Improved assessment of delayed neutron detector data in CANDU reactors

Aylward, William Thomas and Wallace, Christopher and West, Graeme and McEwan, Curtis (2019) Improved assessment of delayed neutron detector data in CANDU reactors. In: 27th International Conference on Nuclear Engineering, 2019-05-19 - 2019-05-24, Tsukuba International Congress Center. (In Press)

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Abstract

A common challenge at nuclear power plants is to ensure that routinely-collected data is fully utilised. Data analytics provides an opportunity for improvements in prognostics and health monitoring by identifying correlations in related datasets without major capital investment. This paper describes work focused on improving the fuel defect identification process in Bruce Power’s eight CANDU nuclear reactors in the province of Ontario, Canada. The CANDU reactor comprises individually-pressurised horizontal channels which can be refuelled without taking the reactor offline. The detection and location of fuel defects is typically achieved using two systems: the first monitors the primary coolant for the presence of fission products and specific radionuclides, and is used to detect the presence of fuel defects within the core. The second system is deployed periodically and uses the emission of delayed neutrons to identify the channel containing defect fuel. In this paper we focus on improving the assessment of on-line delayed neutron monitoring data, with the aim of reducing the time taken between initial detection of a defect somewhere in the core to scheduling the channel for removal of the damaged fuel. The existing process is manually intensive and reliant on a domain expert to make a judgement call as to which channel contains the fuel defect. The defect location process is challenging as the data has low resolution and high variance. Operating experience using the existing data processing system has demonstrated that the time taken for the channel containing the fuel defect to become distinguishable from its neighbouring channels varies considerably. A first stage of investigation examines potential improvements to the current data processing system: developing and applying new analytic techniques in this area has shown promising results, with some fuel defects potentially identified several days earlier than the current system. Results from some short representative case studies are presented and further work will consider a larger volume of data. In addition, an extensive historical dataset is available which spans several years. In the second stage of investigation, the paper explores previously undocumented trends in this data and discusses the potential to produce correlations with other reactor parameters. The application of this knowledge can lead to opportunities in the use of Machine Learning algorithms to allow more accurate predictions regarding which channel contains the fuel defect to be made.

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