Title: Differential probabilistic space-temporal model for real-time power prognosis in failures in a nuclear reactor

Authors: Nazira Guerrero-Jezzini; Alejandro Nuñez-Carrera; Alejandro Vázquez-Rodríguez; Zaira I. Jiménez-Balbuena; Pablo H. Ibargüengoytia; Luis Enrique Sucar

Addresses: Instituto Tecnológico y de Estudios Superiores de Monterrey, Cd de México 14380, Mexico ' Comisión Nacional de Seguridad Nuclear y Salvaguardias, Cd de México 03020, Mexico ' Área de Ingeniería en Recursos Energéticos, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, Col. Vicentina, México DF 09340, Mexico ' Área de Ingeniería en Recursos Energéticos, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, Col. Vicentina, México DF 09340, Mexico ' Instituto Nacional de Electricidad y Energías Limpias, Cuernavaca 62490, Morelos, Mexico ' Instituto Nacional de Astrofísica, Óptica y Electrónica, Tonantzintla 72843, Puebla, Mexico

Abstract: The aim of this paper is the neutronic flux prognosis in a nuclear reactor for faults in the measurement of local power range monitors (LPRMs) in real time using differential probabilistic space-temporal model (DPSTM). The LPRMs provide inputs to the average power range monitor (APRM). The LPRM houses a fission chamber and their associated signal cables. The failure of one or more chains of LPRMs is common during the operational cycle. The circuit averages only LPRM signals that are operational and the output from the averaging circuit for each APRM channel is the route to the process computer. The DPSTM allows a reliable reconstruction in real time signal of those LPRMs that are out of order. The DPSTM is evaluated in terms of predictive accuracy for different time horizons and compared to a time series. The DPSTM based prognosis methodology was developed and validated with real signals of Ringhals stability benchmarks.

Keywords: BWR; LPRMs; APRMs; Ringhals NPP; Bayesian network; neutron flux; spatial-temporal model; Markov random; prognosis process; real time.

DOI: 10.1504/IJNEST.2019.103220

International Journal of Nuclear Energy Science and Technology, 2019 Vol.13 No.3, pp.195 - 215

Received: 10 Sep 2018
Accepted: 13 May 2019
Published online: 22 Oct 2019 *

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