An Approach To Improving The Physical And Cyber Security Of A Bulk Power System With FACTS Mariesa Crow & Bruce McMillin School of Materials, Energy & Earth Resources Department of Computer Science University of Missouri-Rolla Stan Atcitty Power Sources Development Department Sandia National Laboratories FACTS FACTS Physical Attack Natural Faults Sandia is a multi-program laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.
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An Approach To Improving ThePhysical And Cyber Security Of ABulk Power System With FACTS
Mariesa Crow & Bruce McMillinSchool of Materials, Energy & Earth Resources
Department of Computer Science
University of Missouri-Rolla
Stan AtcittyPower Sources Development Department
Sandia National Laboratories
FACTS
FACTSPhysicalAttack
NaturalFaults
Sandia is a multi-program laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United StatesDepartment of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.
ACKNOWLEDGMENTS
Funded in part by the Energy Storage Systems Program of the U.S. Department Of Energy (DOE/ESS) through Sandia National Laboratories (SNL).
Flexible AC Transmission Systems (FACTS)Power Electronic ControllersMeans to modify the power flow through a particular transmission corridorIntegration 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 controlInteraction – Local Dynamic control
Vulnerabilities of the combined physical/ cyber systemRecovery 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
45
46
W.Lancst Crooksvl
47
G69
68
G
G
49
66
65
Zanesvll48
50
Philo
MuskngumN
MuskngumS
67
G
Natrium
Kammer
44WMVernon
N.Newark
SpornW
Summerfl
62
64
37
34
36
NwLibrty39
40 41 42
43
S.Kenton
38
S.TiffinWest End Howard
Rockhill
EastLima
Sterling
Portsmth
Bellefnt 74 75SthPoint
CollCrnr23
GTannrsCk
Trenton
24
Portsmth
Hillsbro72
70
71
Sargents73
NPortsmt
WLima35
SpornE
54
51
Cascading ScenarioOutage 48-49
45
46
W.Lancst Crooksvl
47
G69
68
G
G
49
66
65
Zanesvll48
50
Philo
MuskngumN
MuskngumS
67
G
Natrium
Kammer
44WMVernon
N.Newark
SpornW
Summerfl
62
64
37
34
36
NwLibrty39
40 41 42
43
S.Kenton
38
S.TiffinWest End Howard
Rockhill
EastLima
Sterling
Portsmth
Bellefnt 74 75SthPoint
CollCrnr23
GTannrsCk
Trenton
24
Portsmth
Hillsbro72
70
71
Sargents73
NPortsmt
WLima35
SpornE
54
51
Cascading ScenarioOutage 48-49
45
46
W.Lancst Crooksvl
47
G69
68
G
G
49
66
65
Zanesvll48
50
Philo
MuskngumN
MuskngumS
67
G
Natrium
Kammer
44WMVernon
N.Newark
SpornW
Summerfl
62
64
37
34
36
NwLibrty39
40 41 42
43
S.Kenton
38
S.TiffinWest End Howard
Rockhill
EastLima
Sterling
Portsmth
Bellefnt 74 75SthPoint
CollCrnr23
GTannrsCk
Trenton
24
Portsmth
Hillsbro72
70
71
Sargents73
NPortsmt
WLima35
SpornE
54
51
Cascading ScenarioOutage 48-49
45
46
W.Lancst Crooksvl
47
G69
68
G
G
49
66
65
Zanesvll48
50
Philo
MuskngumN
MuskngumS
67
G
Natrium
Kammer
44WMVernon
N.Newark
SpornW
Summerfl
62
64
37
34
36
NwLibrty39
40 41 42
43
S.Kenton
38
S.TiffinWest End Howard
Rockhill
EastLima
Sterling
Portsmth
Bellefnt 74 75SthPoint
CollCrnr23
GTannrsCk
Trenton
24
Portsmth
Hillsbro72
70
71
Sargents73
NPortsmt
WLima35
SpornE
54
51
FACTS Placement and Control
FACTS Control
Distributed Long-Term control algorithms for FACTS settings
Run by each processor in each FACTSAlternatives
MaxMax--flow algorithms flow algorithms Local optimizationsLocal optimizationsAgentAgent--based frameworkbased framework
AssessmentReduction of OverloadsReduction of OverloadsComputabilityComputability
FACTS Placement
PlacementPlace 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
maxω
Performance Index Metric
Gradient Descent on PI Metric vs. Maximum Flow
FACTS Interaction Laboratory (FIL)
FIL OverviewConstruct a Laboratory System to Study and Mitigate
Cascading FailuresDeleterious effects of interacting power control devicesCyber Vulnerabilities
Hardware in the Loop (HIL)Real-time Simulation Engine
Simulate Existing Power SystemsSimulate Existing Power SystemsInject 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
FACTS Interaction Laboratory
HIL Line
UPFC
Simulation
Engine
Cyber Fault Detection
Fault Tolerance
Define correct operation of the power system with FACTS/ESSEmbed as executable constraints into each FACTS/ESS computerFACTS/ESS check each other during operation of distributed control algorithms
Cyber Fault Injection
Attempt to confuse the FACTS embedded computersAttempt to disrupt the communication between FACTS embedded computersConfuse 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
CONTROLAREA A CONTROL
AREA B
A 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 controllerswith ideal observability
Uncontrollable modes in generator speeds due to device interactions
Project Benchmarks
Construction of HILDemonstration of Cascading FailuresPlacement and ControlHardware/Software ArchitectureCyber Fault DetectionDynamic ControlVisualization
Special Thanks
Imre Gyuk – DOE Energy StorageStan Atcitty – Sandia National LabJohn Boyes – Sandia National Lab