Nuclear Engineering and Technology

  • 제55권5호
  • /
  • Pages.1630-1643
  • /
  • 2023
  • /
  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
권호 표지
DOI QR코드

DOI QR Code

Long-term prediction of safety parameters with uncertainty estimation in emergency situations at nuclear power plants

  • Hyojin Kim (Department of Nuclear Engineering, Chosun University) ;
  • Jonghyun Kim (Department of Nuclear Engineering, Chosun University)
  • 투고 : 2022.08.14
  • 심사 : 2023.01.28
  • 발행 : 2023.05.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science ICT) (No. RS-2022-00144042 and No. 2018M2B2B1065651).

참고문헌

  1. IAEA, Advanced Surveillance, Diagnostic and Prognostic Techniques in Monitoring Structures , Systems and Components in Nuclear Power Plants, IAEA Nucl. Energy Ser., 2013. 
  2. S.G. Kim, Y.H. Chae, P.H. Seong, Development of a generative-adversarial-network-based signal reconstruction method for nuclear power plants, Ann. Nucl. Energy 142 (2020), 107410, https://doi.org/10.1016/j.anucene.2020.107410. 
  3. H. Wang, M.J. Peng, P. Wu, S.Y. Cheng, Improved methods of online monitoring and prediction in condensate and feed water system of nuclear power plant, Ann. Nucl. Energy 90 (2016) 44-53, https://doi.org/10.1016/j.anucene.2015.11.037. 
  4. M.R. Endsley, Toward a theory of situation awareness in dynamic systems, Hum. Factors 37 (1995) 32-64, https://doi.org/10.1518/001872095779049543. 
  5. J. She, T. Shi, S. Xue, Y. Zhu, S. Lu, P. Sun, H. Cao, Diagnosis and prediction for loss of coolant accidents in nuclear power plants using deep learning methods, Front. Energy Res. 9 (2021), https://doi.org/10.3389/fenrg.2021.665262. 
  6. S.W. Cheon, Application of neural networks to nuclear power plants in Korea, Inform 20 (1996) 469-474. 
  7. K. Moshkbar-Bakhshayesh, Comparative study of application of different supervised learning methods in forecasting future states of NPPs operating parameters, Ann. Nucl. Energy 132 (2019) 87-99, https://doi.org/10.1016/j.anucene.2019.04.031. 
  8. M. El-Sefy, A. Yosri, W. El-Dakhakhni, S. Nagasaki, L. Wiebe, Artificial neural network for predicting nuclear power plant dynamic behaviors, Nucl. Eng. Technol. 53 (2021) 3275-3285, https://doi.org/10.1016/j.net.2021.05.003. 
  9. J. Qiu, B. Wang, C. Zhou, Forecasting stock prices with long-short term memory neural network based on attention mechanism, PLoS One 15 (2020) 1-15, https://doi.org/10.1371/journal.pone.0227222. 
  10. H.P. Nguyen, J. Liu, E. Zio, A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators, Appl. Soft Comput. J. 89 (2020), https://doi.org/10.1016/j.asoc.2020.106116. 
  11. J. Bae, G. Kim, S.J. Lee, Real-time prediction of nuclear power plant parameter trends following operator actions, Expert Syst. Appl. 186 (2021), 115848, https://doi.org/10.1016/j.eswa.2021.115848. 
  12. H.P. Nguyen, P. Baraldi, E. Zio, Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants, Appl. Energy 283 (2021), https://doi.org/10.1016/j.apenergy.2020.116346. 
  13. Z. Cui, R. Ke, Z. Pu, Y. Wang, Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction, vols. 1-11, 2018. http://arxiv.org/abs/1801.02143. 
  14. Q. Chen, W. Zhang, Y. Lou, Forecasting stock prices using a hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network, IEEE Access 8 (2020) 117365-117376, https://doi.org/10.1109/ACCESS.2020.3004284. 
  15. M. Schuster, K.K. Paliwal, Bidirectional recurrent neural networks, IEEE Trans. Signal Process. 45 (1997) 2673-2681, https://doi.org/10.1109/78.650093.093. 
  16. I. Sutskever, O. Vinyals, Q.V. Le, Sequence to sequence learning with neural networks, Adv. Neural Inf. Process. Syst. 4 (2014) 3104-3112. 
  17. D. Bahdanau, K.H. Cho, Y. Bengio, Neural machine translation by jointly learning to align and translate, 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc. (2015) 1-15. 
