Cyber-Physical Distributed Systems: Modeling, Reliability Analysis and Applications

Description

CYBER-PHYSICAL DISTRIBUTED SYSTEMS Gather detailed knowledge and insights into cyber-physical systems behaviors from a cutting-edge reference written by leading voices in the field In Cyber-Physical Distributed Systems: Modeling, Reliability Analysis and Applications, 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. The book offers a comprehensive treatment on the aging process and corresponding online maintenance, networked 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. Cyber-Physical Distributed Systems covers recent advances in combinatorial models and algorithms for cyber-physical systems modeling and analysis. The book also includes: 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 Discussions of general modeling, assessment, verification, and optimization of industrial cyber-physical systems Explorations of stability analysis and enhancement of cyber-physical systems, including the integration of physical systems and open communication networks A detailed treatment of a system-of-systems framework for the reliability analysis and optimal maintenance of distributed systems with aging components Perfect for undergraduate and graduate students in computer science, electrical engineering, cyber security, industrial and system engineering departments, Cyber-Physical Distributed Systems 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. Cover Title Page Copyright Page Contents Preface Acronyms and Abbreviations Chapter 1 Introduction 1.1 Challenges of Traditional Physical and Cyber Systems 1.2 Research Trends of CPSs 1.2.1 Stability of CPSs 1.2.2 Reliability of CPSs 1.3 Opportunities for CPS Applications 1.3.1 Managing Reliability and Feasibility of CPSs 1.3.2 Ensuring Cybersecurity of CPSs Chapter 2 Fundamentals of CPSs 2.1 Models for Exploring CPSs 2.1.1 Control-Block-Diagram for CPSs 2.1.1.1 Control Signal in CPSs 2.1.1.2 Degraded Actuator and Sensor 2.1.1.3 Time-Varying Model of CPSs 2.1.2 Implementation in TrueTime Simulator 2.1.2.1 Introduction of TrueTime Simulator 2.1.2.2 Architectures of CPSs in TrueTime 2.2 Evaluation and Verification of CPSs 2.2.1 CPS Performance Evaluation 2.2.1.1 CPS Performance Index 2.2.1.2 Reliability Evaluation of CPSs 2.2.2 CPS Model Verification 2.3 CPS Performance Improvement 2.3.1 PSO-Based Reliability Enhancement 2.3.2 Optimal PID-AGC Chapter 3 Stability Enhancement of CPSs 3.1 Integration of Physical and Cyber Models 3.1.1 Basics of WAPS 3.1.1.1 Physical Layer 3.1.1.2 Cyber Layer 3.1.1.3 WAPS Realized in TrueTime 3.1.2 An Illustrative WAPS 3.1.2.1 Illustrative Physical Layer 3.1.2.2 Illustrative Cyber Layer 3.1.2.3 Illustrative Integrated System 3.2 Settings of Stability Analysis 3.2.1 Settings for Delay Predictions 3.2.2 Settings for Illustrative WAPS 3.2.3 Cases for Illustrative WAPS 3.3 HMM-Based Stability Improvement 3.3.1 On-line Smith Predictor 3.3.1.1 Initialization of DHMM 3.3.1.2 Parameter Estimation of DHMM 3.3.1.3 Delay Prediction via DHMM 3.3.1.4 Smith Predictor Structure 3.3.2 Delay Predictions 3.3.2.1 Settings of DHMM 3.3.2.2 Prediction Comparison 3.3.3 Performance of Smith Predictor 3.3.3.1 Settings of Smith Predictor 3.3.3.2 Analysis of Case 1 3.3.3.3 Analysis of Case 2 3.4 Stability Enhancement of Illustrative WAPS 3.4.1 Eigenvalue Analysis and Delay Impact 3.4.2 Sensitivity Analysis of Network