Kitabı oxu: «Cyber-Physical Distributed Systems»
Modeling, Reliability Analysis and Applications
Huadong Mo
School of Engineering and Information Technology, University of New South Wales
Canberra, Australia
Giovanni Sansavini
Institute of Energy and Process Engineering
Department of Mechanical and Process Engineering
ETH Zurich
Zurich, Switzerland
Min Xie
Department of Systems Engineering and Engineering Management
City University of Hong Kong
Hong Kong, China
This edition first published 2021
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Preface
A cyber‐physical system (CPS) consists of a collection of computing devices communicating with one another and interacting with the physical world via sensors and actuators in a feedback loop. Increasingly, such systems are everywhere, from smart buildings to medical devices to automobiles. The emergence of CPSs as a novel paradigm has revolutionized the relationship between humans, computers, and the physical environment. CPSs are still in their infancy, and most recent studies are application‐specific and lack systematic design methodology. As a result, it is challenging to investigate and explore the core system science perspective needed to design and build complex CPSs, which are of great importance in many applications.
Using the underlying theories of systems science, such as probability theory, decision theory, game theory, control theory, data analysis, organizational sociology, behavioral economics, and cognitive psychology, this book addresses foundational issues central across CPS applications, including: (I) System Verification – How to develop effective metrics and methods to verify and certify large and complex CPSs; (II) System Design – How to design CPSs to be safe, secure, and resilient in rapidly evolving environments; (III) Real‐Time Control and Adaptation – How to achieve real‐time dynamic control and behavior adaptation in diverse environments, such as distribution and in network‐challenged spaces; (IV) System of Systems – How to harness communication, computation, and control for developing new integrated systems, reducing concepts to realizable designs, and producing integrated software–hardware systems at a pace far exceeding today's timeline.
In general, this book has four essential topics. Chapters 1 and 2 provide readers who do not have a sufficient background on CPSs with a general introduction, research gaps, and representative CPS applications, including CPS modeling, statistical analysis of CPS performance, probability prediction of CPS state, robust CPS control techniques, and management and optimization of CPS reliability and risk. Chapters 3 and 4 mainly concern the robust control of CPSs by designing optimal control strategies, or resource management to enhance robust performance and improve the reliability index against time delays and packet dropouts, which are the inherent properties of open communication networks. Chapter 5 addresses the data‐driven degradation modeling of aging physical (actuators) and cyber (sensors) components of CPSs, and corresponding optimal maintenance plans to improve the reliability of CPSs. Chapters 6 and 7 investigate the cyber security of CPSs, introduce the general concept of cyberattacks, design vulnerability models, and risk assessment procedures, and develop game‐theoretic mitigation techniques and Bayesian‐based cyberteam deployment strategies.
More specifically, Chapter 1 summarizes the evolution from the traditional physical system to the CPS and provides an overview of dynamic and dependent behaviors to be addressed in the subsequent chapters of the book. The introduction discusses some important and recent challenges in improving traditional physical systems in terms of CPSs, popular research trends in evaluating the impacts of CPSs on society, and opportunities for enhancing the performance of realistic applications, which are primarily network control systems. The detailed properties, requirements, and vulnerabilities of utility systems are also introduced. The reasons why the proposed modeling techniques work is important in a field that would be difficult to deal with if the cyber and physical domains were treated separately.
In Chapter 2, readers acquire the basic knowledge to be used in data‐driven statistical modeling, the estimation of the probabilistic CPS state, and a comprehensive framework for conducting reliability analysis of CPSs. In addition, this chapter introduces how to use to historical data to validate the performance of the proposed CPS model, and how to use performance indexes to facilitate the resilient design of CPSs. Moreover, it also demonstrates a real‐time test platform for various industrial applications and the standard procedures for improving real‐time criteria.
Chapter 3 focuses on the stability of CPSs, where decision makers perform dynamic control and adaptation based on real‐time data from sensors. It provides two examples of the design of the controller parameters for robust system performance. The first example illustrates the development of adaptive control for wide‐area measurement power systems, where communication delays are predicted to provide delay compensation for additional frequency stability. In addition, the integration of control theory, power engineering, and statistical estimation is discussed. The second example is an extension of wide‐area measurement power systems from a dedicated communication network to open communication networks, where occurrences of communication delays and packet dropouts result in the failure of the power management system from renewable energy resources. Explicit and implicit methods are then designed for system integration, analysis, and improvement.
