Cyber-Physical Distributed Systems. Min Xie

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СКАЧАТЬ 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 [1,2]. 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 [4,5]. However, RBDs are incapable of describing the dynamic maintenance and repairable activities; thus, various dynamic modeling methods have been reviewed in [6,7]. 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 СКАЧАТЬ