Improving Power Transmission
Efficiency and Reliability through
Hardware/Software Co-Design
Natural
Faults
B. McMillin, M. L. Crow, D. Tauritz, F. Liu, B.
Chowdhury, and J. Sarangapani
Department of Computer Science
School of Materials, Energy & Earth Resources
Department of Electrical and Computer
Engineering
Intelligent Systems Center
University of Missouri-Rolla
FACTS
FACTS
Physical
Attack
filpower.umr.edu
NSF MRI CNS-040869
Sandia National Lab
Problem Motivation
„
Prevent Cascading
failures:
„
„
2003 Blackout
Causes
„
„
Physical & Cyber
contingencies
Deliberate disruption
„ Hackers
„ Terrorist Activity
Proposed Solution
Flexible AC Transmission Systems (FACTS)
„
„
Power Electronic Controllers
Means to modify the power flow through a
particular transmission corridor
Decentralized Infrastructures
„
Communication and
coordination
„
„
„
„
Operating - Distributed LongTerm control
Dynamic – Local Dynamic
control
Vulnerabilities of the
combined physical/ cyber
system
Recovery and protection
from physical faults and/or
cyber attacks and/or human
error
S.Tiffin
41
West End
40
Howard
42
Cascading Scenario
Outage 48-49
NwLibrty
39
EastLima
37
38
34
Rockhill
36
35
WMVernon
44
43
54
N.Newark
S.Kenton
45
Zanesvll
48
50
Sterling
WLima
47
46
W.Lancst
49
51
Philo
G
Crooksvl
66
MuskngumN
Trenton
23
CollCrnr
Sargents
73
G
62
Natrium
65
MuskngumS
G 68
24
69
72
Hillsbro
Portsmth
SpornE
SpornW
71
NPortsmt
TannrsCk
G
Summerfl
67
Portsmth
70
Bellefnt
74
SthPoint
75
G
64
Kammer
S.Tiffin
41
West End
40
Howard
42
Cascading Scenario
Outage 48-49
NwLibrty
39
EastLima
37
38
34
Rockhill
36
35
WMVernon
44
43
54
N.Newark
S.Kenton
45
Zanesvll
48
50
Sterling
WLima
47
46
W.Lancst
49
51
Philo
G
Crooksvl
66
MuskngumN
Trenton
23
CollCrnr
Sargents
73
G
62
Natrium
65
MuskngumS
G 68
24
69
72
Hillsbro
Portsmth
SpornE
SpornW
71
NPortsmt
TannrsCk
G
Summerfl
67
Portsmth
70
Bellefnt
74
SthPoint
75
G
64
Kammer
S.Tiffin
41
West End
40
Howard
42
Cascading Scenario
Outage 48-49
NwLibrty
39
EastLima
37
38
34
Rockhill
36
35
WMVernon
44
43
54
N.Newark
S.Kenton
45
Zanesvll
48
50
Sterling
WLima
47
46
W.Lancst
49
51
Philo
G
Crooksvl
66
MuskngumN
Trenton
23
CollCrnr
Sargents
73
G
62
Natrium
65
MuskngumS
G 68
24
69
72
Hillsbro
Portsmth
SpornE
SpornW
71
NPortsmt
TannrsCk
G
Summerfl
67
Portsmth
70
Bellefnt
74
SthPoint
75
G
64
Kammer
S.Tiffin
41
West End
40
Howard
42
Cascading Scenario
Outage 48-49
NwLibrty
39
EastLima
37
38
34
Rockhill
36
35
WMVernon
44
43
54
N.Newark
S.Kenton
45
Zanesvll
48
50
Sterling
WLima
47
46
W.Lancst
49
51
Philo
G
Crooksvl
66
MuskngumN
Trenton
23
CollCrnr
Sargents
73
G
62
Natrium
65
MuskngumS
G 68
24
69
72
Hillsbro
Portsmth
SpornE
SpornW
71
NPortsmt
TannrsCk
G
Summerfl
67
Portsmth
70
Bellefnt
74
SthPoint
75
G
64
Kammer
Context Object Diagram of FACTS
Power System
num_generators
num_lines
num_FACTS
num_buses
total_power
type
name
cause
affects : Event
controls : Event
Service
Provider
(Utility)
Event Receptor
Contingency
provides
initiates
FACTS Power
System
supply_power()
reconfigure()
run_FACTS_placement()
Voltage stability, no
overloads, flow balance,
availability
Redirect Power Away from
Overloaded Lines
„
FACTS Device Controls Power Flow
in an individual transmission line.
