TOWARDS RESILIENT SMART GRIDS: ROBUST CONTROL...

TOWARDS RESILIENT SMART GRIDS: ROBUST CONTROL FRAMEWORK DESIGNTO ENHANCE TRANSIENT STABILITY

By

MUHARREM AYAR

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOLOF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2017

c© 2017 Muharrem Ayar

2

To my lovely wife Ozlem, my beloved son Omer Selim and my parents

3

ACKNOWLEDGMENTS

I would like to express my deepest respect and most sincere gratitude to my

advisor, Dr. Haniph A. Latchman, for his encouragement, guidance, and support. He

did not spare any effort to improve my research and teaching skills. His constructive

criticism and invaluable suggestions throughout my graduate study have helped my

research and enabled me to develop a better understanding of the subject. I am

extremely privileged to have been a student under his supervision. I hope that I could

become as good an advisor to my students as Dr. Latchman in the future.

I extend my gratitude to my co-advisor, Dr. Janise McNair, for her guidance and

support. She also assisted me greatly to improve my research and complete my

doctoral dissertation. She is a dedicated advisor, and I have benefited tremendously

from having been a student in her research group.

I also express my appreciation to Dr. Arturo Bretas for his extraordinary guidance

and support to improve my doctoral research. Also, I want to thank Dr. Norman Fitz-Coy

for accepting to serve on my dissertation committee and his recommendations.

A very special appreciation to my friend Dr. Serhat Obuz for his collaboration and

fruitful discussions we held throughout my research, and for his contributions to the

success of our joint research work.

Finally, and most importantly, I would like to thank my family. My parents have been

undeniably my pillar of strength and a constant source of motivation. Their prayers

for me were what sustained me hereto. I cannot thank my lovely wife enough; without

her understanding, patience, and support, I would not have been able to continue and

succeed. My beloved son, your innocence was my fuel and your smile was my remedy.

Special thanks also to my sisters, for their prayers for me.

4

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

CHAPTER

1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.1 Challenges in Smart Grids . . . . . . . . . . . . . . . . . . . . . . . . . . . 171.1.1 Cyber-Physical Security . . . . . . . . . . . . . . . . . . . . . . . . 171.1.2 Stability and Resiliency . . . . . . . . . . . . . . . . . . . . . . . . . 191.1.3 Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.2 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231.3 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241.4 Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2 SMART GRID STABILITY AND CONTROL . . . . . . . . . . . . . . . . . . . . 26

2.1 Smart Grid Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.1.1 Rotor Angle Stability . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.1.1.1 Transient Stability . . . . . . . . . . . . . . . . . . . . . . 282.1.1.2 Small-Signal Stability . . . . . . . . . . . . . . . . . . . . 30

2.1.2 Frequency Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.1.3 Voltage Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.2 Controls for Smart Grid Transient Stability . . . . . . . . . . . . . . . . . . 322.2.1 Decentralized Control . . . . . . . . . . . . . . . . . . . . . . . . . . 332.2.2 Centralized Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.2.3 Distributed Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3 A ROBUST DECENTRALIZED CONTROL FRAMEWORK WITH CONSTANTTIME DELAY COMPENSATION . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.1 Dynamic Model of Synchronous Machines . . . . . . . . . . . . . . . . . 403.2 Robust Nonlinear Controller Design . . . . . . . . . . . . . . . . . . . . . . 413.3 Lyapunov Stability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 443.4 Simulation and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5

4 CYBER-ENABLED DECENTRALIZED CONTROL FRAMEWORK WITH UN-KNOWN TIME-VARYING INPUT DELAY COMPENSATION . . . . . . . . . . . 53

4.1 Dynamic Model and Properties of Synchronous Machines . . . . . . . . . 564.2 Control Objective and Development . . . . . . . . . . . . . . . . . . . . . 584.3 Stability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.4 State Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644.5 Simulation and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5 A DISTRIBUTED NONLINEAR CONTROL STRATEGY FOR ENHANCINGSMART GRID TRANSIENT STABILITY . . . . . . . . . . . . . . . . . . . . . . 74

5.1 Distributed Control Framework . . . . . . . . . . . . . . . . . . . . . . . . 755.1.1 Dynamic Model of Synchronous Machines . . . . . . . . . . . . . . 775.1.2 Control Development . . . . . . . . . . . . . . . . . . . . . . . . . . 785.1.3 Lyapunov Stability Analysis . . . . . . . . . . . . . . . . . . . . . . 82

5.2 Simulation and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 865.3 Case Study under Practical Limitations . . . . . . . . . . . . . . . . . . . . 885.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

6 AN ADAPTIVE MAC PROTOCOL DESIGN FOR SMART GRID HOME AREANETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

6.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 966.2 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 986.3 HomePlug/IEEE 1901 CSMA/CA MAC Protocol . . . . . . . . . . . . . . . 99

6.3.1 Backoff Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . 996.3.2 Priority Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

6.4 Adaptive Contention Window based CSMA/CA MAC . . . . . . . . . . . . 1016.5 Performance Evaluation with Prioritized Traffic . . . . . . . . . . . . . . . . 1076.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

7 CONCLUSIONS AND FUTURE WORK . . . . . . . . . . . . . . . . . . . . . . 113

7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

7.2.1 A Cross-Layer Strategy for Cyber-Physical Security of Smart Grids 1157.2.2 Saturated Robust Controller . . . . . . . . . . . . . . . . . . . . . . 117

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

BIOGRAPHICAL SKETCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

6

LIST OF TABLES

Table page

3-1 Decentralized controller gain settings for known constant time delay case. . . . 48

4-1 Adjusted controller gain values for unknown time-varying constant time delaycase. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

5-1 Distributed controller gain settings. . . . . . . . . . . . . . . . . . . . . . . . . 86

5-2 Default simulation parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

6-1 Contention resolution parameters of the standard HomePlug MAC protocol. . . 99

6-2 HomePlug 1.0 MAC parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . 107

7

LIST OF FIGURES

Figure page

1-1 Evolution of power girds: Past, Today and Future. . . . . . . . . . . . . . . . . . 14

1-2 A satellite image of the Northeastern cities in US during 2003 blackout. . . . . 16

1-3 An overview of smart grid. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1-4 An overview of smart grid communication networks. . . . . . . . . . . . . . . . 21

2-1 Classification of power system stability . . . . . . . . . . . . . . . . . . . . . . . 27

2-2 The dynamic model of synchronous machines. . . . . . . . . . . . . . . . . . . 29

3-1 An overview of decentralized control framework for transient stability. . . . . . 39

3-2 Rotor angle and speed deviation of synchronous machines during and afterthree phase fault. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3-3 Stabilization time versus varying DESS capacity and time delay. . . . . . . . . 49

3-4 Stabilization time versus clearing time. . . . . . . . . . . . . . . . . . . . . . . 50

3-5 Stabilization time versus additive time-varying disturbance. . . . . . . . . . . . 51

4-1 PMUs and synchrophasor data flows in the North American power grid. . . . . 54

4-2 Data flow of the proposed cyber-physical control strategy. . . . . . . . . . . . . 55

4-3 Rotor speed and angle deviation of synchronous machines during and afterthree phase fault. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4-4 Representation of varying actual time delay and its constant estimate. . . . . . 70

4-5 Comparative simulation results under practical limitations. . . . . . . . . . . . . 72

5-1 An overview of designed distributed control framework for smart grids. . . . . . 75

5-2 Rotor angle deviation and rotor speed oscillation over time during and afterthree-phase fault inception. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

5-3 Stabilization performance of the distributed controller under practical limitations. 89

6-1 The MAC throughput comparison of wireless (IEEE 802.11) and wired(HomePlug) networking technologies . . . . . . . . . . . . . . . . . . . . . . . 96

6-2 Operational flow chart of HomePlug/IEEE 1901 CSMA/CA MAC protocol. . . . 100

6-3 Backoff resolution of HomePlug/IEEE 1901 CSMA/CA MAC protocol. . . . . . 101

8

6-4 Markov chain model for adaptive contention window based CSMA/CA MACprotocol. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

6-5 Numeric and analytic solution for po . . . . . . . . . . . . . . . . . . . . . . . . 104

6-6 Estimating number of contending nodes over a changing network traffic. . . . 106

6-7 Simulation results to evaluate MAC efficiency of the proposed MAC protocolwhen the number of nodes is given and estimated . . . . . . . . . . . . . . . . . 106

6-8 Case study to compare the MAC efficiency of the designed controller with thestandardized HomePlug MAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

6-9 Case study to compare the channel access delay performance of the de-signed controller with the standardized HomePlug MAC. . . . . . . . . . . . . . 110

7-1 Cross-layer cyber-physical security framework for smart grid. . . . . . . . . . . 116

9

Abstract of Dissertation Presented to the Graduate Schoolof the University of Florida in Partial Fulfillment of theRequirements for the Degree of Doctor of Philosophy

TOWARDS RESILIENT SMART GRIDS: ROBUST CONTROL FRAMEWORK DESIGNTO ENHANCE TRANSIENT STABILITY

By

Muharrem Ayar

August 2017

Chair: Haniph A. LatcmanCochair: Janise McNairMajor: Electrical and Computer Engineering

Smart grids use digital communications, signal processing, sensing, and control

systems technologies to enhance the efficiency, reliability, and security of electrical

power systems and contribute to the sustainability of energy with the large penetration

of renewable energy sources. Increasing integration of the cutting-edge digital technolo-

gies has become the source of new challenges such as cybersecurity and uncertainties,

while at the same time being the cornerstone of new opportunities such as real-time

control and monitoring systems to enhance the power system stability.