Chapter 4 illustrates a system‐of‐systems framework for the reliability of distributed CPS accounting for the impact of degraded communication networks. This is quite different from the focus of Chapter 3, which mainly covers the stability of CPSs from a control perspective. Based on the collected dataset, the degradation path of open communication networks is described in terms of stochastic continuous time transmission delays and packet dropouts. A distributed generation system with open communication infrastructure is used as an example, which is a multi‐area distributed system that is more complicated than the single‐area power system presented in Chapter 3. An optimal power flow model is proposed to generate consecutive time‐dependent optimal operation scenarios for a distributed CPS. Quantitative analysis is carried out to evaluate the effect of networked degradation on the reliability indexes of CPSs, e.g., energy not supplied and operation cost. A prediction method for reconstructing missing data is proposed to mitigate the influence of packet dropouts, which is universal and applicable to most current industrial applications.
Chapter 5 models the functional dependence between stochastic aging actuators and sensors within their operating environments. This dependence is considered in the time domain, causing a distinct degradation status in the actuators and sensors. Reliability modeling of the stochastic effects and effective maintenance activities are discussed for different types of CPSs, including the cooling system in a nuclear power plant, a one‐area energy system with a single generation group, and a multi‐area energy system with several different generation groups.
Chapter 6 explores the concepts, principles, practices, components, technologies, and tools behind risk management for cybersecurity of CPSs, providing practical experience through a realistic case study that focuses on the methodologies available to identify and assess such threats, evaluate their impact, and determine appropriate measures to prevent, mitigate, and recover from any threat or disruptive event so that the operations and profitability of the organizations are maintained and maximized.
Chapter 6 presents the framework of CPSs under cyberattacks from a game‐theoretic perspective, which makes use of statistical data to model the behavior of cyberattacks and study the dynamic game between the network defender and attacker at the system level. For current utility CPSs, cyber threats from supervisory control and data acquisition (SCADA) systems, and spear‐phishing attacks on the accounts of internal employees to gain access to dedicated communication networks are investigated in Chapters 6 and 7. In addition, these chapters focus on how to modify the basic modeling techniques presented in Chapter 2 to describe cyber vulnerabilities, such as seizing the SCADA system under control, disabling/destroying IT infrastructure components, and denial‐of‐service (DoS) attacks on the control center in smart grids. Based on historical data for IT security spending, the cost of launching distributed DoS attacks, and the occurrence probability of cyber event losses, the contest intensity between the attacker and defender can be accurately predicted for the next period to guide the design of an effective network protection plan, that is, a game‐theoretic protection plan and a Bayesian‐based cyberteam deployment.
Chapter 7 investigates sequential control problems (i.e., sequential cyberteam deployment) in modern CPSs by introducing an adversarial cost sequence with a variation constraint. Chapter 6 reviews the data‐driven vulnerability model, and Chapter 7 deals with the dataset of the arrival time of cyberattacks, which uncovers the statistical pattern of attackers. To solve such problems, a fundamental idea is to first obtain sampled parameters for the arrival model of cyberattacks from the posterior distribution of realistic cyberattack arrival records. The reinforcement learning model for estimating parameters is formulated as a partly parameterized Bayesian model. As a result, the sampled parameters are used instead of the true parameters. The paradigm of this framework can also be applied to other classical models, although specific models are used here for illustration purposes. Next, a Bayesian multi‐node bandit is built to cope with the problem, and an online learning algorithm (the Thompson‐Hedge algorithm) is forwarded to retain a converging regret function that is a function of the cyberteam deployment. By comparison with the existing algorithm, the convergence rate of the regret function in the proposed algorithm is found to be superior.
Each of Chapters 3–7 can be read independently when one is interested in a specific type of application or further research, making the book attractive to readers from different areas or positions. This book has the following distinct features:
• It is the first book to systematically focus on CPSs with respect to modeling and reliability analysis.
• It provides a comprehensive treatment of imperfect fault coverage (single level/multi‐level or modular), functional dependence, common cause failures (deterministic and probabilistic), competing failures (deterministic and probabilistic), and dynamic standby sparing.
• It includes abundant illustrative examples and case studies based on real‐world systems.
• It covers recent advances in combinatorial models and algorithms for CPS modeling and analysis.
• It has a rich set of references, providing helpful resources for readers to pursue further research and study of the topics.
The target audience of the book is undergraduate (senior level) and graduate students, engineers, and researchers in system science and related disciplines, including those in computers, telecommunications, transportation, and other industries. Readers should have some knowledge of basic probability theory, control theory, computer science, optimization, game theory, and stochastic processes. However, the book includes a chapter reviewing the fundamentals that readers need to know for understanding the content of the other chapters covering advanced topics in CPSs and case studies. The book can provide readers with knowledge and insight of CPS behaviors, as well as the skills of modeling and analyzing these behaviors to guide the resilient design of real‐world critical systems. Thus, the book includes necessary background information, making it self‐contained. For some detailed topics, selected for their importance and application potential, references are provided for those interested in further details.
