Real time nuclear power plant operating state cognitive algorithm development using dynamic Bayesian network

Abstract In a reactor, various reactor instruments inform the operator of changes in the operating conditions. However, until now, the identification of the reactor state from interpretations of the instrumentation reading has been mostly left to the human operators, which sometimes can be flawed depending on circumstances. Therefore, an artificial cognitive system that can recognize the nuclear reactor state is suggested in this paper to aid the operator under severe environmental stress. Two reactor operating states are preliminarily considered: Normal state and loss of coolant accident state. The artificial cognitive system predicts the state of the reactor in real time, and it determines the type of LOCA if the reactor state is determined to be in an accident state. The proposed system uses only reactor protection system monitoring parameters among various available measurements. The proposed system is composed of two dynamic Bayesian models: Reactor State Determination (RSD) Model, and Accident Type Categorization (ATC) Model. When an accident occurs, the RSD model took 0.3 s to recognize it with 100% accuracy. The average accuracy of the ATC model is about 88%. The average accuracy increased more when the model was refined which suggests the model can be further improved in the future.

[1]  Hissam Tawfik,et al.  A Neural Networks Design Methodology for Detecting Loss of Coolant Accidents in Nuclear Power Plants , 2018, Applications of Big Data Analytics.

[2]  Qin Zhang,et al.  Pilot study of dynamic Bayesian networks approach for fault diagnostics and accident progression prediction in HTR-PM , 2015 .

[3]  Vojkan Mihajlovic,et al.  Dynamic Bayesian Networks: A State of the Art , 2001 .

[4]  Belkacem Ould Bouamama,et al.  Bayesian reliability models of Weibull systems: State of the art , 2012, Int. J. Appl. Math. Comput. Sci..

[5]  K. Loparo,et al.  HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings , 2005 .

[6]  Claudio Cobelli,et al.  A Dynamic Bayesian Network model for long-term simulation of clinical complications in type 1 diabetes , 2015, J. Biomed. Informatics.

[7]  Carlos J. Perez,et al.  Bayesian analysis of a generalized lognormal distribution , 2009, Comput. Stat. Data Anal..

[8]  Kyung Doo Kim,et al.  MARS code manual volume I: code structure, system models, and solution methods , 2010 .

[9]  Marco Oesting,et al.  Bayesian inference for multivariate extreme value distributions , 2016, 1611.05602.

[10]  D. Darling,et al.  A Test of Goodness of Fit , 1954 .

[11]  Jeong Ik Lee,et al.  Preliminary study of applying adjoint-based mesh optimization method to nuclear power plant safety analysis , 2017 .

[12]  A. Srivastava,et al.  Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks , 2009, Reliab. Eng. Syst. Saf..

[13]  Man Gyun Na,et al.  Prediction of Leak Flow Rate Using Fuzzy Neural Networks in Severe Post-LOCA Circumstances , 2014, IEEE Transactions on Nuclear Science.

[14]  Jin Gu,et al.  Application of FBOLES—a prototype expert system for fault diagnosis in nuclear power plants , 1994 .

[15]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[16]  Peter J. F. Lucas,et al.  Understanding disease processes by partitioned dynamic Bayesian networks , 2016, J. Biomed. Informatics.

[17]  Luigi Portinale,et al.  Dynamic Bayesian Networks for Fault Detection, Identification, and Recovery in Autonomous Spacecraft , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[18]  Yu Wang,et al.  Fault propagation behavior study and root cause reasoning with dynamic Bayesian network based framework , 2015 .

[19]  Zhiwei Wang,et al.  Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information , 2017 .

[20]  Sankaran Mahadevan,et al.  Dynamic Bayesian Network for Aircraft Wing Health Monitoring Digital Twin , 2017 .

[21]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[22]  Miroslaw J. Skibniewski,et al.  A dynamic Bayesian network based approach to safety decision support in tunnel construction , 2015, Reliab. Eng. Syst. Saf..

[23]  George F. Luger,et al.  Intelligent Modeling for Nuclear Power Plant Accident Management , 2017, Int. J. Artif. Intell. Tools.