Maintaining the stability of power systems has become an even more challenging

problem with the increasing complexity by enabling two-way power flow through active

loads and energy storage systems. Fortunately, making real-time data exchange

between sensors and control units possible through communication networks allows

designing advanced control systems. However, communication delay which is inherent

in communication systems limits the capacity of the control systems and introduce

uncertainty to the system. On the other hand, cyber-attacks to the various segments of

smart grids threat the stability and security of power systems by leading to malfunction

of underlying protection and control systems.

In today’s world, isolating systems by deploying private networks cannot entirely

protect them from cyber-attacks and penetration. The Stuxnet case, for example, led to

10

the malfunctioning of control systems in Iranian’s nuclear power station, despite being

physically isolated from the global network. Therefore, addressing various form of cyber-

attacks requires defense-in-depth strategies whereby enabling multiple layers of security

in the underlying communication and control systems.

This dissertation presents distributed and decentralized robust control frameworks

that aim at enhancing the transient stability and resiliency of smart grids in the face

of cyber-physical disturbances by employing phasor measurement units (PMU) and

distributed energy storage systems (DESS). Robust controllers introduce a novel time

delay compensation technique to mitigate the effect of communication and control input

time delay. In addition, uncertainties arising from varying plant parameters and errors in

sensor measurements are considered in the robust controller design. Furthermore, the

communication delay is addressed in network layer by developing an adaptive medium

access control (MAC) protocol to reduce the channel access delay and improve the data

throughput with respect to increasing numbers of connected intelligent devices.

The success of robust controllers is proven by conducting several comparative

case studies based on IEEE 39 bus 10 machines test power system. The enhanced

resiliency to the large time delay and uncertainties highlights the success of robust

controllers in comparison to the state-of-the-art controllers. To illustrate, while the para-

metric feedback linearization (PFL) controller can tolerate up to 160ms communication

delay, the designed distributed controller can stabilize a perturbed power system even in

the existence of 1s communication delay. Similarly, comparisons between decentralized

controller and the well-known multi-band power system stabilizer (MB-PSS) controller

shows that decentralized controller can reduce the stabilization time by 80% when the

delay is known and by 60% if the delay is unknown with respect to the MB-PSS. It is

evident that the robust controller owes this success to the designed novel time delay

compensation technique. Moreover, the simulation results with respect to uncertainty in

sensor measurements show that the robust controller is resilient up to 10% deviation.

11

Whereas, the measurement uncertainty affect the stabilization performance of the PFL

controller significantly.

In addition, the designed MAC protocol is tested in a home area network (HAN)

which hosts large numbers of nodes with various applications. Simulation results

demonstrate that an 80% MAC efficiency is maintained for up to 100 nodes while the

efficiency of standardized HomePlug MAC protocol, for example, reduces down to 10%.

Similarly, an 80% reduction in channel access delay is achieved by the adaptive MAC

protocol with respect to the HomePlug MAC protocol. It is asserted that tailoring the

adaptive MAC protocol for other communication networks such as neighborhood area

network (NAN) and wide area networks (WAN) can reduce the channel access delay

and improve data throughput.

12

CHAPTER 1INTRODUCTION

Electricity is one of the ground-breaking discoveries of the 19th century that has

made a great impact on the socioeconomic development of nations. Reliable delivery

of electricity plays a crucial role in nation’s economy, security, and even in the health

and safety. However, the current electric infrastructures are aging and they are being

pushed to do more than they were originally designed to do. Fundamental changes in

both demand and supply side cause electrical power system to face increasing stress.

On the demand side, increasing world energy consumption has been forcing

capacity of electrical power systems to work on their physical limits. The International

Energy Agency has recently released 2016 World Energy Outlook Report, that projects

30% rise in world energy consumption by 2040 [1]. Similarly, the International Energy

Outlook 2016 (IEO2016) of the U.S. Energy Information Administration estimates that

the energy consumption will grow by 48% between 2012 and 2040 [2]. In order to

meet the current energy demand and preparing the power system for future, advanced

technologies such as demand response (DR) and smart metering have been used to

manage energy consumption and generation.

On the supply side, concerns about energy security and sustainability, environ-

mental effects of fossil fuel emission, and sustained long-term energy prices have led

a shift from bulk energy generation to distributed generation by increasingly integrating

renewable energy sources (RES) which are abundant in the form of solar, hydro, wind,

geothermal, and biomass. According to the IEO2016, harvesting energy from renew-

ables is growing at a rapid pace all over the world. Since renewables are eco-friendly

and economic, many countries including the United States have been promoting green

energy generation by adopting new energy policies and incentives [3]. In the United

States, energy generation from renewables in the form of wind, solar, and geothermal

has been doubled since 2008 and 20% growth has been targeted by 2020 [4]. China

13

Figure 1-1. Evolution of power girds: Past, Today and Future [7].

targeted to achieve 20% renewable by 2020 [3] and more ambitiously set the target of

86% renewable by 2050 [5]. Similarly, European Commission asserted 20% renewable

target of Europe by 2020 [6].

The conventional power system architecture was based on bulk energy generation

remotely located from consumers, hierarchical control systems with minimal feedback,

and passive loads. The capacity, flexibility, reliability, and security of the traditional sys-

tem is far more behind the need for meeting the emerging trends, such as increasing

integration of relatively low inertia generation source, large penetration of distributed

generation, and the need for better resiliency. It is expected from the today’s electrical

grids that dynamically optimize operation and sources, integrate diverse generation

sources, integrate demand response, quickly detect and mitigate disturbances and

provide robust protection against cyber-physical threats. Incorporating all these fea-

tures transforms the conventional electric grids to the future’s modern smart grids as

illustrated in Figure 1-1.

The targeted benefits from the smart grids includes:

• Increasing integration of renewable energy systems,

• Improving the efficiency of energy transmission,

• Engaging end-users in power systems management,

• Optimizing operation and management costs,

14

• Quicker restoration or self-healing power systems after a disturbance,

• Enhancing the security.

The modernization of the traditional power systems requires the use of cutting-edge

technologies such as advanced metering infrastructures (AMI), wireless and fiber optic

networks, and energy storage systems and advanced control that communicate and

collaborate to enable safe and reliable two-way electricity and data flow. Enabling

advanced technologies can greatly decrease the frequency and duration of power

outages, reduces the impact of natural disasters, and accelerates the restoration

service [2,8].

Grid modernization must encompass the technologies that offer cybersecurity pro-

tections and innovative control system architectures. Smart grids are one of the critical

infrastructures of the nations and must be protected well to avoid major blackouts which

may cause socioeconomic disasters by damaging and even worse seizing the operation

of banks, telecommunication, traffic, etc. A smarter grid should, therefore, enhance the

resiliency of power systems in order to prepare them to address emergencies such as

natural disasters and terrorist attacks.

In addition to the resiliency of electrical grids, power system stability is vital for

the reliable and sustainable operation of electrical grids that is maintained by enabling

centralized, decentralized or distributed control architectures. Traditional control systems

with minimal local feedback suffer from lack of situational awareness and the need for

human intervention. The Northwestern blackouts in 2003 [9] and the Italy blackout in

2003 [10], for example, were the result of a cascading failure that occurred very fast

due to the lack of advanced automation and global feedback for control. These two

unhindered major blackouts affected over 50 million people and over 400 generators and

caused a considerable amount of economic loss [11]. In the Figure 1-2, a satellite image

of the US Northeastern cities in 2003 illustrates the blackout.

