Application of data reconciliation for fault detection and isolation of in-core self-powered neutron detectors using iterative principal component test

Abstract In-core neutron detectors are used in large nuclear reactors for the purpose of acquiring knowledge about the spatial flux distribution, which helps in control, monitoring and protection of the reactors. Random errors and faults, which cause degradations of the signals from the true values, invalidate the in-core detector measurement data. A scheme utilizing the analytical redundancy among physically redundant in-core detectors greatly facilitates removing the ill-effects of these errors. Particularly, data-based or process-history-based schemes are suitable when there are a large number of variables as in the case of in-core detectors. In this paper, application of a Data Reconciliation (DR) scheme (with the aim of minimizing the effect of random errors) coupled with a Fault Detection and Isolation (FDI) technique (which makes the data free from faults) to the in-core detector measurement data of the Advanced Heavy Water Reactor (AHWR) is presented. This reactor uses Vanadium Self-Powered Neutron Detectors (VSPNDs) as in-core neutron detectors. Since the constraint model developed for all the in-core neutron detectors as a whole cannot yield satisfactory performance from the DR and FDI, they are developed for the individual clusters of VSPNDs using the Iterative Principal Component Analysis (IPCA), a multi-variate statistical technique. The FDI technique used is the Iterative Principal Component Test (IPCT) discussed in Sagar et al. (2015c). The effectiveness of this scheme is tested as follows: (i) the mathematical model of the AHWR is used to simulate measurement data from VSPNDs; (ii) the data from VSPNDs are grouped as clusters based on correlations among the variables; and (iii) the effectiveness of the DR-based FDI of VSPNDs in some clusters is established based on some performance indices, when the spatial control of the reactor is active.

[1]  C. M. Crowe,et al.  Reconciliation of process flow rates by matrix projection. Part II: The nonlinear case , 1983 .

[2]  Richard S. H. Mah,et al.  Generalized likelihood ratio method for gross error identification , 1987 .

[3]  Sirish L. Shah,et al.  Model Identification and Error Covariance Matrix Estimation from Noisy Data Using PCA , 2004 .

[4]  Charles E. Heckler,et al.  Applied Multivariate Statistical Analysis , 2005, Technometrics.

[5]  A. P. Tiwari,et al.  Online Fault Detection and Diagnosis of In-Core Neutron Detectors Using Generalized Likelihood Ratio Method , 2015, IEEE Transactions on Nuclear Science.

[6]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[7]  Akhilanand Pati Tiwari,et al.  Spatial control of a large pressurized heavy water reactor , 1996 .

[8]  Shankar Narasimhan,et al.  Data reconciliation & gross error detection: an intelligent use of process data , 1999 .

[9]  Hongwei Tong,et al.  Detection of gross erros in data reconciliation by principal component analysis , 1995 .

[10]  S. R. Shimjith,et al.  Space-time kinetics modeling for the determination of neutron detector signals in Advanced Heavy Water Reactor , 2013, 2013 IEEE International Conference on Control Applications (CCA).

[11]  Andrew N. Hrymak,et al.  Reconciliation of Process Flow Rates by , 1983 .

[12]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[13]  Anil Kakodkar,et al.  Design and development of the AHWR—the Indian thorium fuelled innovative nuclear reactor , 2006 .

[14]  Petre Stoica,et al.  A test for whiteness , 1977 .

[15]  Akhilanand Pati Tiwari,et al.  An iterative principal component test for fault detection and isolation , 2015, 2015 IEEE Conference on Control Applications (CCA).

[16]  A. P. Tiwari,et al.  Application of Data Reconciliation and Fault Detection and Isolation of Ion Chambers in Advanced Heavy Water Reactor , 2015 .

[17]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[18]  Madhu N. Belur,et al.  Data reconciliation and gross error analysis of self powered neutron detectors: comparison of PCA and IPCA based models , 2012 .

[19]  Si-Zhao Joe Qin,et al.  Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..

[20]  Madhu N. Belur,et al.  Clustering of Self Powered Neutron Detectors: Combining Prompt and Slow Dynamics , 2014, IEEE Transactions on Nuclear Science.

[21]  Qing Li,et al.  Fault detection and isolation for self powered neutron detectors based on Principal Component Analysis , 2015 .

[22]  H. Hotelling.  Multivariate Quality Control-illustrated by the air testing of sample bombsights , 1947 .