Abstract
This study aims to develop an autonomous algorithm to control the safety systems of nuclear power plant (NPP) by using the deep learning that is one of machine learning methods. The autonomous algorithm has two main goals. First, it achieves a high level of automation for nine safety functions of NPP. Second, the algorithm controls the nine safety functions in an integrated way. The function-based hierarchical framework is suggested to represent the multi-level structure that models NPP safety systems with the levels of goal, function and system. The function-based hierarchical framework is used to model the NPP for the application of the multi-system deep learning network. Multi-system deep learning network is applied to develop the algorithm for autonomous control. This approach enables the systematic analysis of power plant system and development of the database for the deep learning network.
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Nasri, S., et al.: Autonomous hybrid system and coordinated intelligent management approach in power system operation and control using hydrogen storage. Int. J. Hydrogen Energy 42(15), 9511–9523 (2017)
Li, X., et al.: Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles. Mech. Syst. Sig. Process. 87, 118–137 (2017)
Guo, H., et al.: Regional path moving horizon tracking controller design for autonomous ground vehicles. Sci. China Inf. Sci. 60(1), 013201 (2017)
Petres, C., et al.: Path planning for autonomous underwater vehicles. IEEE Trans. Robot. 23(2), 331–341 (2007)
Arora, A., Robert, F., Salah S.: An approach to autonomous science by modeling geological knowledge in a Bayesian framework. arXiv preprint arXiv:1703.03146 (2017)
Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)
Antsaklis, P.J., Passino, K.M.: Introduction to Intelligent Control Systems with High Degrees of Autonomy. Kluwer Academic Publishers, Berlin (1993)
Liu, Z., Fan, J.: Technology readiness assessment of small modular reactor (SMR) designs. Progress Nuclear Energy 70, 20–28 (2014)
Ruan, D.: Intelligent systems in nuclear applications. Int. J. Intell. Syst. 13(2–3), 115–125 (1998)
Sadighi, M., Setayeshi, S., Salehi, A.A.: PWR fuel management optimization using neural networks. Ann. Nuclear Energy 29(1), 41–51 (2002)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Ngiam, J., et al.: Multimodal deep learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011) (2011)
Corcoran, W.R., et al.: The critical safety functions and plant operation. Nuclear Technol. 55(3), 690–712 (1981)
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Lee, D., Kim, J. (2018). Autonomous Algorithm for Safety Systems of the Nuclear Power Plant by Using the Deep Learning. In: Fechtelkotter, P., Legatt, M. (eds) Advances in Human Factors in Energy: Oil, Gas, Nuclear and Electric Power Industries. AHFE 2017. Advances in Intelligent Systems and Computing, vol 599. Springer, Cham. https://doi.org/10.1007/978-3-319-60204-2_8
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DOI: https://doi.org/10.1007/978-3-319-60204-2_8
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