15

Figure 1-2. A satellite image of the Northeastern cities in US during 2003 blackout.

The two-way interactive communication capacity of the smart grids equipment

allows advanced digital control architectures with a real-time global feedback to en-

hance the stability and resiliency of electrical power grids. Current power systems has

been relatively interacted with information and communication technologies and keep

increasing interaction and to support automation, protection and control application as

depicted in Figure 1-3. In today’s smart grid, wide-area control systems (WACS) are

employed throughout generation and transmission systems. However, their centralized

architecture emerges several challenges such as increasing communication traffic and

data volume with large penetration of advanced sensors, and cybersecurity risk due to

their dependence on a central controller. Thus, the current trend for future smart grid

control systems shifts to distributed architectures rather than centralized ones.

The cybersecurity of smart grids is paramount for national security and need to be

investigated and treated carefully. The National Institute of Standards and Technology

(NIST) advocates that cybersecurity of electrical power grids requires a defense-in-

depth strategies by enabling multiple security throughout communication, protection

and control systems. In this dissertation, several control frameworks that take in

consideration of cyber-physical disturbances to enhance the transient stability are

presented. Moreover, a communication protocol is presented that aims at improving the

data throughput and the channel access delay for smart grid application within home

16

Figure 1-3. An overview of smart grid [8].

area networks. An adaptive medium access control (MAC) protocol has been designed

to leverage the efficiency of highly populated networks.

The major contributions of this dissertation includes:

• Decentralized and distributed control frameworks design,

• Developing model-free based robust nonlinear controllers,

• Implementing novel time delay compensation techniques to overcome effects ofknown and unknown time-varying delay,

• Using distributed energy storage systems (DESS) for transient stability enhance-ment,

• Designing an adaptive MAC protocol for smart grid home-area network.

1.1 Challenges in Smart Grids

1.1.1 Cyber-Physical Security

Electrical energy is the major driver of social and economic dynamic of nations and

thus securing the energy is critical. It is evident as the US has designated electrical

power system as one of the top 16 critical infrastructures [12]. Concerns about the

security of electrical grids have raised recently because of increasingly connecting

grid equipment to communication networks that provides multiple entry points for

the intrusion of adversary. Cyber-physical security, thus, has become one of the top

17

priorities of nations and many initiatives and research activities have been supported by

governments. The US government, for example, allocated $14 billion budget for funding

cybersecurity research and initiatives in FY 2016 [13].

Smart grids are one of the critical cyber-physical architectures comprised of a

broad range of technologies implemented throughout the generation, transmission,

and distribution systems that are owned and regulated by numerous owners. The large

diversity in incorporated technological equipment and varying regional regulations

across the countries requires consensus to address cyber-physical security. In the

US, for example, several organizations including the Department of Energy (DoE), The

National Electric Sector Cybersecurity Organization (NESCO), the National Institute of

Standards (NIST), and the Nort American Electric Reliability Corporation (NERC) take

on a mission with regard to the electrical power sector cybersecurity and resiliency.

The NIST advocates that cybersecurity of intelligent electrical grids need defense-

in-depth strategies that utilize multiple layer security throughout the communication,

protection and restoring systems [14]. Adaptive and dynamic nature of cyber-attacks

threatens the various layer of power systems. The Stuxnet, for example, is a virus first

identified in 2010 that caused a substantial damage in Iran’s nuclear program [15]. This

particular malware does not have a domain-specific design and an architecture which

could be tailored as a platform for attacking modern supervisory control and data ac-

quisition (SCADA) and PLC systems that are highly utilized in power system protection

and control. It is evident that boosting only the cybersecurity of communication system

or isolating SCADA systems from global networks may not be enough to protect smart

grids as it was experienced by the Stuxnet that was introduced to the systems via a USB

drive. Because of these reasons, cybersecurity needs to be considered throughout the

entire grid operations and functions including communication, protection and control

systems.

18

Electrical power grids are highly interconnected and a local failure or a cyber-

attack, which can lead a cascaded fault that may result in major blackouts. As an

example, Ukrainian blackout in 2015 caused by a cyber-attack to a substation and

culminated with a major power outage that affected over 225,000 people in Ukraine [16].

Quadrennial Energy Review report of US Department of Energy reveals the significance

of smart grid cybersecurity by stating that “electricity system faces imminent danger

from cyber-attacks” [17]. To draw attention towards cybersecurity of smart grid, many

warnings [18–20] and some guidelines have been published, such as NISTIR 7628 [14]

and NIST SP 1108 [21].

Since there is no single security measure that could counter all types of threats in

smart grids, there is a need for cross-layer security solutions among communication,

protection and control systems as suggested by the NIST. Communications systems

need to be leveraged to detect and protect grid equipment from intrusions, malicious

data injection, and intentionally occupation to increase latency. If communication

layer fails, protection systems such as state estimation (SE) must be able to detect

and correct malicious parameters to prevent systems from malfunctioning because of

disturbances. On the other hand, control systems must be resilient to either failure of

communication and protection system or inability to provide a totally reliable feedback

to maintain the stability of systems or recovering the system that subjected to a major

disturbance.

1.1.2 Stability and Resiliency

Smart grid must address not only deliberate cyber-attacks but also inadvertent

compromises of utilized cyber-physical infrastructures due to user errors, equipment

failures and natural disasters to maintain the stability of systems. The power systems

stability has always been a major concern for sustaining their reliable and secure oper-

ation. However, increasing integration of low inertia generations from renewables, large

penetration of distributed energy generations at the distribution level, and introducing

19

active loads in electrical grid to enable bi-directional power flow have fueled the power

system instability problems.

In conventional electrical power systems, stability has been treated by conducting

local control actions with a minimal feedback signals from associated equipment.

The emerging instability problem with the increasing complexity in power system

environment and growing interdependency between cyber and physical components

reveal the need for advanced robust control system architectures. Fortunately, the

ability of information technology to enable real-time two-way communication between

widely dispersed sensors such as PMU and control systems provides an opportunity to

design real-time monitoring and control systems such as wide-area monitoring systems

(WAMS) and wide-area control systems (WACS).

The WACS proposes a centralized control architecture that aggregates system

information at a central point via remotely installed sensors, calculate control signals

and actuates remote devices in the case of disturbances. The increased visibility of grid

through distributed sensors helps control systems to command the entire grid effectively.

However, concerns about scalability and security of centralized control architectures

have been arising because of the increasing communication burden and security risk

of being dependent on a single control center. Emerging uncertainties that may stem

from the communication delay and the loss or the failure of sensors contribute to the

instability of power systems.

On the other hand, the coexistence of a variety of low inertia energy generators with

bulk energy generators adversely affect the physical dynamics of the electrical power

system [22]. Distributed generation units such as inverter-connected wind turbines and

PV, which do not have rotational inertia, have been effectively displacing the rotating

conventional generators. This transformation results in changes in electromechanical

20

dynamics of synchronous machines and affects the power system stability [23]. More-

over, the fast frequency dynamics with low rotational inertia make frequency control and

power system stability even more challenging.

Taking into account all these challenges, new robust control architectures are

explicitly required to enhance the stability and resiliency power systems in the face of

cyber-physical disturbances. It is concluded that controller that is required must be able

to withstand to uncertainties that arise from cyber treats and compromises of the cyber

and physical infrastructures, and changing nonlinear dynamics coupled with reducing

overall rotational inertia.

1.1.3 Communication

Figure 1-4. An overview of smart grid communication networks [24].

Communication technologies are the key enablers of the smart grid applications

such as demand response, real-time monitoring, automation, and control, etc. Smart

grid communication networks (SGCN) are typically comprised of several segments,

each of which operates within a specific region of electrical power systems to ensure

information and control message exchange among entities. The communication

characteristics of these segments differ from each other in terms of operation region,

supported application, delay, and bandwidth requirements. In general, the SGNC are

decomposed into three representative networks that are home area networks (HAN),

21

neighborhood area networks (NAN) and wide area networks (WAN), as illustrated in

Figure 1-4.

Home Area Networks (HAN)

A HAN is utilized in residential dwellings to support a range of applications such as

smart meters, smart appliances, charging and control of Plug-in Hybrid Electric Vehicles

(PHEVs), and IP multimedia. Typically, HANs need to cover areas of up to 200m2.

