Autonomous Nuclear Power Plants with Artificial Intelligence

280 Pages·2023·9.82 MB·English

by  Jonghyun Kim, Seungjun Lee, Poong Hyun Seong

Lecture Notes in Energy 94
Jonghyun Kim
Seungjun Lee
Poong Hyun Seong
Autonomous
Nuclear
Power Plants
with Artificial
Intelligence
Lecture Notes in Energy
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· ·
Jonghyun Kim Seungjun Lee Poong Hyun Seong
Autonomous Nuclear Power
Plants with Artificial
Intelligence
Jonghyun Kim Seungjun Lee
Department of Nuclear Engineering Department of Nuclear Engineering
Chosun University UNIST
Gwangju, Korea (Republic of) Ulsan, Republic of Korea
Poong Hyun Seong
Department of Nuclear and Quantum
Engineering
KAIST
Daejeon, Republic of Korea
ISSN 2195-1284 ISSN 2195-1292 (electronic)
Lecture Notes in Energy
ISBN 978-3-031-22385-3 ISBN 978-3-031-22386-0 (eBook)
https://doi.org/10.1007/978-3-031-22386-0
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Preface
The top priority of a nuclear power plant (NPP) is safety. Indeed, safety is a ubiquitous
concern over the whole life cycle of an NPP, from its design to decommissioning.
But in a complex large-scale system with a huge number of components such as
an NPP, it is not easy to identify all vulnerabilities and achieve a perfect level of
safety. As the TMI-2, Chernobyl, and Fukushima severe accidents demonstrated,
an NPP emergency does not occur by a single cause but rather by a complicated
combination of hardware, software, human operators, decision-makers, and so on.
Research has revealed that human error takes up more than half of core damage
frequency, a common risk metric, and it is also known that human factors were the
direct or indirect cause of most nuclear accidents in history.
One of the approaches to enhance the safety of NPPs is to develop operator support
systems to reduce latent human errors by optimizing the operators’ required work-
load or automating some portion of their tasks. In fact, research into the application of
artificial intelligence (AI) to NPPs has been performed for decades, but few develop-
ments have actually been reflected in real NPPs. Despite this though, AI technology
is now being particularly highlighted again due to increases in data processing and
advancements in hardware design, graphics processing units, and related methods.
This has led to an explosive growth in recent years of research related to AI tech-
niques in the nuclear industry. Such global efforts toward applying AI techniques
to NPPs may result in better performance than conventional methods based on the
characteristics of NPPs, such as complexity in operation, dynamic behaviors, and
high burden in decision-making.
This book is divided into nine chapters. In the first part, Chaps. 1 and 2, the
framework of the autonomous NPP and the fundamentals of AI techniques are
introduced. Chapter 1 suggests a high-level framework for an autonomous NPP
including the functional architecture of the autonomous operation systems. This
chapter defines multiple levels and sub-functions necessary for automating oper-
ator tasks. The framework consists of monitoring, autonomous control, the human–
autonomous system interface, and the intelligent management of functions. Chapter 2
provides fundamental explanations of the various AI techniques appearing in the book
as an overview.
v
vi Preface
In Chaps. 3–6, the essential methods necessary for developing operator support
systems and autonomous operating systems are introduced. Chapters 3, 4, and 5
apply various AI-based techniques for the monitoring of NPPs including signal vali-
dation, diagnosis of abnormal situations, and prediction of plant behavior using both
supervised and unsupervised learning methods. Chapter 6 presents AI applications
for autonomous control using reinforcement learning for both normal and emergency
situations along with domain analysis methods.
Lastly, in Chaps. 7 and 8, applications are introduced. Chapter 7 provides an
example of an integrated autonomous operating system for a pressurized water reactor
that implements the suggested framework. Lastly, Chap. 8 deals with the interac-
tion between operators and the autonomous systems. Even though the autonomous
operation systems seek to minimize operator interventions, supervision and manual
control by human operators are inevitable in NPPs for safety reasons. Thus, the last
chapter addresses the design of the human–autonomous system interface and oper-
ator support systems to reduce the task burden of operators as well as increase their
situational awareness in a supervisory role.
This book is expected to provide useful information for researchers and students
who are interested in applying AI techniques in the nuclear field as well as other indus-
tries. Various potential areas of AI applications and available methods were discussed
with examples. Traditional approaches to recent applications of AI were examined.
In addition, the specific techniques and modeling examples provided would be infor-
mative for beginners in AI studies. While the focus of this book was the autonomous
operation of NPPs with AI, the methods addressed here would also be applicable to
other industries that are both complex and safety-critical.
Gwangju, Korea (Republic of) Prof. Jonghyun Kim
Ulsan, Republic of Korea Prof. Seungjun Lee
Daejeon, Republic of Korea Prof. Poong Hyun Seong
October 2022
Acknowledgements
This book includes various researches that were supported by National Research