We would like to express our sincere appreciation to the many researchers who have proposed some underlying concepts, frameworks, and methods used in this book, or who have co‐authored with us some topics of the book and provided their insights; to name a few, Professor Enrico Zio from Politecnico di Milano, Professor Yanfu Li from Tsinghua University, Dr. Gregory Levitin from the Israel Electric Corporation, Professor David W. Coit from Rutgers University, and Professor Junlin Xiong from the University of Science and Technology of China. Though there are many other researchers to mention, and we have tried to recognize their significant contributions in the bibliographical references of the book.
Finally, it was our huge pleasure to work with Juliet Booker, managing editor of Electrical & Computer Engineering, John Wiley & Sons Ltd., and her team, who have assisted in the publication of this book. We deeply appreciate their efforts and support.
December 15, 2020Huadong MoMin XieGiovanni Sansavini
Acronyms and Abbreviations
ACE | Area Control Error
AERs | All‐Electric Ranges
AGC | Automatic Generation Control
AGC' | Actual Global Cost
B | Biomass Power
BESS | Battery Energy Storage System
CBM | Condition‐Based Maintenance
CC | Control Center
CCC | Centralized Control Centers
CCF | Cross‐Correlation Factor
CPS | Cyber‐Physical System
CSMA/AMP | Carrier Sense Multiple Access with Arbitration on Message Priority
CSMA/CD | Carrier Sense Multiple Access with Collision Detection
CTMC | Continuous‐Time Markov Chains
DC | Direct Current
DEG | Diesel Engine Generator
DERs | Distributed Energy Resources
DG | Distributed Generation
DGS | Distributed Generation Systems
DHMM | Discrete HMM
DoS | Denial of Service
DR | Demand Response
EENS | Expected ENS
EM | Expectation‐Maximization
ENS | Energy Not Supplied
ERP | Expected RP
ESS | Energy Storage System
EV | Electrical Vehicle
EWMA | Exponentially Weighted Moving Average
FDs | Feeders
FERC | Federal Energy Regulatory Commission
FESS | Flywheel Energy Storage System
FFT | Fast Fourier Transform
G | Natural Gas Plant
GA | Genetic Algorithms
GC | Global Cost
GM | Grey Differential Model
Ls | Loads
LAN | Local Area Networks
LC | Long‐Run Cost Rate
LFC | Load Frequency Control
LP | Linear Programming
HAN | Home Area Networks
HGSAA | Hybrid Genetic‐Simulated‐Annealing Algorithm
HMM | Hidden Markov Model
HPS | Hybrid Power System
MAC | Media Access Control
MADT | Maximum Allowable Delay Time
MCR | Maintenance Cost Rate
MCS | Monte Carlo Simulation
MDEM | Missing Data Expectation Maximization
MLE | Maximum Likelihood Estimation
MNB | Multi‐Node Bandit
MS | Main Supply
MSE | Mean Squared Errors
MTU | Maximum Transmission Unit
NAN | Neighbourhood Area Networks
NCS | Networked Control System
NHPP | Non‐Homogeneous Poisson Process
OPF | Optimal Power Flow
O&M | Operation & Maintenance
PBM | Performance‐Based Maintenance
PCLPs | PHEV Charging Load Profiles
PCM | Percentage of Corrective Maintenance
PI | Proportional–Integral
PID | Proportional–Integral–Derivative
PHEVs | Plug‐In Hybrid Electric Vehicles
PMU | Phasor Measurement Units
PO | Percentage Overshoot
PPM | Percentage of Preventive Maintenance
PSO | Particle Swarm Optimization
PV | Photovoltaic Power
RBD | Reliability Block Diagram
RERs | Renewable Energy Resources
RP | Redundant Power
RT | Rising Time
RTU | Remote Terminal Unit
RUL | Remaining Useful Lifetime
SA | Simulated Annealing
SCADA | Supervisory Control and Data Acquisition
SOC | State of Charge
ST | Settling Time
TCo | Total O&M Cost
TENS | Total ENS
W | Wind Power
WAMS | Wide‐Area Measurement Systems
WAN | Wide Area Networks
WAPS | Wide‐Area Power System
WCSS | Within‐Cluster Sum of Square
WLAN | Wireless Local Area Networks
WTG | Wind Turbine Generator
1
Introduction
In this chapter, descriptions of traditional physical and cyber systems are provided to identify existing challenges. Current research trends of cyber‐physical systems (CPSs) are then illustrated to address these challenges. The major applications of the proposed methods in CPSs are reviewed.