Heterogeneity is the biggest challenge in HAN because of different needs of supported

applications such as various data rate and delay requirements. HANs tailor wireless,

wired or hybrid networking technologies such as PLC, Zigbee and Wi-Fi to support

smart applications within the premises.

Neighborhood Area Networks (NAN)

The NAN enables information exchange between a cluster of smart meters at

customer premises and utility company’s WANs. The population of each NAN varies

from a few hundred to a few thousand of smart meters depending on the power grid

topology and the deployed communications technology and protocol. The required

data rate varies depending on deployed applications. While a simple demand meter

reading application only needs a few bps, supporting sophisticated applications such

as advanced distribution automation, fault detection and restoration may require

higher data rates, e.g., a few tens of kbps per meter. It is worth to note that the NAN

is responsible for transporting a huge volume of different types of data and distributing

control signals between utility companies and a large number of devices installed at

customer premises [25]. The NAN can be formed by using power line communication

(PLC), Wi-Fi and cellular technologies.

Wide Area Network (WAN)

WAN is responsible for long-haul communications such as delivering aggregated

data of multiple NANs to utility company’s private networks and ensuring data exchange

among different data concentrators of power generation plants, distributed energy

22

resource stations, transmission and distribution grids, control centers, etc [25]. The

WAN may cover a very large area, i.e., thousands of square kilometers and could

aggregate a large number of supported devices and thus require hundreds of megabits

per second (Mbps) of data transmission. WANs are usually enabled by using cellular

networking technology such as WiMaX, 3G, and LTE and fiber optical solutions for

the given long distances among entities. Installation cost of fiber optical networks

and operational cost of cellular network emerge as a problem from utility & business

perspective.

The seamless collaboration of all these segments reveals the need for interop-

erability of various communications technologies to coexist and to meet the require-

ments of each applications in terms of data rates, communications latency, deploy-

ment/maintenance costs. The Electric Power Research Institute (EPRI) and the National

Institute of Standards and Technology (NIST) emphasize that communication between

each component in the smart grid is extremely important to secure the energy genera-

tion and consumption in an efficient way [26].

Currently standardized wireless (e.g., cellular, satellite, microwave, WiMAX, WiFi,

and Zigbee) and wired (e.g, copper cable, fiber optic cable, and power line carrier)

network technologies have been designed specifically for internet communication but

tailored for SGCN. It must be noted that, the internet and smart grid communication

networks pose fundamental differences in terms of bandwidth, latency, and security

requirements. While, the data rate and the fairness, for example, are the key metrics

for internet communication, latency requirements are more stringent in smart grids. For

instance, load shedding for under frequency has a delay allowance of only 10 ms [25].

1.2 Research Motivation

This dissertation looks for answers for following questions arises in smart grid

control and communications:

23

1. How to enhance the resiliency of smart grids by addressing transient stability

problem?

2. How to address the negative influence of large penetration of distributed genera-

tions?

3. How to mitigate the effect of uncertainties such as delay, erroneous sensor

measurement, and varying plant parameters in control systems?

4. How to improve the channel access delay and throughput performance of the

smart grid communication networks at the medium access control layer?

1.3 Research Contributions

The contributions of the research can be summarized as follows:

1. A decentralized control framework has been developed to enhance the transient

stability margin of synchronous generators (SG) when a large disturbance such

as loss of large loads or generators occurs. The designed control framework

employs PMU to obtain local measurements and DESS to provide external power

for damping the frequency oscillation quickly when SG are perturbed. A model-

free based nonlinear robust controller with a novel constant and known input

delay compensation technique has been developed and the proposed framework

has been validated on IEEE 39 bus 10 machine test power system via Matlab-

Simulink.

2. The delay compensation technique applied to decentralized control framework

has been leveraged to take care of unknown time-varying delay. Also, the control

framework has been boosted by enabling state estimation function to filter out the

PMU measurement before entering the controller.

3. A distributed control framework has been designed to enhance the transient

stability of smart grids by enabling situational awareness. In addition to the local

measurements, remote data from adjacent buses are considered to calculate

control signal. In contrast to the centralized control architectures such as WACS,

24

the distributed control framework reduces the communication burden while

partially maintaining situational awareness. Also, the distributed controller is

advantageous over WACS in terms of cybersecurity. For example, a cyber-attack

to the centralized controller may result in failure of the entire system, however, a

cyber-attack to a distributed controller can only effect a particular area in the power

grid and this area can be isolated to prevent the system from cascaded failures.

4. An adaptive medium access control protocol for smart grid home area networks

have been developed to enhance the throughput and delay performance for the

highly congested network. The increasing traffic throughout network might be

because of either high connectivity or cyber attacks such as denial-of-service.

The proposed MAC protocol maintains the throughput and delay characteristics

of the network even for highly congested networks. Analytical and numerical

studies demonstrate the success of the proposed MAC protocol with respect to the

state-of-art models.

1.4 Dissertation Organization

The rest of the dissertation is organized as follows. Chapter 2 presents a detailed

review on power systems stability and control systems. In Chapter 3, a decentralized

control framework has been introduced to address the transient stability issue of smart

grids. In Chapter 4, the proposed decentralized controller has been extended by

considering cyber-physical limitations and developing a new nonlinear robust controller

to compensate for unknown time-varying time delay. In Chapter 5, a distributed control

framework has been developed to enhance the stability and resiliency of synchronous

generators. In Chapter 6, an adaptive channel access protocol is presented that has

been developed to enhance the throughput and delay performance of HAN. The

conclusion and final remarks are given in Chapter 6.

25

CHAPTER 2SMART GRID STABILITY AND CONTROL

In the conventional power systems, generally organized as generation, transmis-

sion, distribution and end-users (customers), the electrical power is generated through

bulk energy sources that are located far away from end-users and first transformed to

high voltage (HV) by step-up transformers before transmitting through long distances. At

the distribution side, the high voltage is reduced to medium voltage (MV) or low voltage

(LV) to provide electrical power to the residential customers and industry.

The smart grid has drastically changed the classical power systems by enabling

two-way power and communication data flow with the increasing integration of dis-

tributed generations (DG) and advanced digital technologies. The large penetration of

DG distribution system is shifting distribution systems from being a passive network,

containing only loads, to an active network. However, the intermittent and inertia-less

nature of distributed energy sources such as wind power, photo voltaic (PV) impacts the

stability of the power systems. Furthermore, equipping the power system components

with intelligent electronic devices to connect them to data network raises cybersecurity

issues besides their advantageous in terms of enhancing operation and management

power systems.

This chapter investigates the existing stability problems in smart grids and dis-

cusses control systems that have been developed for addressing the instability of power

systems. Furthermore, contributions of the state-of-the-art controllers to the resiliency

and the cyber-physical security of the smart grid are studied and the existing problems

that are remained open are presented.

2.1 Smart Grid Stability

Stability of power systems has been considered as critical for secure and reliable

operation of system since 1920s [27] and has been defined by IEEE/CIGRE Joint Task

26

Figure 2-1. Classification of power system stability .

Force in [28] as the ability of electrical power systems to regain a state of operation point

after being subjected to a physical disturbance.

Instability of smart grids is a complex problem as it can emerge in various forms

depending on numerous factors. Throughout the history, maintaining the transient

stability has been the dominant issue on most systems including electrical power grids.

With the continuous evolution of power systems through increasing interconnections,

integration of renewable energy sources, and deployment of digital technologies, various

forms of power systems stability have emerged such as frequency stability, voltage

stability, and rotor angle stability. It should be noted that instability of the power systems

must be carefully identified before treating. In [28], the power systems stability has

been defined and categorized as in Figure 2-1 by considering the physical nature of the

resulting mode of instability, the size of the disturbances, and the devices, process and

time spans to assess stability.

2.1.1 Rotor Angle Stability

Rotor angle stability is the ability of the interconnected synchronous machines

(generators) to remain in synchronism being subjected to a large or a small disturbance.

Multiple synchronous machines run parallel and deliver active power to the loads

depends on the rotor angle of the machines. Power system faults such as losing a large

load lead to a sudden changes on the generator electrical power output. However, the

mechanical power input to generators cannot response instantaneously. In steady-state

27

condition, there is an equilibrium between the input mechanical power and the electrical

output power of all synchronous machines in interconnected power systems and speed

of the machines remain same.

