Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

Xingang Zhao,Junyung Kim,Hyun Gook Kang,Pradeep Ramuhalli,Kyle Warns,Sacit Cetiner,Michael Golay,Xinyan Wang

doi:10.3389/fenrg.2021.696785

Abstract

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

Highlights

Reducing anthropogenic greenhouse gas (GHG) emissions for climate change mitigation while expanding energy access to billions of people is a central global challenge of this century
it places a strong emphasis on the state of the art
the overarching gaps that still must be addressed by the nuclear industry

Summary

Introduction

Reducing anthropogenic greenhouse gas (GHG) emissions for climate change mitigation while expanding energy access to billions of people is a central global challenge of this century. Despite its important role in energy transitions to meet climate goals, the nuclear industry is facing an uncertain future in many countries, due to the March 2011 Fukushima accident in Japan and more fundamentally for economic reasons. In advanced economies such as the United States, unfavorable market conditions—including weak growth in electricity demand, low natural gas prices, and increasing competition from renewables-based power supply—are putting pressure on the financial performance of existing nuclear power plants (NPPs), which may lead to their early retirements. Near-term interests are rising in LWR-based small modular reactors (SMRs) and mature Generation-IV concepts

Methods

Conclusion