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Gross, K.C.; Singer, R.M.; Wegerich, S.W.; Herzog, J.P.; VanAlstine, R.; Bockhorst, F.
Argonne National Lab., IL (United States). Funding organisation: USDOE Office of Energy Research, Washington, DC (United States)1997
Argonne National Lab., IL (United States). Funding organisation: USDOE Office of Energy Research, Washington, DC (United States)1997
AbstractAbstract
[en] To assure the continued safe and reliable operation of a nuclear power station, it is essential that accurate online information on the current state of the entire system be available to the operators. Such information is needed to determine the operability of safety and control systems, the condition of active components, the necessity of preventative maintenance, and the status of sensory systems. To this end, ANL has developed a new Multivariate State Estimation Technique (MSET) which utilizes advanced pattern recognition methods to enhance sensor and component operational validation for commercial nuclear reactors. Operational data from the Crystal River-3 (CR-3) nuclear power plant are used to illustrate the high sensitivity, accuracy, and the rapid response time of MSET for annunciation of a variety of signal disturbances
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1997; 6 p; International conference on intelligent systems applications to power systems; Seoul (Korea, Republic of); 6-10 Jul 1997; CONF-970765--2; CONTRACT W-31109-ENG-38; Also available from OSTI as DE97052981; NTIS; US Govt. Printing Office Dep
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