During a fault, synchronous machines lose the equilibrium which results in the

acceleration or deceleration of the machines. If a synchronous machine temporarily

moves faster relative to the other machines, the rotor angle of the machine will advance

with respect to slow machine. The resulting angular difference lead to increase load

delivered by faster machine and decrease load delivered by slow machine in order to

reduce the speed difference and hence the angular separation. Also, after a certain

point, an increase in angular separation will be followed by decreasing the power

transfer by fast machine which further increases the angular separation. If the kinetic

energy corresponding to the angular separation cannot be absorbed, then instability

occurs and synchronization of a machine or a group of machines will be lost.

The power system stability depends on the existence of synchronizing torque and

damping torque for each synchronous machine. Lack of synchronizing torque causes

non-oscillatory instability and lack of damping torque lead to oscillatory instability. Rotor

angle stability is divided into two subcategories: transient stability and small-signal

stability which are the concern of power system when subjected to a large disturbance

and small disturbance respectively.

2.1.1.1 Transient Stability

Transient stability is the ability of synchronous machines to return to stable condition

and maintaining synchronism after being subjected larges disturbances come out of

switching ON and OFF of the circuit breakers to isolate and clear the faults in perturbed

area in interconnected power systems. Since power systems often experience these

type of faults, the transient stability needs to be studied carefully. The transient stability

depends on the initial states and the severeness of the disturbances. Instability may

take place in the form of angular separation due to insufficient synchronizing torque, that

28

Figure 2-2. The dynamic model of synchronous machines.

is also called as first swing instability. In large systems, transient stability may occur as a

result of superposition of a slow inertia swing mode and local plant swing mode leading

a large excursion of rotor angle beyond the first swing [29]. The transient stability is a

short term phenomena, that is, the target time frame for transient stability studies is

usually 3 to 5 seconds following a disturbance. However, the time frame may extend to

10-20 seconds for very large systems with dominant inter-area swings [28].

In order to study the transient stability of a synchronous generators using swing

equation, a synchronous machine, as illustrated in Figure 2-2, can be examined. A

synchronous machine supplied with input mechanical power PM produces a mechan-

ical torque equal to TS that rotates the machine at a speed of ω rad/sec and outputs

electromagnetic torque TE and electrical power PE on the receiving end. In steady state

condition, if the synchronous machine is supplied from one end and a constant load

connected to the other end, a relative angular displacement, , known as load angle δ,

occurs between rotor axis and stator magnetic field in proportional to the loading of the

machine.

A sudden change in loading (add or remove), leads to deceleration or acceleration

of the rotor accordingly with respect to the stator magnetic field and causes swinging of

the rotor speed relative to the stator magnetic field. The relative motion of the load angle

and stator magnetic field is known as swing equation for transient stability of power

system [29–31].

There are numbers of factors that may influence the transient stability of power

systems including generator inertia, generator loading, generator power output during

29

fault, and fault clearing time [29]. The inertia have become even critical with increasing

penetration of inertialess renewable energy generations. The decreasing overall inertia

makes transient stability a challenging issue for interconnected smart grid power

systems.

2.1.1.2 Small-Signal Stability

Small-disturbance or small-signal stability is associated with the ability of power

system to resist small disturbances such that the linearization of the system dynamic

model is allowable for stability analysis [29]. Small-signal stability problems can be

resolved into local and global instability problems. Local problems are associated with

only a small part of the power system and depends on the rotor angle oscillation of a

single machine with respect to the rest of the power systems. Damping these oscilla-

tions rely on the strength of the transmission system. excitation control system and plant

output [29]. On the other hand, global problems arises from inter area oscillations where

oscillation of a group of machine in one area of swinging against a group of machines

in another area. The characteristics of the global instability problems are very compli-

cated and load characteristics are the major effects on the inter area stability mode.

The time span for small-signal stability is on the order of 10 to 20 seconds following a

disturbances [28].

2.1.2 Frequency Stability

Frequency stability stands for maintaining the steady frequency of power systems

after being subjected to large disturbances perturbing the load and generation bal-

ance. The frequency instability causes tripping of generating units and/or loads. In

general, large disturbances end up with excursion of frequency, power flows, voltage,

hence advanced control and protection schemes that are required but not included in

conventional transient stability and voltage stability studies.

Frequency stability requires to maintain an equilibrium between loads and genera-

tors with a minimum unintentional loss of loads. In interconnected power systems, this

30

instability condition emerges during islanding power networks and arises the questions

of whether or not each island will reach the equilibrium with minimum unintentional loss

of load. The frequency stability problems generally stems from the poor response of

the incorporated equipment and lack of coordination between control and protection

systems [28].

During frequency excursion, the response time of the activated process and devices

range from a fraction of seconds for the response of load shedding, generator control,

and protections, to a several minutes for the response time of the devices such as load

voltage controller and prime mover energy supply systems.

2.1.3 Voltage Stability

Voltage stability aims at maintaining steady voltages at all buses after power system

being subjected to a disturbance from a given initial operating points. Voltage instability

occurs when the equilibrium between load and supply is lost, because the load tends

to restore more power than the capacity of generation [29]. The voltage instability may

end up with losing load or tripping transmission lines and so cascaded outages. Also,

some generators may lose synchronism because of outages or operating conditions that

violate field current limit [28,32].

The voltage stability is analyzed under two subcategories as shown in Figure 2-1

that are small disturbance stability and large disturbance stability. Small-disturbance

voltage stability aims at maintaining steady voltages after power systems being sub-

jected to small disturbances such as load variations. Whereas, large-disturbance

voltage stability targets sustaining all bus voltages within an allowable level following a

large disturbance such as loss of generation, systems faults, presence of contingen-

cies [28, 32]. Also, it is worth to note that the time span for voltage stability problem

ranges from a few seconds that corresponds to short-term, to tens of minutes that

corresponds to long-term voltage stability. The short-term voltage stability comprise of

fast acting load components such as electronically controlled loads, induction motors,

31

and HVDC converters. Whereas, the long-term voltage stability includes slow acting

components such as generator current limiters and tap-changing transformers [28].

2.2 Controls for Smart Grid Transient Stability

Reliability is the major concern of the power system design and operation. Main-

taining reliability depends on security of the power system. To be secure, the power

system must be stable and also must be secure against intentional or unintentional

cyber physical disturbances at all level. In conventional power systems, various control

and protection systems have been employed to enhance the reliability, security and

stability of the electrical power grid. However, the power grids have experienced a big

changes with the integration of digital technologies, distributed energy generation and

various smart applications such as demand response, real-time monitoring and control.

The rapid transformation of the power systems into smart grid emerges new security

and stability problems beside numerous advantageous.

The growing complexity of the power systems has left the conventional control

systems insufficient in terms of security and stability. Transient stability, for instance, has

been taken care of local excitation controllers in traditional power system. However, the

increasing penetration of low inertia or inertia-less power generations arises and fuels

the transient stability problem once again. In order to maintain the transient stability

with respect to reducing rotational inertia in power system, robust control architectures

are required. Due to the fact that smart grid has a cyber-physical nature, the proposed

controllers need to be studied with a particular focus on cyber-physical effects and

limitations.

In the literature, control architectures for stability of power systems are categorized

into three groups: i) decentralized (local) controllers, ii) centralized controllers, and iii)

distributed controllers. In this section, different control schemes that fall in these groups

are reviewed.

32

2.2.1 Decentralized Control

Decentralized controllers are distributed all over the power systems that operates

locally and perform its own objective rather than global objectives. Most of the excitation

controllers in conventional power systems fall into this category. In decentralized

control, each local controller situated near synchronous generators observes the rotor

speed and rotor angle of its generator and calculate the control signal. Little or no

communication need in decentralized control systems reduces the cost but lack of

information exchange decreases the overall system performance.

Conventional power system stabilizers (PSS) are an excellent example of the local

linear controllers [33–35]. However, PSS are designed to respond to small disturbances,

caused by small load variations, thus their contribution for transient stability is marginal.

Over the last few years, advanced excitation control schemes have been developed to

enhance the robustness of power systems by using nonlinear techniques or linearization

techniques such as direct feedback linearization (DFL). The DFL method has been

proposed in [36–39] to linearize the nonlinear dynamic model of multi-machine power

systems in order to make it possible to use linear control techniques. Even though

these DFL techniques provide a stabilization in large extent, they all require the exact

knowledge of the power system plant parameters which is usually unavailable in

practice [40]. Also, unexpected faults in system and external disturbances could

degrade the stabilizing capability of a controller that is designed for a specific model.