  18. S. Hao, D.H. Lee, D. Zhao, Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system, Transport. Res. C Emerg. Technol. 107 (2019) 287-300, https://doi.org/10.1016/j.trc.2019.08.005. 
  19. Y. Lu, Z. Tian, R. Zhou, W. Liu, Multi-step-ahead prediction of thermal load in regional energy system using deep learning method, Energy Build. 233 (2021), 110658, https://doi.org/10.1016/j.enbuild.2020.110658. 
  20. D.P. Kingma, D.J. Rezende, S. Mohamed, M. Welling, Semi-supervised learning with deep generative models, Adv. Neural Inf. Process. Syst. 4 (2014) 3581-3589. 
  21. D.P. Kingma, M. Welling, Auto-encoding variational bayes, 2nd Int. Conf. Learn. Represent. ICLR 2014 - Conf. Track Proc. (2014) 1-14. 
  22. T. Tieleman, G. Hinton, Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude, COURSERA: Neural Networks for Machine Learning 4 (2012) 26-31. 
  23. D.P. Kingma, D.J. Rezende, S. Mohamed, M. Welling, Semi-supervised learning with deep generative models, Adv. Neural Inf. Process. Syst. 4 (2014) 3581-3589. 
  24. H. Xu, W. Chen, N. Zhao, Z. Li, J. Bu, Z. Li, Y. Liu, Y. Zhao, D. Pei, Y. Feng, J. Chen, Z. Wang, H. Qiao, Unsupervised anomaly detection via variational autoencoder for seasonal KPIs in web applications, web conf, WWW 2018, in: 2018 - Proc. World Wide Web Conf, vol. 2, 2018, pp. 187-196, https://doi.org/10.1145/3178876.3185996. 
  25. H. Kim, A.M. Arigi, J. Kim, Development of a diagnostic algorithm for abnormal situations using long short-term memory and variational autoencoder, Ann. Nucl. Energy 153 (2021), 108077, https://doi.org/10.1016/j.anucene.2020.108077. 
  26. J. Gordon, J.M. Hern andez-Lobato, Combining deep generative and discriminative models for Bayesian semi-supervised learning, Pattern Recogn. 100 (2020), 107156, https://doi.org/10.1016/j.patcog.2019.107156. 
  27. H. Le, T. Tran, T. Nguyen, S. Venkatesh, Variational memory encoder-decoder, Adv. Neural Inf. Process. Syst. 2018-Decem (2018) 1508-1518. 
  28. KAERI, Advanced Compact Nuclear Simulator Textbook, Nuclear Training Center in Korea Atomic Energy Research Institute, 1990. 
  29. D. Lee, P.H. Seong, J. Kim, Autonomous operation algorithm for safety systems of nuclear power plants by using long-short term memory and function-based hierarchical framework, Ann. Nucl. Energy 119 (2018) 287-299, https://doi.org/10.1016/j.anucene.2018.05.020. 
  30. KHNP APR1400 Design Description, Korea Hydro & Nuclear Power Co., Ltd, 2014. 
  31. I. Jebli, F.Z. Belouadha, M.I. Kabbaj, A. Tilioua, Prediction of solar energy guided by pearson correlation using machine learning, Energy 224 (2021), 120109, https://doi.org/10.1016/j.energy.2021.120109. 
  32. M.R. Donihue, Evaluating the role judgment plays in forecast accuracy, J. Forecast. 12 (1993) 81-92, https://doi.org/10.1002/for.3980120203. 
  33. J.S. Armstrong, Evaluating Forecasting Methods, 2001, pp. 443-472, https://doi.org/10.1007/978-0-306-47630-3_20. 
  34. J.J. Montano Moreno, A. Palmer Pol, A. Sese Abad, B. Cajal Blasco, Using the R-MAPE index as a resistant measure of forecast accuracy, Psicothema 25 (2013) 500-506, https://doi.org/10.7334/psicothema2013.23. 
  35. X.B. Jin, W.Z. Zheng, J.L. Kong, X.Y. Wang, Y.T. Bai, T.L. Su, S. Lin, Deep-learning forecasting method for electric power load via attention-based encoder-decoder with bayesian optimization, Energies 14 (2021), https://doi.org/10.3390/en14061596. 
  36. B. Shahriari, K. Swersky, Z. Wang, R.P. Adams, N. De Freitas, Taking the human out of the loop: a review of Bayesian optimization, Proc. IEEE 104 (2016) 148-175, https://doi.org/10.1109/JPROC.2015.2494218. 
  37. H. Abbasimehr, R. Paki, Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization, Chaos, Solit. Fractals 142 (2021), 110511, https://doi.org/10.1016/j.chaos.2020.110511.