1.1 Challenges of Traditional Physical and Cyber Systems
Over the past three decades, studies have addressed numerous concerns regarding the capability of traditional static modeling methodologies, such as the fault tree method and the event tree method, to adequately and quantitatively analyze the impact of hardware and software interaction on the stochastic behavior of CPSs 12. During the past decade, the dynamical Markov reliability model was proposed to solve similar problems in CPSs 3. Control block diagrams were presented for cooling loop systems. The reliability block diagram (RBD) was then established and used to describe the overall reliability status of individual components in a simplified form 45. However, RBDs are incapable of describing the dynamic maintenance and repairable activities; thus, various dynamic modeling methods have been reviewed in 67. The Markov methodology has the advantage of tracking the dynamic changes and time‐dependent features of CPSs, and simply integrates all failure states that occur after each working state into one failure state. The Markov methodology eliminates most of the failure states into a system failure state (absorbing node) by conducting a necessary fault injection test and achieving a sparse transfer matrix but may still result in a very large model due to many existing surviving states. Its modeling precision largely depends on the number of fault injection tests, and more cycles yield higher accuracy. To avoid the disadvantages of these two methodologies, some studies have proposed hybrid reliability models combining RBDs and Markov models for CPSs 8.
The control block diagram introduces blocks to represent each part of the control system, including the controllers, actuators, and control objectives. Control block diagrams are widely used in modern control systems because they can visually describe the relations among the important components, data flow, and control sign flow. In addition, compared with other mathematical models, they have the advantage of simply reflecting the actual correlations in a CPS. It is reasonable to build a reliability model based on the control block diagram of a CPS. In the model, the controller has many input signals, including commands and system state feedback. In general, commands are the system's expected outputs. Control signal flows are given in the control block diagram, and sensors play an important role in this feedback system. This control block diagram clearly indicates the internal dynamic relations of the system, covering most of the aspects that need to be studied.
For applications in CPSs, we are interested in real‐time performance. Therefore, from a control perspective, the ability to adjust the transient and steady‐state response of a feedback CPS is a beneficial outcome of the design of the CPS. One of the first steps in the design process is to specify the performance measures. In this chapter, we introduce common time‐domain specifications, such as percent overshoot, settling time, time to peak, time to rise, and steady‐state tracking error. We will use selected input signals, such as the step and ramp, to test the response of the CPS. The correlations between the system performance and the stability, reliability, and resilience strategies of CPSs are investigated. We will develop valuable relationships between the performance specifications and the component states for CPSs.
The ability of a feedback CPS to compensate for the consequences of the inherent faults redefines the concept of failures, i.e., the reliability of the CPS is dependent not only on the type of failure that may occur, but also on the evolving states of system output and control signals in each period 910. Classical reliability evaluation methods, such as fault tree analysis, event tree analysis, and failure mode and effect analysis, are not appropriate for application to these evolving states due to the level of complexity and dynamics of CPSs. In 1112, structured analyses and design techniques based on Monte Carlo simulation (MCS) for reliability evaluation are presented. This approach explicitly formalizes the functional interactions between subsystems, identifies the characteristic values affecting the reliability of complex CPSs, and quantifies the reliability, availability, maintainability, and safety (RAMS) parameters related to the operational architecture. As the remaining ability of the system to maintain the expected control goal after faults occur is crucial, ordered sequences of multi‐failure methods have been applied to assess the reliability of all possible CPS architectures 13. A new methodology called a multi‐fault tree is proposed, and time‐ordered sequences of failures are addressed.
In contrast to the aforementioned studies, the reliability of a CPS as a function of the required performance from a control viewpoint is evaluated in 14. The CPS is regarded as a failure if the dynamic performance does not satisfy all the requirements. Difference equations are introduced to describe the stochastic model of the CPS, explicitly illustrating the influence of the transmission delays and packet dropouts on changing the model parameters. A linear discrete‐time dynamic approach for modeling the signal flow in, out, and among all subsystems promotes straightforward calculation of fundamental dynamic aspects, such as times and fault characteristics 15.
MCS has been shown to be a straightforward yet accurate approach for the study of such complex systems 161718. The general approach in MCS for reliability assessment is to generate operational requirements that lead to the failure of the entire system. However, this approach requires knowledge of the system requirements‐to‐failure distribution in advance. In 19, an event‐based MCS method was proposed for multi‐component systems, in which the failure time for each component is generated and then used to verify the success or failure of the system subject to the required operational time. Because no attempt is made to generate the failure time for the entire system, which requires knowledge of the time‐to‐failure distribution of the entire system as well as the distribution approximation at the component level, it is quite different from previous methods and can reduce the possible error and computational effort in estimating the system reliability.
In 20, this method was extended to estimate the reliability of CPSs and replaces the constraint on the number of replications used in 21 with two other constraints, namely, a precision interval and a percentage of simulations belonging to this interval. The networked degradations for each channel are generated and are then used to determine the success or failure of the CPS for a given combination of operational requirements. Therefore, the reliability of the CPS is estimated as a tabulated function of the operational requirements. Compared with the results in 22, the results obtained in 23 guarantee the estimated reliability to satisfy a given precision.