Parameters 3.4.3 Optimal AGC 3.4.3.1 Optimal Controller Performance 3.4.3.2 Scenario 1 Analysis 3.4.3.3 Scenario 2 Analysis 3.4.3.4 Scenario 3 Analysis 3.4.3.5 Scenario 4 Analysis 3.4.3.6 Robustness of Optimal AGC Chapter 4 Reliability Analysis of CPSs 4.1 Conceptual DGSs 4.2 Mathematical Model of Degraded Network 4.2.1 Model of Transmission Delay 4.2.2 Model of Packet Dropout 4.2.3 Scenarios of Degraded Network 4.3 Modeling and Simulation of DGSs 4.3.1 DGS Model 4.3.1.1 Preliminary Model 4.3.1.2 Power Source Model 4.3.2 Data Interpolation 4.4 Reliability Estimation Via OPF 4.4.1 Data Prediction 4.4.2 MCS of DGSs 4.4.3 OPF of DGSs 4.4.4 Actual Cost and Reliability Analysis 4.5 OPF of DGSs Against Unreliable Network 4.5.1 Settings of Networked DGSs 4.5.2 OPF Under Different Demand Levels 4.5.3 OPF Under Entire Period Chapter 5 Maintenance of Aging CPSs 5.1 Data-driven Degradation Model for CPSs 5.1.1 Degraded Control System 5.1.2 Parameter Estimation via EM Algorithm 5.1.3 LFC Performance Criteria 5.2 Maintenance Model and Cost Model 5.2.1 PBM Model 5.2.2 Cost Model 5.3 Applications to DGSs 5.3.1 Output of Aging Generators 5.3.2 Impact of Aging on DGSs 5.3.2.1 Settings of Aging DGSs 5.3.2.2 Validations of Generator Performance Indexes 5.3.2.3 Quantitative Aging Impact 5.4 Applications to Gas Turbine Plant 5.4.1 Sensitivity Analysis of PBM 5.4.1.1 Impact of Degradation on LFC 5.4.1.2 Numerical Sensitivity Analysis 5.4.1.3 Pictorial Sensitivity Analysis 5.4.2 Optimal Maintenance Strategy 5.4.3 Maintenance Models Comparison Chapter 6 Game Theory Based CPS Protection Plan 6.1 Vulnerability Model for CPSs 6.2 Multi-state Attack-Defence Game 6.2.1 Backgrounds of Game Model for CPSs 6.2.2 Mathematical Game Model 6.3 Attack Consequence and Optimal Defence 6.3.1 Damage Cost Model 6.3.2 Attack Uncertainty 6.3.3 Optimal Defence Plan 6.4 Applications to Distributed Generation Systems (DGSs) with Uncertain Cyber-attacks 6.4.1 Settings of Game Model 6.4.2 Optimal Protection with Constant Resource Allocation 6.4.2.1 Impact Under Constant Case 6.4.2.2 Optimal Constant Resource Allocation Fraction 6.4.3 Optimal Protection with Dynamic Resource Allocation 6.4.3.1 Vulnerability Model Under Dynamic Case 6.4.3.2 Optimal Dynamic Resource Allocation Fraction 6.4.3.3 Optimization Results Justification Chapter 7 Bayesian Based Cyberteam Deployment 7.1 Poisson Distribution based Cyber-attacks 7.1.1 Impacts of DoS Attack 7.1.2 Poisson Arrival Model Verification 7.1.3 Average Arrival Attacks 7.2 Cost of MNB Model 7.2.1 Regret Function of Worst Case 7.2.2 Upper Bound on Cost 7.3 Thompson-Hedge Algorithm 7.3.1 Hedge Algorithm 7.3.2 Details of Thompson-Hedge Algorithm 7.3.2.1 Separation of Target Regret 7.3.2.2 Upper Bound of .1 7.3.2.3 Upper Bound of .2 7.3.2.4 Upper Bound of Regret RTH 7.4 Applications to Smart Grids 7.4.1 Operation Cost of Smart Grids 7.4.2 Numerical Analysis of Cost Sequences 7.5 Performance of Thompson-Hedge Algorithm 7.5.1 Comparison Study Against R.EXP3 7.5.2 Sensitivity to the Variation Chapter 8 Recent Advances in CPS Modeling, Stability and Reliability 8.1 Modeling Techniques for CPS Components 8.1.1 Inverse Gaussian Process 8.1.2 Hitting Time to a Curved Boundary 8.1.3 Estimator Error 8.2 Theoretical Stability Analysis 8.2.1 Impacts of Uncertainties 8.2.2 Small Gain Theorem based Stability Criteria 8.2.3 Robust Stability Criteria 8.3 Game Model for CPSs References Index EULA