An Approach To Improving The Physical And Cyber Security Of A Bulk Power System With FACTS

An Approach To Improving ThePhysical And Cyber Security Of ABulk Power System With FACTS

Mariesa Crow & Bruce McMillinSchool of Materials, Energy & Earth

ResourcesDepartment of Computer Science

University of Missouri-Rolla

Stan AtcittyPower Sources Development Department

Sandia National Laboratory

FACTS

FACTSPhysical Attack

NaturalFaults

Funded through the DOE Energy Storage Program

Problem Motivation

Prevent Cascading failures: 2003 Blackout

Causes Physical & Cyber

contingencies Deliberate disruption

HackersHackers Terrorist ActivityTerrorist Activity

Proposed Solution

Flexible AC Transmission Systems (FACTS) Power Electronic Controllers Means to modify the power flow through a

particular transmission corridor Integration with energy storage systems

US FACTS Installations

San Diego G&E/STATCOM/100 MVA

Mitsubishi

Eagle Pass (Texas)Back-to-back HVDC

37 MVA/ ABB

CSWS (Texas)STATCOM/ 150

MVA / W-Siemens

Austin EnergySTATCOM/ 100MVA

ABB

AEP/ Unified Power Flow Controller /100 MVA/ EPRI

TVASTATCOM/ 100MVA

EPRI

Northeast Utilities/ STATCOM/ 150 MVA/

Areva (Alstom)

NYPA/ Convertible Static Compensator/

200 MVA

Vermont Electric/ STATCOM/ 130

MVA/ Mitsubishi

Communication and coordination Scheduling - Distributed Long-

Term control Interaction – Local Dynamic

control Vulnerabilities of the

combined physical/ cyber system

Recovery and protection from physical faults and/or cyber attacks and/or human error

Decentralized Infrastructures

Identify cascading failure scenarios for

test systems

Cascading ScenarioOutage 48-49

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Cascading ScenarioOutage 48-49

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Cascading ScenarioOutage 48-49

45

46

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G69

68

G

G

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66

65

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50

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MuskngumN

MuskngumS

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44WMVernon

N.Newark

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62

64

37

34

36

NwLibrty

39

40 41 42

43

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Rockhill

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Sterling

Portsmth

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Trenton

24

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Hillsbro72

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Cascading ScenarioOutage 48-49

45

46

W.Lancst Crooksvl

47

G69

68

G

G

49

66

65

Zanesvll48

50

Philo

MuskngumN

MuskngumS

67

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Kammer

44WMVernon

N.Newark

SpornW

Summerfl

62

64

37

34

36

NwLibrty

39

40 41 42

43

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FACTS Placement and Control

FACTS Control

Distributed Long-Term control algorithms for FACTS settings Run by each processor in each FACTS Alternatives

Max-flow algorithms Max-flow algorithms Local optimizationsLocal optimizations Agent-based frameworkAgent-based framework

Assessment Reduction of OverloadsReduction of Overloads ComputabilityComputability

FACTS Placement

Placement Place few FACTS

in a large network for maximum benefit

Evolutionary Algorithms (EAs) will be used to place FACTS devices in the network

kn

line

line

lineline

yContingencyContingenc S

SPPI

2

max

Performance Index Metric

Gradient Descent on PI Metric vs. Maximum Flow

FACTS Interaction Laboratory (FIL)

FIL Overview Construct a Laboratory System to Study

and Mitigate Cascading Failures Deleterious effects of interacting power

control devices Cyber Vulnerabilities

Hardware in the Loop (HIL) Real-time Simulation Engine

Simulate Existing Power SystemsSimulate Existing Power Systems Inject Simulated FaultsInject Simulated Faults

Interconnected laboratory-scale UPFC FACTS Device

Measure actual device interactionMeasure actual device interaction

FACTS Interaction LaboratoryArchitecture

FACTS

10 KVA

3332

31 30

35

80

78

74

7966

75

77

7672

8281

8683

8485

156157 161162

vv

167165

15815915544

45160

166

163

5 11

6

8

9

1817

43

7

14

12 13

138139

147

15

19

16

112

114

115

118

119

103

107108

110

102

104

109

142

376463

56153 145151

15213649

4847

146154

150149

143

4243

141140

50

57

230 kV345 kV500 kV

36

69

Simulation Engine

(multiprocessor)

FACTS/ESS

FACTS/ESS

FACTS/ESS

A/D D/A

A/D

D/A

A/D

D/A

Network

HIL Laboratory Interface

Machine 1

D/A output

A/D input

FACTS1

ControllableLoad

Machine 2

Machine 3

Power System Simulation Engine

ControllableLoad

ControllableLoad

FACTS2

FACTS3

D/A output

A/D input

D/A output

A/D input

FACTS – Flexible AC Transmission System Prototype Device

Computer

BoardsDSP board

Interface boardSensor boardPower supply

Converters

TransformersFilters

DC capacitorsTwo driving & protection boards

Two six-pulse converters

One shunt transformerOne series transformer

Two filters

FACTS Interaction Laboratory

HIL Line

UPFC

Simulation

Engine

Cyber Fault Detection

Fault Tolerance

Define correct operation of the power system with FACTS/ESS

Embed as executable constraints into each FACTS/ESS computer

FACTS/ESS check each other during operation of distributed control algorithms

Cyber Fault Injection

Attempt to confuse the FACTS embedded computers

Attempt to disrupt the communication between FACTS embedded computers

Confuse the power system’s operation

Error Coverage of Distributed Executable Correctness Constraints

(Maximum Flow Algorithm)

System Dynamic Control

Power Network Embedded With FACTS Devices

Tie-line flow

CONTROL

AREA A

CONTROL

AREA BA decentralized power network embedded with FACTS devices can be viewed as a

hybrid dynamical system (Differential-algebraic-discrete-event).

While the FACTS devices offer improved controllability, their actions in a decentralized power network can cause deleterious interactions among them.

Performance of FACTS controllers with ideal observability

Uncontrollable modes in generator speeds due to device interactions

Project Benchmarks

Construction of HIL Demonstration of Cascading Failures Placement and Control Hardware/Software Architecture Cyber Fault Detection Dynamic Control Visualization

Special Thanks

Imre Gyuk – DOE Energy Storage Stan Atcitty – Sandia National Lab John Boyes – Sandia National Lab