Оглавление
Min Xie. Cyber-Physical Distributed Systems
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Cyber‐Physical Distributed Systems. Modeling, Reliability Analysis and Applications
Preface
Acronyms and Abbreviations
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
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
Note
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
Remark 3.1
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
Theorem 3.1
Theorem 3.2
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
Note
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
Example 4.1
Remark 4.1
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
Example 4.2
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
Note
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
Note
6 Game Theory Based CPS Protection Plan
6.1 Vulnerability Model for CPSs
Remark 6.1
Example 6.1
6.2 Multi‐state Attack‐Defence Game. 6.2.1 Backgrounds of Game Model for CPSs
Assumption 6.1
Assumption 6.2
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
Note
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
Assumption 7.1
7.3 Thompson‐Hedge Algorithm
7.3.1 Hedge Algorithm
Algorithm 7.1 Hedge (λ) Algorithm Initialization
Lemma 7.1
7.3.2 Details of Thompson‐Hedge Algorithm
Algorithm 7.2 Thompson‐Hedge Algorithm
Lemma 7.2
Theorem 7.1
7.3.2.1 Separation of Target Regret
7.3.2.2 Upper Bound ofΛ1
7.3.2.3 Upper Bound ofΛ2
7.3.2.4 Upper Bound of RegretRTH
Remark 7.1
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
Algorithm 7.3 Simulation for comparison
7.5.2 Sensitivity to the Variation
Note
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
Algorithm 8.1
8.1.3 Estimator Error
8.2 Theoretical Stability Analysis
8.2.1 Impacts of Uncertainties
Assumption 8.1
Assumption 8.2
8.2.2 Small Gain Theorem based Stability Criteria
Theorem 8.1
8.2.3 Robust Stability Criteria
Theorem 8.2
8.3 Game Model for CPSs
References
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