Foundation of Korea (NRF) grants funded by the Korean government (MSIT) from
2016 to 2022 (NRF-2016R1A5A1013919). The authors appreciate the assistance and
support of the Korean national fund that initiated the research toward the application
of artificial intelligence to nuclear power plant operation. The authors would also like
to acknowledge the sincere contribution and effort by students and graduates from
three universities: Dr. Daeil Lee, Hyojin Kim, Younhee Choi, and Subong Lee from
Chosun University, Jae Min Kim, Jeeyea Ahn, Jeonghun Choi, Junyong Bae, and Ji
Hyeon Shin from the Ulsan National Institute of Science and Technology (UNIST),
and Dr. Young Ho Chae and Dr. Seung Geun Kim from the Korea Advanced Institute
of Science and Technology (KAIST).
vii
Contents
1 Introduction ................................................... 1
1.1 Background ............................................... 1
1.2 A Framework of Autonomous NPPs ........................... 4
References ..................................................... 7
2 Artificial Intelligence and Methods ............................... 9
2.1 Definitions of AI, Machine Learning, and Deep Learning ......... 9
2.1.1 AI ................................................. 9
2.1.2 ML ................................................ 9
2.1.3 DL ................................................. 10
2.2 Classification of ML Methods Based on Learning Type .......... 10
2.2.1 Supervised Learning .................................. 11
2.2.2 Unsupervised Learning ............................... 12
2.2.3 Reinforcement Learning (RL) .......................... 12
2.3 Overview of Artificial Neural Networks (ANNs) ................ 12
2.3.1 History of ANNs ..................................... 12
2.3.2 Overview of ANNs ................................... 13
2.4 ANN Algorithms ........................................... 18
2.4.1 Convolutional Neural Networks (CNNs) ................. 18
2.4.2 Recurrent Neural Networks (RNNs), Long
Short-Term Memory Networks (LSTMs), and Gated
Recurrent Units (GRUs) .............................. 19
2.4.3 Variational Autoencoders (VAEs) ...................... 22
2.4.4 Graph Neural Networks (GNNs) ....................... 24
2.4.5 Generative Adversarial Networks (GANs) ............... 25
2.5 Model-Based and Data-Based Approaches ..................... 27
References ..................................................... 27
3 Signal Validation ............................................... 29
3.1 Sensor Fault Detection Through Supervised Learning ............ 31
3.1.1 Sensor Fault Detection System Framework
with Supervised Learning ............................. 32
ix
x Contents
3.1.2 Case Study .......................................... 38
3.2 Signal Validation Through Unsupervised Learning .............. 42
3.2.1 Signal Behaviour in an Emergency Situation ............. 43
3.2.2 Signal Validation Algorithm Through Unsupervised
Learning for an Emergency Situation ................... 44
3.2.3 Validation ........................................... 56
3.3 Signal Generation with a GAN ............................... 57
3.3.1 GAN ............................................... 58
3.3.2 GAN-Based Signal Reconstruction Method .............. 61
3.3.3 Experiments ......................................... 65
References ..................................................... 76
4 Diagnosis ...................................................... 79
4.1 Diagnosis of Abnormal Situations with a CNN ................. 80
4.1.1 Raw Data Generation ................................. 81
4.1.2 Data Transformation .................................. 81
4.1.3 Structure of the CNN Model ........................... 83
4.1.4 Performance Evaluation Metrics ....................... 86
4.1.5 Experimental Settings ................................ 87
4.1.6 Results ............................................. 89
4.2 Diagnosis of Abnormal Situations with a GRU .................. 94
4.2.1 Characteristics of Abnormal Operation Data ............. 95
4.2.2 PCA ............................................... 96
4.2.3 GRU ............................................... 97
4.2.4 Two-Stage Model Using GRU ......................... 98
4.2.5 Experimental Settings ................................ 100
4.2.6 Results ............................................. 100
4.3 Diagnosis of Abnormal Situations with an LSTM and VAE ....... 103
4.3.1 Methods ............................................ 104
4.3.2 Diagnostic Algorithm for Abnormal Situations
with LSTM and VAE ................................. 107
4.3.3 Implementation ...................................... 112
4.4 Sensor Fault-Tolerant Accident Diagnosis ...................... 118
4.4.1 Sensor Fault-Tolerant Diagnosis System Framework ...... 121
4.4.2 Comparison Results .................................. 127
4.4.3 Considerations for Optimal Sensor Fault Mitigation ....... 134
4.5 Diagnosis of Multiple Accidents with a GNN ................... 135
4.5.1 GNN ............................................... 135
4.5.2 GNN-Based Diagnosis Algorithm Representing
System Configuration ................................. 137
4.5.3 Experiments ......................................... 140
4.6 Interpretable Diagnosis with Explainable AI .................... 145
4.6.1 Need for Interpretable Diagnosis ....................... 145
4.6.2 Explainable AI ...................................... 146
4.6.3 Examples of Explanation Techniques ................... 147