberg-marquardt training, Industrial electronics handbook, 5.12, 1 (2011).
21. Kh. Moshkbar-Bakhshayesh, M.B. Ghofrani, Development of a Robust Identifier for NPPs Transients Combining ARIMA Model and EBP Algorithm, IEEE Transactions on Nuclear Science, 61.4, 2383-391 (2014).
22. Kh. Moshkbar-Bakhshayesh, M.B. Ghofrani, Development of an efficient identifier for nuclear power plant transients based on latest advances of error back-propagation learning algorithm, IEEE Transactions on Nuclear Science, 61, 602-610 (2014).
Keywords
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