2.2.2 Centralized Control

Centralized controllers are considered advantageous over decentralized controllers

in terms of situational awareness since centralized controllers enable information

exchange globally. The integration of information communication systems makes

possible monitoring the power system components in real-time through sensors and

communication networks and control the entire systems with a broad knowledge. Wide-

area control systems (WACS) are currently implemented centralized control approaches.

33

WACS are implemented implemented in addition to the local primary controllers. It

is envisioned that WACS can effectively address the inter-area oscillation problem

since information exchange is enabled [41]. WACS are developing in parallel to the

advancement in communication, sensor and digital signal processing technologies.

Phasor measurement units (PMU), for instance, are one of the advanced sensor

technologies deployed to the power systems in order to measure and transmit the

real-time phasor data with a high precision and in real-time accordingly.

Besides all advantages of WACS structures, the increasing number of PMU

deployment for enhancing observability leads an exponential rise in data volume and

arises communication problems such as time delay. Aggregating information at a

central point to calculate the control action and transmitting control signal back to local

actuators significantly increase the communication overhead. Moreover, the reliability

of centralized controller is low, because communication systems are vulnerable to

cyber-attacks and rely on a single point which increases the risk that any possible attack

may lead the system to instability. In addition, communication latency is another issue

that centralized controller hold due to the long communication distances. Transient

stability, for example, is a short term phenomena and need to be circumvent quickly. If

the latency goes beyond the acceptable rate, it becomes one of the key disturbance by

itself for centralized controller.

2.2.3 Distributed Control

The distributed controller have been a rising trend for multi-agent systems because

of their efficiency in terms of processing and capability of enabling coordination between

agents. The rising communication and security problems in centralized controllers

and lack of information exchange in classical decentralized controller make distributed

control strategies promising for power system stability. In distributed power system

control strategies, first the entire system is divided into small regions and the interaction

34

between these regions are maintained through distributed controllers. Since sensors

only transmit data to the associated controller, communication burden is relieved.

Utilizing distributed control approaches do not only ease the communication

burden and decrease latency but also enhance the power system security in contrast

to centralized scheme. For example, if a distributed control agent experience a cyber-

attack, other distributed controllers can sustain their operation by isolating the attacked

controller from the system. Furthermore, distributed controller are usually secondary

controller, hence even if their operation perturbed, the primary local controller will

continue to treat the system.

Distributed controls for networked multi-agent systems have attracted many re-

searchers because of their advantages in reducing communication load while enhancing

the system performance. In [42, 43], authors present distributed consensus controllers

for networked multi-agent systems. A distributed model predictive control (MPC) is

presented in [44] and Venkat et al., uses distributed MPC strategy for power system

automatic generation controller in [45,46].

Furthermore, a distributed control strategy was designed to address transient

stability problem by using parametric feedback linearization (PFL) technique to actuate

external storage sources (ESS) in [47, 48]. Authors aim at damping the oscillation after

power system being subjected a large disturbance by absorbing or injecting power

from generator buses via fast acting EES such as flywheels or batteries. However,

the proposed model is inconsistent with the distributed approach since it explicitly

considers the knowledge of all generator angles and voltages in calculation of power

flow. In addition , the proposed PFL based control requires exact model knowledge of

the dynamic system which is unrealistic for highly nonlinear smart grid power systems.

Furthermore, a distributed frequency control algorithm that is capable of restoring

system frequency after a disturbance is presented in [49]. This method uses a PI-like

controller to actuate the mechanical power input of generators to damp oscillations of

35

power systems. Technological limitations of the generator turbine and valve actuation

speeds and time constants may limit the performance and effectiveness of this kind

of approach for system stabilization. One limitation of this method is that it considers

linearized power flow equations, which may introduce errors when generator angles

largely deviate from the equilibrium point during severe disturbances.

Time delay is still an issue even for distributed controllers since even a small

time delay may significantly affect the control performance. Time delay systems have

been thus a major interest in many studies [50]. In recent decades, efforts focused on

designing controllers which are subjected to time delay for linear dynamics [51, 52] and

nonlinear dynamics [53–59]. Compensating the time delay problem has been addressed

for linear systems in [60–64] and for nonlinear systems in [65–68] with exact model

knowledge. In [66–68], predictor based controllers are designed to compensate for

an arbitrary long input time delay by transforming the input delay as a transport partial

differential equation. However, the predictor-based controllers require the exact model

knowledge of the dynamics and do not consider uncertainties in field parameters and

external disturbances. Uncertainties in field parameters and external disturbances are

inevitable in many nonlinear systems such as smart grids. In order to abstain from the

exact model knowledge requirement of the dynamics, several nonlinear controllers

are designed [59, 69–71]. Hence, there is a need for designing controllers that can

compensate for the time delay and external disturbances for uncertain nonlinear

dynamics. The designed controllers [59, 69, 70] consider single-agent systems and

do not consider the communication delay. Therefore, there is a need for distributed

controllers that are robust to communication and control input delay and also external

disturbances for uncertain nonlinear dynamics such as smart grids.

2.3 Summary

In this chapter, the emerging and existing power systems stability problems with

their causes and effects are discussed for current smart grid power systems. Also, the

36

state-of-art control systems that are already implemented or proposed for addressing

power system instability are presented. Since the major interest of this thesis is to

develop control systems to enhance the the transient stability of smart grid, the literature

review is extended for controllers that have been developed for the same purposes.

In conclusion, we assert that the transient stability problem is becoming even

more critical with the rapid penetration of inertia-less renewable energy sources.

Also, the integration of information and communication systems has transformed the

power system into cyber-physical system, hence cyber security emerges another

challenging problem. Therefore, we advocate that in order to enhance the resiliency

of the smart grid, advanced control strategies that are robust to cyber disturbances

such as communication delay, contingent sensor measurement and-cyber attacks, and

physical disturbances such as losing loads or generators are required.

37

CHAPTER 3A ROBUST DECENTRALIZED CONTROL FRAMEWORK WITH CONSTANT TIME

DELAY COMPENSATION

Reliability is the key to the success of power system that invokes robust control

systems for enhancing the stability margin. The large penetration of distributed genera-

tions with their inertia-less generation units leads to a transient stability problem, which

refers to the ability of synchronous generators to return back to their nominal operation

frequency after being subjected a large disturbance. Transient instability has always

been a major problem in multi-machine interconnected power systems with respect to

varying network configuration, loading, and power flow condition and become even more

dynamic with the integration of intermittent renewable energy sources.

Current excitation control systems implemented for transient stability of power

systems are insufficient to maintain stability during inter-area oscillations. Therefore, in

this chapter, a new nonlinear controller for the synchronous machine excitation system

is presented that uses a novel control input time delay compensation technique and

distributed energy storage sources (DESS) to enhance the transient stability of smart

grids. The designed control framework, shown in Figure 3-1, uses local measurements

(rotor angle and rotor speed) obtained by phasor measurement units connected at

the generator buses and actuates fast acting DESS such as flywheels or batteries to

inject or absorb damping power to the system. The objective is to return the perturbed

synchronous machines back to synchronism as quick as possible.

DESS are currently being deployed in power systems to perform zero-energy

ancillary services, such as load following and regulation, especially due to the increasing

penetration of intermittent solar and wind power sources [72]. The presented control

framework adds a function to DESS by using them for transient stability. PMU sensors

are employed because of their capability in providing real-time data to controller and

other power system functions to enhance visibility of entire system.

38

Figure 3-1. An overview of decentralized control framework for transient stability.

As the designed decentralized control framework can be used to address transient

instability problem by itself, it can also be used as a backup controller to Wide-area

control systems (WACS). Due to the centralized nature of WACS, they may suffer from

large communication delay, cyber disturbances and loss of remote sensors. Hence, a

backup controller like primary excitation controllers is needed to enhance the resiliency

of the smart grids.

Time delay is inherent for all systems and significantly affects the performance

of the incorporated applications. The time delay in the proposed control framework

arises from the communication between sensor and controller, processing in controller

and response time of the DESS. To mitigate the effect of time delay and uncertainties

arise from varying plant parameters and cyber disturbances, the proposed nonlinear

robust controller is leveraged by developing a novel delay compensation technique and

considering the time-varying additive disturbances in the dynamic model.

