Cyber-Physical Distributed Systems

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

© 2021 John Wiley & Sons Ltd

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

The right of Huadong Mo, Giovanni Sansavini and Min Xie to be identified as the authors of this work has been asserted in accordance with law.

Registered Offices

John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA

John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

Editorial Office

The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats.

Limit of Liability/Disclaimer of Warranty

While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

Library of Congress Cataloging‐in‐Publication Data applied for

Hardback ISBN: 9781119682677

Cover Design: Wiley

Cover Image: © Wright Studio/Shutterstock

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.

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