modern heuristic optimization techniques and potential applications to power system control mohamed...
TRANSCRIPT
Modern Heuristic Modern Heuristic Optimization Techniques Optimization Techniques
and Potential Applications and Potential Applications to Power System Controlto Power System Control
Mohamed A El-SharkawiMohamed A El-SharkawiThe CIA labThe CIA lab
Department of Electrical Department of Electrical EngineeringEngineering
University of WashingtonUniversity of WashingtonSeattle, WA 98195-2500Seattle, WA 98195-2500
[email protected]@ee.washington.eduu
http://http://cialab.ee.washington.educialab.ee.washington.edu
Heuristic Optimization Heuristic Optimization TechniquesTechniques
• Genetic AlgorithmsGenetic Algorithms• Evolutionary ProgrammingEvolutionary Programming• Swarm IntelligenceSwarm Intelligence• Particle SwarmParticle Swarm• DNA ComputingDNA Computing• Artificial LifeArtificial Life• Intelligent AgentsIntelligent Agents
Biocomputation
• The use of biological processes or behavior as metaphor, inspiration, or enabler in developing new computing technologies
• The field is highly multidisciplinary, Engineers, computer scientists, molecular biologists, geneticists, mathematicians, physicists, and others.
Nature is a Powerful Paradigm
• Brain neural networks• Evolution theory genetic algorithms• Flock of birds particle swarm
optimization• Insects swarm intelligence• ……• ……
Classical Control: Design
Systeminputs
ControlInputs
Constraints
Classical Control: Operation
Systeminputs
ControlInputs
Constraints
PSO Control
Systeminputs
ControlInputs
Constraints
PSO/NN Control
Systeminputs
ControlInputs
Constraints
Gradient Search vs MAS
Gradient Search MAS
Evolutionary Evolutionary AlgorithmsAlgorithms
Population Pool
1 0 0 1 11 1 1 11 0 0 000 0 11 1 1 0 00 0
...
Byte 1 Byte 2 Byte n
1
00 1 11 1 1 11 0 0 0 00 0 11 110 0 0 0
...
1 0 0 1 11 1 111 0 0 000 0 11 1 1 0 0
00
...
1 0 01
1
1 1 1 11 0 00 00 0 1
1
1 10 000
...
0
individual
#1
#2
#3
#K
2 n
Fitness Evaluation
#1
#2
#3
Individuals
1 0 0 111 0
00 111 0
1 0 0 11 1 0
1 0 01 11 0
0
#n
Fitness
Computations
f(.)Normalize
Ranked Individuals
#q
#p0 0 11 1 0
1 0 0 11 1 0
#p
#q
0
0 0 11 1 00
1 0 0 11 1 0
#1 1 0 0 111 0
1 0 01 11 0#3
#n 1 0 0 11 1 0
#2 00 111 00
Two-point Crossover
• Two crossover points are obtained by a random number generator
#p
#q0 0 11 1 00
1 0 0 11 1 0
Crossover 1
0 0
1 1
1
0
1 0
0 0
0 1
1#p
#q
Crossover points
1 2 1 2
Mutation
0 1 0 1 0 0 1
0 1 0 0 0 0 1
mutation
#p
#p
Particle Swarm Particle Swarm OptimizationOptimization
PersonalBest at previous step
Currentmotion
Component in thedirection of personal best
Component in thedirection of previous motion
Component in thedirection of global best
New Motion
Global best
Border (Edge) Identification
The Art of Fitness Function
• To find points anywhere on the boundary
Metric: |f(x)-boundary value|
Results - Case 1
The Art of Fitness Function
• Distribute points uniformly on the boundary
Metric: |f(x)-boundary value| -Distance to closest neighbor
(to penalize proximity to neighbors)
Results - Case 2
The Art of Fitness Function
• Distribute points uniformly on the boundary close to current state
Metric: |f(x)-boundary value| -Distance
to closest neighbor + Distance to current state
(penalize proximity to neighbors, penalize distance from current state)
Results - Case 3
Test SystemWSCC 179 Bus
System
Cascading event
Base Case61,411 MW
12,330 MVAR
31 30
80
78
74
79 65
77
76 72
82
81
86
83
v v
First Event – Initial Contingency
Three Phase fault on the line between John Day (#76) and Grizzly (#82)
Second Event
Trip the line between
John Day (#76) and Hanford (#78)
Third Event
Trip the line between
John Day (#78) and North 500 (#80)
Swarm IntelligenceSwarm Intelligence
Swarm IntelligenceSwarm Intelligence
=Coordination Coordination withoutwithout
Direct CommunicationDirect Communication
Swarm Intelligence
• Appears in biological swarms of certain insect species
• Interactions is indirect (stigmergy)
• The end result is accomplishment of very complex forms of social behavior and fulfillment of a number of tasks
A
B
C
D
G
E
F
AB 0.23
BC 0.11AB 0.23
CD 0.14BC 0.11AB 0.23
DE 0.15CD 0.14BC 0.11AB 0.23
A
B
C
D
G
E
F
AB 0.23
BC 0.11AB 0.23
CD 0.14BC 0.11AB 0.23
DE 0.15CD 0.14BC 0.11AB 0.23
FinisFinis