The Lyapunov stability analysis proves that all error tracking signals are globally

uniformly ultimately bounded (GUUB) and the decentralized nonlinear controller can

guarantee the system stability over the whole operating region regardless of fault loca-

tions or parameter uncertainties of the transmission network. Furthermore, the designed

decentralized control framework was implemented on IEEE 39 bus 10 machine test

39

power system through Matlab/Simulink and validated with respect to varying fault loca-

tions, DESS capacity, control input time delay and parametric uncertainties. Simulation

results demonstrate the success of the proposed control framework in damping the

post-fault frequency oscillation very quickly.

3.1 Dynamic Model of Synchronous Machines

Multi-machine power systems consist of n synchronous generators, which are

interconnected through a transmission network. The transient stability of synchronous

generators are studied by using swing equations defined in [29–31,73] as:

δ̇i = ωi,

ω̇i =1

Mi(−Diωi + Pm,i − Pe,i) , (3–1)

where ωi ∈ R is the rotor speed deviation from the synchronous rotating reference

of generator i in radian per second (rad/s), which is the difference between electrical

actual rotor speed ωacti ∈ R (rad/s) and synchronous rotor speed ωo ∈ R (rad/s),

ωi = ωacti − ω0 and ω̇i ∈ R is the acceleration of the generator i. The δi ∈ R denotes

the rotor angle deviation from the synchronous rotating reference of the generator i in

radian, equivalent to the transient internal voltage angle of the machine [29]. Di is per

unit damping constant and Mi denotes the normalized inertia constant, given in s2/rad.

The difference between mechanical power input (Pm,i) and electrical power output (Pe,i),

both are in p.u, is defined as the accelerating power (Pa,i), that is, (Pa,i = Pm,i−Pe,i). Pm,i

can be assumed as constant for transient stability analysis and Pe,i can be calculated by

using the following power flow equation.

Pe,i =κ∑k=1

|Ei| |Ek|(Gik cos (δi−δk)+Bik sin (δi−δk)) , (3–2)

where κ ∈ R is the set of buses connected to bus i, Gik ∈ R denotes the equivalent

conductance between the buses i and k, while Bik ∈ R is the equivalent susceptance

40

between the buses i and k. Ei, Ek ∈ C are the voltages of buses i and k, respectively,

while δi ∈ R and δk ∈ R are their respective angles.

3.2 Robust Nonlinear Controller Design

By considering the external damping power absorbed or injected through DESS, the

swing equation (3–1) modifies as follows:

ω̇i =1

Mi(−Diωi + Pa,i + di (t) + ui(t− τi)) , (3–3)

where, ui (t− τi) ∈ R represents the external generalized delayed input control signal

which corresponds to the DESS power output. τi ∈ R is a known constant non-negative

time delay and t0 ∈ R is the initial time. Also, di : [t0,∞) → R denotes uncertain

time-varying exogenous disturbance stems from uncertainties in power system. The

controller is developed based on the following assumptions:

Assumption 1. The rotor angle (δi) and rotor speed (ωi) are measurable.

Assumption 2. Mi is bounded by known, positive, constants such that mi ≤ Mi ≤ m̄i,

where m̄i,mi ∈ R. Di and Pe,i are also bounded by a known constants.

Assumption 3. The unknown nonlinear exogenous disturbance and its first time

derivative exist and are bounded by known positive constants [74].

Assumption 4. δid ∈ R, is designed such that the δid refers to the pre-fault value of δi.

Assumption 5. The input delay is a known non-negative constant. Also, it is assumed

that the system in (3–1) does not escape to infinity during the time interval [t0, t0 + τi]

[59].

The objective of the control design is to ensure that the actual rotor angle of

each generator (δi) tracks its desired rotor angle (δid) despite a known constant input

41

delay, additive disturbances and uncertainties in the dynamics. To quantify the control

objective, a measurable auxiliary tracking error, denoted by e0i ∈ R is defined as

e0i =

∫ t0

(δi (θ)− δd,i (θ)) dθ. (3–4)

where the first time derivative of e0i quantifies the control objective. To facilitate the

subsequent analysis, auxiliary tracking errors, denoted by e1i ∈ R, defined as

e1i = ė0i + αie0i (3–5)

where αi ∈ R is an adjustable, positive, constant control gain.

In order to compensate the input delay, an auxiliary error signal denoted by eui ∈ R

is developed to to inject a delay-free input signal and cancel the delayed input signal in

the closed-loop error system as follows:

eui =

∫ tt−τi

ui (θ) dθ. (3–6)

Furthermore, an auxiliary tracking errors, denoted by ri ∈ R, defined as

ri = ė1i + βie1i + ηieui, (3–7)

where βi, ηi ∈ R are adjustable, positive, constant control gains. Based on the sub-

sequent stability analysis, the following continuous robust controller is is designed

as

ui = −kiri, (3–8)

where ki ∈ R are positive, adjustable, constant control gains. Taking the time derivative

of (3–7) and using (3–3)-(3–5), (3–6), and (3–8), the closed-loop dynamics for ri can be

obtained as

ṙi =

(αi + βi −

DiMi

)ωi +

1

MiPa,i +

diMi−(ηi −

1

Mi

)ui(t− τi)

+αiβi (e1i − αie0i)− ηikiri. (3–9)

42

To facilitate the stability analysis, the expressions in (3–9) can be segregated as terms

that can be upper bounded by a state-dependent function and by a constant, such that

ṙi = Ñi +Ni − e1i +(ηi −

1

Mi

)kiri(t− τi)− ηikiri (3–10)

where the auxiliary terms Ñi, Ni ∈ R can be defined as

Ñi ,

(αi + βi −

DiMi

)ωi + αiβi (e1i − αe0i) (3–11)

Ni ,1

MiPa,i +

diMi

. (3–12)

Remark 3.1. By using Assumption 2, an upper bound can be obtained for (3–11) as∣∣∣Ñi∣∣∣ ≤ψ1i ‖zi‖ , (3–13)where ψ1i ∈ R is a known positive constant and zi ∈ R4 is the vector of error signals

defined as

zi , [e0i, e1i, ri, eui]T . (3–14)

Remark 3.2. Ni is upper bounded by a known constant, by Assumptions 2,

supt∈ R

|Ni| ≤ ψ2i, (3–15)

where ψ2i ∈ R is a known positive constant .

To facilitate the subsequent stability analysis, auxiliary bounding positive constants

σi,Ωi ∈ R are defined as

σi,min

{(αi− 12

),(βi− η

2i

2ε1i−1

2

),(ω2i3τi−ε1i

), ηiki

8

}(3–16)

Ωi ,min

{σi2,ω2ik

2i

3,

1

3τi

}(3–17)

43

where �1i, ω2i ∈ R are known, positive constants. It should be noted that �1i, ω2i ∈ R are

not implemented in the control law but will be used for stability analysis. Also, σi and Ωi

will be used as bounding constants (for convergence decay rate and definition of the

domain of attraction) in Section 3.3. As a conclusion, the result is global in the sense

that D = SD = R5.

3.3 Lyapunov Stability Analysis

Theorem 3.1. Given the dynamics in (3–1), the controller given in (3–8) ensures

globally uniformly ultimately bounded tracking in the sense that

‖ei (t)‖ ≤ �0i exp (−�1it) + �2i, (3–18)

where �0i , (1 + αi)√

2Vi (t0)− 2λ2iΦ2i

Ωimiηiki, �1i , − Ωiλ2i (t− t0) and �2i , (1 + αi)

√2λ2iΦ2i

Ωimiηiki.

The control gains are selected sufficiently large relative to the initial conditions of the

system such that the following conditions are satisfied.