*
C o m m u n ic a t io n
F A C T S D e v ic e
Em bedded
C o m p u te r
L o w V o lta g e C o n tr o l
S y s te m
*
P o w e r L in e
H ig h V o lta g e P o w e r
C o n v e r s io n S y s te m
C o n t r o lle d L in e :
P o w e r L in e
*
FACTS Power System Object
Decomposition
limits, monitors
Neighbors
manipulates
setpoint
FACTS
Device
flows
capacities
generations
loads
change_setpoint(newSetpoint
)
control_power_line()
reconfigure()
supply_power()
flow balance
Power
Transmission System
places
senses
initiates
locations
Event Receptor
Placement
compute_locations()
Placement is optimized
affects:
Event
AG[For each line
-capacity<= flow <= capacity
{checked by FACTS
}]
-----------------------------AG[For each bus
sum of lines.flow is0.
{checked by FACTS
}]
-----------------------------AG[For each load
load is greater than or equal 0.
to
{checked by FACTS
}]
uses
affects: Event
provides
FACTS Control
„
Distributed Long-Term control
algorithms for FACTS settings
„
„
„
Run by each processor in each FACTS
Alternatives
„ Max-flow algorithms
„ Local optimizations
„ Agent-based framework
Assessment
„ Reduction of Overloads
„ Computability
System Dynamic
Control
Power Network Embedded With
FACTS Devices
Tie-line flow
CONTROL
AREA A
CONTROL
AREA B
While the FACTS devices offer improved controllability, their
actions in a decentralized power network can cause deleterious
interactions among them.
Uncontrollable modes in generator
speeds due to device interactions Control based on local information only
Performance of FACTS controllers
with ideal observability
Control Issues
„
Can we get global information?
„
„
„
Incomplete information
Time-delayed information
Opens the potential for increased
security issues
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 Systems
Inject Simulated Faults
Interconnected laboratory-scale UPFC
FACTS Device
„
Measure actual device interaction
FACTS Power System Object
Decomposition
Hardware/Software
FACTS Interaction Laboratory
uses
senses
sensor_data
power_system_config
generation_setpoint
affects : Event
Simulation Engine
configure_simulation()
accept_contingency()
send_sensor_data()
set_HIL_line()
compute_power()
HIL Line
sets
manipulates
limits, monitors
change_HIL_Line_flow
()
FACTS Interaction Laboratory
Architecture
230 kV
345 kV
500 kV
35
33
32
31
30
FACTS/
ESS
74
80
79
66
75
78
72 6976
vv
77
82
81
10 KVA
156 157 161162
112
114
5
167
165
158159
11
6
45160
115
166
Simulation
Engine
(multiprocessor)
A/D D/A
155
44
A/D D/A
83
A/D D/A
36
8485
86
163
118
FACTS FACTS/
12 13
108
109
119
107
110 104
63
103
37
64
143
154
138139
FACTS/
ESS
9
14
147
142 146
153 151 145
136
49
47
140 152
150
141
149
57
43 42 50
10256
48
18
8 17
7
3
4
ESS
19
16 15
Network
FACTS Interaction Laboratory
UPFC
Simulation
Engine
HIL Line
Cyber Fault
Detection
Fault Tolerance
„
„
„
Define correct operation of the
power system with FACTS
Embed as executable
constraints into each FACTS
computer
FACTS check each other during
operation of distributed control
algorithms – State
Dissemination
Some Basic Constraints
„
Constraint 1
„
„
Power flow into a bus = power flow out of a bus
Constraint 2
„
Line Power Flow ≤ Maximum Line capacity.
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)
Project Benchmarks
„
„
„
„
„
„
„
Construction of FIL
Demonstration of Cascading Failures
Placement and Control
Hardware/Software Architecture
Cyber Fault Detection
Dynamic Control
Visualization