Details

<p>Preface v</p> <p>List of Acronyms and Abbreviations ix</p> <p> </p> <p>Introduction 1</p> <p>Challenges of Traditional Physical and Cyber Systems 1</p> <p>Research Trends in Cyber-Physical Systems (CPSs) 3</p> <p>Stability of CPSs 3</p> <p>Reliability of CPSs 6</p> <p>Opportunities for CPS Applications 7</p> <p>Managing Reliability and Feasibility of CPSs 7</p> <p>Ensuring Cybersecurity of CPSs 9</p> <p> </p> <p>Fundamentals of CPSs 13</p> <p>Models for Exploring CPSs 14</p> <p>Control-Block-Diagram of CPSs 14</p> <p>Control Signal in CPSs 14</p> <p>Degraded Actuator and Sensor 14</p> <p>Time-Varying Model of CPSs 15</p> <p>Implementation in TrueTime Simulator 16</p> <p>Introduction of TrueTime Simulator 16</p> <p>Architecture of CPSs in TrueTime 17</p> <p>Evaluation and Verification of CPSs 18</p> <p>CPS Performance Evaluation 18</p> <p>CPS Performance Index 18</p> <p>Reliability Evaluation of CPSs 19</p> <p>CPS Model Verification 20</p> <p>CPS Performance Improvement 21</p> <p>PSO-Based Reliability Enhancement 22</p> <p>Optimal PID-Automatic Generation Control (AGC) 23</p> <p> </p> <p>Stability Enhancement of CPSs 29</p> <p>Integration of Physical and Cyber Models 30</p> <p>Basics of Wide-Area Power Systems (WAPS) 30</p> <p>Physical Layer 30</p> <p>Cyber Layer 31</p> <p>WAPS Realized in TrueTime 32</p> <p>An Illustrative WAPS 33</p> <p>Illustrative Physical Layer 33</p> <p>Illustrative Cyber Layer 34</p> <p>Illustrative Integrated System 36</p> <p>Settings of Stability Analysis 36</p> <p>Settings of Delay Predictions 37</p> <p>Settings of Illustrative WAPS 37</p> <p>Cases for Illustrative WAPS 38</p> <p>Hidden Markov Model (HMM)-Based Stability Improvement 38</p> <p>Online Smith Predictor 38</p> <p>Initialization of Discrete HMM (DHMM) 39</p> <p>Parameter Estimation of DHMM 41</p> <p>Delay Prediction via DHMM 43</p> <p>Smith Predictor Structure 44</p> <p>Delay Predictions 44</p> <p>Settings of DHMM 45</p> <p>Prediction Comparison 46</p> <p>Performance of Smith Predictor 47</p> <p>Settings of Smith Predictor 47</p> <p>Analysis of Case 1 47</p> <p>Analysis of Case 2 48</p> <p>Stability Enhancement of Illustrative WAPS 49</p> <p>Eigenvalue Analysis and Delay Impact 49</p> <p>Sensitivity Analysis of Network Parameters 49</p> <p>Optimal AGC 50</p> <p>Optimal Controller Performance 50</p> <p>Scenario 1 Analysis 51</p> <p>Scenario 2 Analysis 51</p> <p>Scenario 3 Analysis 52</p> <p>Scenario 4 Analysis 52</p> <p>Robustness of Optimal AGC 52</p> <p> </p> <p>Reliability Analysis of CPSs 65</p> <p>Conceptual Distributed Generation Systems (DGSs) 65</p> <p>Mathematical Model of Degraded Network 65</p> <p>Model of Transmission Delay 66</p> <p>Model of Packet Dropout 67</p> <p>Scenarios of Degraded Network 68</p> <p>Modeling and Simulation of DGSs 69</p> <p>DGS Model 69</p> <p>Preliminary Model 69</p> <p>Power Source Model 70</p> <p>Data Interpolation 71</p> <p>Reliability Estimation Via Optimal Power Flow (OPF) 71</p> <p>Data Prediction 71</p> <p>Monte Carlo Simulation (MCS) of DGSs 73</p> <p>OPF of DGSs 74</p> <p>Actual Cost and Reliability Analysis 75</p> <p>OPF of DGSs Against Unreliable Network 76</p> <p>Settings of Networked DGSs 76</p> <p>OPF Under Different Demand Levels 78</p> <p>OPF Under Entire Period 79</p> <p> </p> <p>Maintenance of Aging CPSs 87</p> <p>Data-driven Degradation Model for CPSs 88</p> <p>Degraded Control System 88</p> <p>Parameter Estimation via EM Algorithm 89</p> <p>Load Frequency Control (LFC) Performance Criteria 90</p> <p>Maintenance Model and Cost Model 91</p> <p>Performance Based Maintenance (PBM) Model 91</p> <p>Cost Model 93</p> <p>Applications to DGSs 94</p> <p>Output of Aging Generators 94</p> <p>Impact of Aging on DGSs 94</p> <p>Settings of Aging DGSs 94</p> <p>Validations of Generator Performance Indexes 95</p> <p>Quantitative Aging Impact 96</p> <p>Applications to Gas Turbine Plant 98</p> <p>Settings of Networked DGS Sensitivity Analysis of PBM 98</p> <p>Impact of Degradation on LFC 98</p> <p>Numerical Sensitivity Analysis 98</p> <p>Pictorial Sensitivity Analysis 99</p> <p>Optimal Maintenance Strategy 100</p> <p>Maintenance Models Comparison 100</p> <p> </p> <p>Game Theory Based CPS Protection Plan 109</p> <p>Vulnerability Model for CPSs 110</p> <p>Multi-state Attack-Defence Game 111</p> <p>Backgrounds of Game Model for CPSs 111</p> <p>Mathematical Game Model 112</p> <p>Attack Consequence and Optimal Defence 113</p> <p>Damage Cost Model 113</p> <p>Attack Uncertainty 114</p> <p>Optimal Defence Plan 115</p> <p>Applications to DGSs with Uncertain Cyber-Attacks 116</p> <p>Settings of Game Model 116</p> <p>Optimal Protection with Constant Resource Allocation 116</p> <p>Impact Under Constant Case 116</p> <p>Optimal Constant Resource Allocation Fraction 117</p> <p>Optimal Protection with Dynamic Resource Allocation 118</p> <p>Vulnerability Model Under Dynamic Case 119</p> <p>Optimal Dynamic Resource Allocation Fraction 120</p> <p>Optimization Results Justification 121</p> <p> </p> <p>Bayesian Based Cyberteam Deployment 125</p> <p>Poisson Distribution based Cyber-attacks 125</p> <p>Impacts of DoS Attack 125</p> <p>Poisson Arrival Model Verification 126</p> <p>Average Arrival Attacks 127</p> <p>Cost of Multi-node Bandit Model 128</p> <p>Regret Function of Worst Case 128</p> <p>Upper Bound on Cost 129</p> <p>Thompson-Hedge Algorithm 130</p> <p>Hedge Algorithm 130</p> <p>Details of Thompson-Hedge Algorithm 131</p> <p>Separation of Target Regret 132</p> <p>Upper Bound of Λ_1 133</p> <p>Upper Bound of Λ_2 133</p> <p>Upper Bound of Regret R^TH 134</p> <p>Applications to Smart Grids 135</p> <p>Operation Cost of Smart Grid 135</p> <p>Numerical Analysis of Cost Sequences 137</p> <p>Performance of Thompson-Hedge Algorithm 137</p> <p>Comparison Study Against R.EXP3 137</p> <p>Sensitivity to the Variation 140</p> <p> </p> <p>Recent Advances in CPS Modeling, Stability and Reliability 145</p> <p>Modeling Techniques for CPS Components 145</p> <p>Inverse Gaussian Process 145</p> <p>Hitting Time to a Curved Boundary 146</p> <p>Estimator Error 147</p> <p>Theoretical Stability Analysis 148</p> <p>Impacts of Uncertainties 148</p> <p>Small Gain Theorem based Stability Criteria 149</p> <p>Robust Stability Criteria 150</p> <p>Game Model for CPSs 151</p> <p> </p> <p>References 153</p> <p>Index 177</p>

