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Nabeshima, Kunihiko; Subekti, M.*; Matsuishi, Tomomi*; Ono, Tomio*; Kudo, Kazuhiko*; Nakagawa, Shigeaki
Journal of Power and Energy Systems (Internet), 2(1), p.92 - 103, 2008/00
The neural networks have been utilized in on-line monitoring-system of High Temperature Engineering Tested Reactor (HTTR) with thermal power of 30 MW. In this system, several neural networks can independently model the plant dynamics with different architecture, input and output signals and learning algorithm. One of main task is real-time plant monitoring by Multi-Layer Perceptron (MLP) in auto-associative mode, which can model and estimate the whole plant dynamics by training normal operational data only. Other tasks are on-line reactivity prediction, reactivity and helium leak monitoring, respectively. From the on-line monitoring results at the safety demonstration tests, each neural network shows good prediction and reliable detection performances.
Nabeshima, Kunihiko; Matsuishi, Tomomi*; Makino, Jun*; Subekti, M.*; Ono, Tomio*; Kudo, Kazuhiko*; Nakagawa, Shigeaki
Proceedings of 15th International Conference on Nuclear Engineering (ICONE-15) (CD-ROM), 6 Pages, 2007/04
The neural networks have been utilized in on-line monitoring system of High Temperature Engineering Tested Reactor (HTTR) with thermal power of 30MW. In this system, several neural networks can independently model the plant dynamics with different architecture, input and output signals and learning algorithm. One of main task is real-time plant monitoring by Multi-Layer Perceptron (MLP) in auto-associative mode, which can model and estimate the whole plant dynamics by training normal operational data only. Other tasks are on-line reactivity prediction, reactivity and helium leak monitoring, respectively. From the on-line test results, each neural network shows good prediction and reliable detection performances.
Nabeshima, Kunihiko; Nakagawa, Shigeaki; Makino, Jun*; Matsuishi, Tomomi*; Subekti, M.*; Ono, Tomio*; Kudo, Kazuhiko*
no journal, ,
The neural networks have been utilized in on-line monitoring-system of High Temperature Engineering Tested Reactor (HTTR) with thermal power of 30MW. From the real-time test results during "reactivity insertion test; control rod withdrawal test" and "coolant flow reduction test", the monitoring system with neural networks showed good prediction and reliable detection performances.