αi >1

2, (3–19)

βi >η2i

2ε1i+

1

2, (3–20)

ηi <1

mi+ ψ3i, (3–21)

ki ≥2ψ1iηiσi

, (3–22)

ω2i >3τiε1i, (3–23)

τi ≤ηi8− (2φ

2i

�1i+ψ23iηi

) (3–24)

Proof. Let V : D → R be a continuously differentiable Lyapunov function candidate

defined as

Vi,1

2eT0ie0i +

1

2eT1ie1i +

1

2rTi ri +

φi2eTiueiu +Q1i +Q2i, (3–25)

44

where Q1i, Q2i ∈ R are defined as

Q1i ,

(ψ23ik

2i

ηiki+φ2i kiε1i

) t∫t−τi

ri (θ)2 dθ, (3–26)

Q2i ,ω2i

t∫t−τi

t∫s

u2i (θ) dθds, (3–27)

and let yi ∈ R5 be defined as

yi ,

[zi,√Q1i,√Q2i

]T. (3–28)

The following inequalities can be obtained for (3–25) as

λ1i ‖yi‖2 ≤ Vi ≤ λ2i ‖yi‖2 , (3–29)

where λ1i , min{12 ,φi2} andλ2i , max{φi2 , 1}. The time derivative of (3–26), (3–27) and

using (3–5)-(3–6), (3–10), the time derivative of (3–25) can be obtained as

V̇i = eT0i (e1i − αe0i) + eT1i (ri − βie1i − ηieiu) + φieTuiki (ri − ri(t− τi))

+ rTi

(Ñi +Ni − e1i +

(ηi −

1

Mi

)kiri(t− τi)− ηikiri

)

+

(ψ23ik

2i

ηiki+φ2i kiε1i

)(r2i − r2i (t− τi)

)+ ω2i

τik2i r2i − t∫t−τi

u2i (θ) dθ

(3–30)After completing the squares for ri with rTi Ñi, rTi Ni, applying Young Inequality for cross

terms in (3–30), the following upper bound can be obtained for (3–30) as

V̇i ≤ −(αi −

1

2

)e20i −

(βi −

η2i2ε1i− 1

2

)e21i + ε1ie

2ui +

ψ1iηiki‖zi‖2 +

1

ηikiψ22i

−(ηiki8−(φ2i kiε1i

+ψ23ik

2i

ηiki+φ2i kiε1i

+ ω2iτik2i

))r2i − ω2i

t∫t−τi

u2i (θ) dθ (3–31)

45

The Cauchy-Schwartz inequality is used to develop the following upper bound for e2ui.

e2ui ≤ τi

t∫t−τi

u2i (θ) dθ. (3–32)

The following upper bound can be obtained for Q2i as

Q2i ≤ ω2iτi sups�[t−τi,t]

t∫s

u2i (s) ds

≤ ω2iτi t∫t−τi

u2i (θ) dθ. (3–33)

Using (3–8), (3–26), (3–32), (3–33) and the gain condition (3–24), the following upper

bound can be obtained for (3–30) as

V̇i ≤ −(αi −

1

2

)e20i −

(βi −

η2i2ε1i− 1

2

)e21i −

ηiki8r2i −

(ω2i3τi− ε1i

)e2ui

+ψ1iηiki‖zi‖2 −

ω2ik2i

3Q1i −

Q2i3τi

+1

ηikiψ22i (3–34)

Using the definitions of zi in (3–14) and σi in (3–16), and the gain conditions (3–19),

(3–20), (3–23) an upper bound can be obtained for (3–34) as

V̇i ≤ −(σi2− ψ1iηiki

)‖zi‖ 2 −

σi2‖zi‖ 2 −

ω2ik2i

3Q1i −

Q2i3τi

+1

ηikiψ22i (3–35)

Using the definition of Ωi in (3–17), the gain condition in (3–22), the definition of yi and

(3–29), an upper bound can be obtained for (3–35) as

V̇i ≤ −Ωiλ2i

Vi +1

ηikiψ22i (3–36)

The solution of the differential equation in (3–36) can be obtained as

Vi (t) ≤ Λi (3–37)

46

where Λi , Vi (t0) e− Ωiλ2i

(t−t0) +ψ22iλ2iδiηiki

(1− e−

Ωiλ2i

(t−t0))

. Using (3–25) and (3–37), the

following inequalities can be obtained for e0i, e1i, ri and eui as

|e0i| ≤√

2Λi, |e1i| ≤√

2Λi,

|ri| ≤√

2Λi, |eui| ≤√

2

φΛi.

By using the time derivative of (3–4) and (3–5), |δi − δid| can be upper bounded as

|δi − δd,i| ≤ (1 + αi)√

2Λi,

By using (3–1), (3–4) and (3–5), |ωi − ωd,i| can be upper bounded as

|ωi − ωd,i| ≤(

1 + βi +ηiφi

)√2Λi.

It is concluded that by using the Theorem 4.18 in [75], y is semi-globally uniformly

ultimately bounded where uniformity in initial time can be concluded from the indepen-

dence of δi and the ultimate bound from to. Since e0i, e1i, ri and eui ∈ L∞, then from

(3–8), ui ∈ L∞. Analysis of the closed-loop system shows that the remaining signals are

bounded.

3.4 Simulation and Results

The developed control framework has been implemented and validated on the IEEE

39 bus 10 machines test power system via Matlab-Simulink. The model parameters

of test power system were obtained from [31, 76] and given in Appendix A. The power

output capacity of each connected DESS (PDESS ) is rated to the mechanical power

output of the associated generator, that is, ( PDESS = ρPm ). Also, desired rotor angles

values (δd,i ) are set to their own pre-fault values for the sake of easiness as suggested

in [48]. In addition, The controller gains, given in Table 3-1, are found with respect to the

gain conditions given in (3–19) - (3–24).

47

Table 3-1. Decentralized controller gain settings for known constant time delay case.αi βi ηi ki

Generator 1 0.01 0.08 60 3.85

Generator 2 0.01 0.08 60 0.23

Generator 3 0.01 0.08 60 0.27

Generator 4 0.01 0.08 60 0.22

Generator 5 0.01 0.08 60 0.20

Generator 6 0.01 0.08 60 0.26

Generator 7 0.01 0.08 60 0.20

Generator 8 0.01 0.08 60 0.18

Generator 9 0.01 0.08 60 0.26

Generator 10 0.01 0.08 60 0.32

The objective of the control framework is to recover the perturbed synchronous

generators after a major disturbance. Therefore, a solid three-phase fault is applied

at bus 17 in line 16-17 at to=0.5 s in order to test the stabilization performance of the

controller. Afterwards, the fault is cleared by opening circuit breakers within clearing

time that is set to 100 ms unless otherwise specified. The controller takes action

and actuates the DESS to inject or absorb power according to the control signal

after 100 ms. The performance of the proposed control framework is quantified in

terms of stabilization time that is calculated as the time between fault inception and

convergence of all generators speed to the synchronous frequency of 60 Hz within a

tolerance of 0.1%, that is, [59.94-60.06] Hz. This stabilization criterion is inspired by the

Frequency Trigger Levels of 0.05 Hz deviation by North American Electric Reliability

Corporation [77]. Finally, the simulation time was set to 20 ms and beyond that time limit

refers to losing stability and failure of controller.

The frequency oscillation and rotor angle deviation of all generator during and after

fault, shown in Figure 3-2 over time, demonstrate the stabilization performance of the

proposed controller when the control input delay and maximum capacity of DESS are

48

0 1 2 3 4 5 6 7 8 9 10

Time (seconds)

0

10

20

30

40

50

60

70

80

90A

ng

le -δ (

Deg

ree)

δ

1

δ2

δ3

δ4

δ5

δ6

δ7

δ8

δ9

0 1 2 3 4 5 6 7 8 9 10

Time (seconds)

59.6

59.7

59.8

59.9

*59.94

60

*60.06

60.1

60.2

60.3

60.4

60.5

Spee

d (

ω )

Hz

ω1

ω2

ω3

ω4

ω5

ω6

ω7

ω8

ω9

ω10

Figure 3-2. Rotor angle and speed deviation of synchronous machines during and afterthree phase fault.

set to τi=10 ms and ρi=0.2 respectively. Furthermore, the response of the controller with

respect to varying control input delay, DESS power output capacity and the magnitude

of additive disturbance were evaluated.

It should be noted that all PSS controllers were disabled in this simulation to make

the condition more aggressive. First, the performance of the designed control framework

was demonstrated in Figure 3-3 with respect to varying DESS capacity and control

input time delay. The simulation results show that the proposed control framework

can stabilize the frequency oscillation within [6-8] s based on the time delay (τi) when

5 10 15 20 25 30 35

DESS power output capacity - ρ

0

1

2

3

4

5

6

7

8

9

10

Sta

bil

izati

on

tim

e [

s]

τi =10ms

τi =50ms

τi =100ms

τi =130ms

Figure 3-3. Stabilization time versus varying DESS capacity and time delay.

49

0.05 0.1 0.15 0.2 0.25

Fault clearing time [s]

0

2

4

6

8

10

12

14

16

18

20

Sta

biliz

ati

on

tim

e [

s]

Decentralized (τi =10ms)




MBPSS

Figure 3-4. Stabilization time versus clearing ti

TOWARDS RESILIENT SMART GRIDS: ROBUST CONTROL...

Documents

Transcript of TOWARDS RESILIENT SMART GRIDS: ROBUST CONTROL...