<p><b>Huadong Mo, PhD,</b> is Senior Lecturer in the School of Engineering and Information Technology at the University of New South Wales. He received his doctorate from the City University of Hong Kong in the area of cyber-physical system reliability engineering. </p> <p><b>Giovanni Sansavini, PhD,</b> is Associate Professor at the Reliability and Risk Engineering Laboratory, Institute of Energy and Process Engineering, ETH Zurich, Switzerland. He is also the director of Reliability and Risk Engineering Laboratory, in the Institute of Energy and Process Engineering, Department of Mechanical and Process Engineering. He received his doctorate in nuclear engineering in 2010 from Politecnico di Milano, Italy, and a doctorate in engineering mechanics from Virginia Tech in Blacksburg in 2010. <p><b>Min Xie, PhD,</b> is Chair Professor of Industrial Engineering in the Department of Advanced Design and Systems Engineering, at City University of Hong Kong. He received his doctorate in Quality Technology in 1987 from Linkoping University in Sweden and was elected as a Fellow of the IEEE in 2006.

<p><b>Gather detailed knowledge and insights into cyber-physical systems behaviors from a cutting-edge reference written by leading voices in the field </b></p> <p>In <i>Cyber-Physical Distributed Systems: Modeling, Reliability Analysis and Applications</i>, distinguished researchers and authors Drs. Huadong Mo, Giovanni Sansavini, and Min Xie deliver a detailed exploration of the modeling and reliability analysis of cyber physical systems through applications in infrastructure and energy and power systems. The book focuses on the integrated modeling of systems that bring together physical and cyber elements and analyzing their stochastic behaviors and reliability with a view to controlling and managing them. <p>The book offers a comprehensive treatment on the aging process and corresponding online maintenance, network degradation, and cyber-attacks occurring in cyber-physical systems. The authors include many illustrative examples and case studies based on real-world systems and offer readers a rich set of references for further research and study. <p><i>Cyber-Physical Distributed Systems</i> covers recent advances in combinatorial models and algorithms for cyber-physical systems modeling and analysis. The book also includes: <ul><li>A general introduction to traditional physical/cyber systems, and the challenges, research trends, and opportunities for real cyber-physical systems applications that general readers will find interesting and useful</li> <li>Discussions of general modeling, assessment, verification, and optimization of industrial cyber-physical systems</li> <li>Explorations of stability analysis and enhancement of cyber-physical systems, including the integration of physical systems and open communication networks</li> <li>A detailed treatment of a system-of-systems framework for the reliability analysis and optimal maintenance of distributed systems with aging components</li></ul> <p>Perfect for undergraduate and graduate students in computer science, electrical engineering, cyber security, industrial and system engineering departments, <i>Cyber-Physical Distributed Systems</i> will also earn a place on the bookshelves of students taking courses related to reliability, risk and control engineering from a system perspective. Reliability, safety and industrial control professionals will also benefit greatly from this book.

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