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Autonomous Nuclear Power Plants with Artificial Intelligence

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This book introduces novel approaches and practical examples  of autonomous nuclear power plants that minimize operator intervention.

Autonomous nuclear power plants with artificial intelligence presents a framework to enable nuclear power plants to autonomously operate and introduces artificial intelligence (AI) techniques to implement its functions. Although nuclear power plants are already highly automated to reduce human errors and guarantee the reliability of system operations, the term “autonomous” is still not popular because AI techniques are regarded as less proven technologies. However, the use of AI techniques and the autonomous operation seems unavoidable because of their great advantages, especially, in advanced reactors and small modular reactors.

The book includes the following topics:

Monitoring, diagnosis, and prediction.

Intelligent control.

Operator support systems.

Operator-autonomous system interaction.

Integration into the autonomous operation system.

This book will provides useful information for researchers and students who are interested in applying AI techniques in the fields of nuclear as well as other industries. This book covers broad practical applications of AI techniques from the classical fault diagnosis to more recent autonomous control. In addition, specific techniques and modelling examples are expected to be very informative to the beginners in the AI studies.

Author(s): Jonghyun Kim, Seungjun Lee, Poong Hyun Seong
Series: Lecture Notes in Energy, 94
Publisher: Springer
Year: 2023

Language: English
Pages: 279
City: Cham

Preface
Acknowledgements
Contents
Acronyms
1 Introduction
1.1 Background
1.2 A Framework of Autonomous NPPs
References
2 Artificial Intelligence and Methods
2.1 Definitions of AI, Machine Learning, and Deep Learning
2.1.1 AI
2.1.2 ML
2.1.3 DL
2.2 Classification of ML Methods Based on Learning Type
2.2.1 Supervised Learning
2.2.2 Unsupervised Learning
2.2.3 Reinforcement Learning (RL)
2.3 Overview of Artificial Neural Networks (ANNs)
2.3.1 History of ANNs
2.3.2 Overview of ANNs
2.4 ANN Algorithms
2.4.1 Convolutional Neural Networks (CNNs)
2.4.2 Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Gated Recurrent Units (GRUs)
2.4.3 Variational Autoencoders (VAEs)
2.4.4 Graph Neural Networks (GNNs)
2.4.5 Generative Adversarial Networks (GANs)
2.5 Model-Based and Data-Based Approaches
References
3 Signal Validation
3.1 Sensor Fault Detection Through Supervised Learning
3.1.1 Sensor Fault Detection System Framework with Supervised Learning
3.1.2 Case Study
3.2 Signal Validation Through Unsupervised Learning
3.2.1 Signal Behaviour in an Emergency Situation
3.2.2 Signal Validation Algorithm Through Unsupervised Learning for an Emergency Situation
3.2.3 Validation
3.3 Signal Generation with a GAN
3.3.1 GAN
3.3.2 GAN-Based Signal Reconstruction Method
3.3.3 Experiments
References
4 Diagnosis
4.1 Diagnosis of Abnormal Situations with a CNN
4.1.1 Raw Data Generation
4.1.2 Data Transformation
4.1.3 Structure of the CNN Model
4.1.4 Performance Evaluation Metrics
4.1.5 Experimental Settings
4.1.6 Results
4.2 Diagnosis of Abnormal Situations with a GRU
4.2.1 Characteristics of Abnormal Operation Data
4.2.2 PCA
4.2.3 GRU
4.2.4 Two-Stage Model Using GRU
4.2.5 Experimental Settings
4.2.6 Results
4.3 Diagnosis of Abnormal Situations with an LSTM and VAE
4.3.1 Methods
4.3.2 Diagnostic Algorithm for Abnormal Situations with LSTM and VAE
4.3.3 Implementation
4.4 Sensor Fault-Tolerant Accident Diagnosis
4.4.1 Sensor Fault-Tolerant Diagnosis System Framework
4.4.2 Comparison Results
4.4.3 Considerations for Optimal Sensor Fault Mitigation
4.5 Diagnosis of Multiple Accidents with a GNN
4.5.1 GNN
4.5.2 GNN-Based Diagnosis Algorithm Representing System Configuration
4.5.3 Experiments
4.6 Interpretable Diagnosis with Explainable AI
4.6.1 Need for Interpretable Diagnosis
4.6.2 Explainable AI
4.6.3 Examples of Explanation Techniques
4.6.4 Application to an Abnormal Event Diagnosis Model
References
5 Prediction
5.1 Real-Time Parameter Prediction
5.1.1 Multi-step Prediction Strategies
5.1.2 Plant Parameter Prediction Model with Multi-step Prediction Strategies
5.1.3 Case Study with Data from an NPP Simulator
5.1.4 Operator Support System with Prediction
References
6 Control
6.1 Autonomous Control for Normal and Emergency Operations with RL
6.1.1 Case Study 1: Power-Increase Operation
6.1.2 Case Study 2: Emergency Operation
References
7 Monitoring
7.1 Operation Validation System Through Prediction
7.1.1 CIA System Framework
7.1.2 Step 1 Filtering: PCC Module
7.1.3 Step 2 Filtering: COSIE Module
7.1.4 CIA System Prototype
7.1.5 Case Study
7.1.6 Summary and Scalability of the Operation Validation System
7.2 Technical Specification Monitoring System
7.2.1 Identification of Functional Requirements
7.2.2 Conceptual Design of the TSMS
7.2.3 Implementation of the TSMS
7.2.4 TSMS Prototype
References
8 Human–Autonomous System Interface and Decision-Making
8.1 Human–Autonomous System Interface
8.2 Decision-Making
References
9 Conclusion
Index