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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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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