DEVELOPMENT OF INSTRUMENTATION AND CONTROL SYSTEMS FOR AN INTEGRAL LARGE SCALE PRESSURIZED WATER REACTOR

Small and large scale integral light water reactors are being developed to supply electrical power and to meet the needs of process heat, primarily for water desalination. This dissertation research focuses on the instrumentation and control of a large integral inherently safe light water reactor (designated as I2S-LWR) which is being designed as part of a grant by the U.S. Department of Energy Integrated Research Project (IRP). This 969 MWe integral pressurized water reactor (PWR) incorporates as many passive safety features as possible while maintaining competitive costs with current light water reactors. In support of this work, the University of Tennessee has been engaged in research to solve the instrumentation and control challenges posed by such a reactor design. This dissertation is a contribution to this effort. The objectives of this dissertation are to establish the feasibility and conceptual development of instrumentation strategies and control approaches for the I2S-LWR, with consideration to the state of the art of the field. The objectives of this work are accomplished by the completion of the following tasks: Assessment of instrumentation needs and technology gaps associated with the instrumentation of the I2S-LWR for process monitoring and control purposes. Development of dynamic models of a large integral PWR core, micro-channel heat exchangers (MCHX) that are contained within the reactor pressure vessel, and steam flashing drums located external to the containment building. Development and demonstration of control strategies for reactor power regulation, steam flashing drum pressure regulation, and flashing drum water level regulation for steady state and load-following conditions. Simulation, detection, and diagnosis of process anomalies in the I2S-LWR model. This dissertation is innovative and significant in that it reports the first instrumentation and control study of nuclear steam supply by integral pressurized water reactor coupled to an isenthalpic expansion vessel for steam generation. Further, this dissertation addresses the instrumentation and control challenges associated with integral reactors, as well as improvements to inherent safety possible in the instrumentation and control design of integral reactors. The results of analysis and simulation demonstrate the successful development of dynamic modeling, control strategies, and instrumentation for a large integral PWR

Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey

The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including hardware and software vulnerabilities, communication issues, and personnel errors, etc. Therefore, enhancing the cyber-resiliency of DER-based smart grid - the ability to survive successful cyber intrusions - is becoming increasingly vital and has garnered significant attention from both industry and academia. In this survey, we aim to provide a systematical and comprehensive review regarding the cyber-resiliency enhancement (CRE) of DER-based smart grid. Firstly, an integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasis on vulnerability identification and impact analysis. Then, the defense-in-depth strategies encompassing prevention, detection, mitigation, and recovery are comprehensively surveyed, systematically classified, and rigorously compared. A CRE framework is subsequently proposed to incorporate the five key resiliency enablers. Finally, challenges and future directions are discussed in details. The overall aim of this survey is to demonstrate the development trend of CRE methods and motivate further efforts to improve the cyber-resiliency of DER-based smart grid.Comment: Submitted to IEEE Transactions on Smart Grid for Publication Consideratio

Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward

Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs ($\mu$-PMUs), and smart meters. However, a large amount of data collected by these devices brings several challenges as control room operators need to use this data with models to make confident decisions for reliable and resilient operation of the cyber-power systems. Machine-learning (ML) based tools can provide a reliable interpretation of the deluge of data obtained from the field. For the decision-makers to ensure reliable network operation under all operating conditions, these tools need to identify solutions that are feasible and satisfy the system constraints, while being efficient, trustworthy, and interpretable. This resulted in the increasing popularity of physics-informed machine learning (PIML) approaches, as these methods overcome challenges that model-based or data-driven ML methods face in silos. This work aims at the following: a) review existing strategies and techniques for incorporating underlying physical principles of the power grid into different types of ML approaches (supervised/semi-supervised learning, unsupervised learning, and reinforcement learning (RL)); b) explore the existing works on PIML methods for anomaly detection, classification, localization, and mitigation in power transmission and distribution systems, c) discuss improvements in existing methods through consideration of potential challenges while also addressing the limitations to make them suitable for real-world applications

Deterministic Artificial Intelligence

Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

The progress made by levels 1, 2, and 3 of the Office of Space Station in developing and applying advanced automation and robotics technology is described. Emphasis is placed upon the Space Station Freedom Program responses to specific recommendations made in the Advanced Technology Advisory Committee (ATAC) progress report 10, the flight telerobotic servicer, and the Advanced Development Program. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for the Space Station Freedom

Deterministic Artificial Intelligence

Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book

False Data Injection Attack Detection based on Hilbert-Huang Transform in AC Smart Islands

In Smart Island (SI) systems, operators of power distribution system usually utilize actual-time measurement information as the Advanced Metering Infrastructure (AMI) to have an accurate, efficient, advanced control and monitor of whole their system. SI system can be vulnerable to complicated information integrity attacks such as False Data Injection Attack (FDIA) on some equipment including sensors and controllers, which can generate misleading operational decision in the system. Hence, lack of detailed research in the evaluation of power system that links the FDIAs with system stability is felt, and it will be important for both assessment of the effect of cyber-attack and taking preventive protection measures. In this regards, time-frequency-based differential approach is proposed for SI cyber-attack detection according to non-stationary signal assessment. In this paper, non-stationary signal processing approach of Hilbert-Huang Transform (HHT) is performed for the FDIA detection in several case studies. Since various critical case studies with a small FDIA in data where accurate and efficient detection can be a challenge, the simulation results confirm the efficiency of HHT approach and the proposed detection frame is compared with shallow model. In this research, the configuration of the SI test case is developed in the MATLAB software with several Distributed Generations (DGs). As a result, it is found that the HHT approach is completely efficient and reliable for FDIA detection target in AC-SI. The simulation results verify that the proposed model is able to achieve accuracy rate of 93.17% and can detect FDIAs less than 50 ms from cyber-attack starting in different kind of scenarios

Analysis of spacecraft anomalies

The anomalies from 316 spacecraft covering the entire U.S. space program were analyzed to determine if there were any experimental or technological programs which could be implemented to remove the anomalies from future space activity. Thirty specific categories of anomalies were found to cover nearly 85 percent of all observed anomalies. Thirteen experiments were defined to deal with 17 of these categories; nine additional experiments were identified to deal with other classes of observed and anticipated anomalies. Preliminary analyses indicate that all 22 experimental programs are both technically feasible and economically viable