Next Generation Adaptive
and Intelligent Algorithms
for the Control of Complex
and Dynamic Systems
Dr. Sukumar Kamalasadan
Department of Engineering and Computer Technology
University of West Florida
Pensacola, FL

32514
Sukumar Kamalasadan Ph.D.
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Presentation Outline
Overview
Part I: Theoretical Design and Algorithms
Part II: Current Research Projects
Speed Control of Synchronous Generator.
Multi

Machine Power System Control and Angular Stability.
Part III: Other Research Projects and Directions
Smart

Grid Applications
Wide Area Monitoring and Control based on scalable intelligent
supervisory loop concept.
Distributed Power Generation Control and Grid Interface.
Summary
Overview
Main focus
Modeling and control of dynamic systems
Mathematical modeling
Using Computational Intelligence
Simulation using computer algorithms
Designing and developing novel control, optimization
and identification techniques
Real

time implementation of scalable algorithms
Integrating research elements to teaching
Dissemination and Outreach
This talk is about one particular dynamic system
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Overview:
Importance of Modern Power System Control
Fast acting MIMO devices such as generators, Distributed
Generation (DG) and their integration, tight and congested
transmission systems, deregulated power system …
Shows multiple behavior such as: discrete changes (transformer
taps), deterministic operations (voltage and speed control),
stochastic behavior (load forecasting), optimal needs (power
transactions with constraints).
Existence of multiple controllers that increases the system
complexity and controller interactions.
Advances in high speed digital processor and computer
architecture enhance the feasibility of modern control design
techniques:
Operates in real

time
Provide some elements of learning and adaptation
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Overview: Existing control topologies for
Generator/Power Systems
Linear controllers such as conventional Automatic Voltage Regulators (AVR)
(voltage control) and Governor (speed control).
Conventional Power System Stabilizers (CPSS) used for damping of
generator oscillations, used in industry (P. Kundur, O.P. Malik et. al.).
Model based controllers for generators (adaptive controllers) has been
proposed and used (adaptable and simple in architecture) (K.S. Narendra,
Ghandakly et. al.)
—
Provide linear adaptation but no learning and memory.
Nonlinear controllers and adaptive nonlinear controller (Feedback
Linearization, backstepping)
–
Useful but often cannot cover entire domain.
Neural network based designs (Venayagamoorthy, Harley, Lee)
—
Provide
learning and adaptation especially with time delayed system
—
Not always
needed.
Proposed Solution:
Provide hybrid control architecture that is system

centric
in nature.
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Overview: Intelligent Power System Control
and Analysis
Why Hybrid Intelligent Control Architecture?
Operates in a decentralized way while exhibiting
desirable system

wide characteristics (Complex tasks
can be made simpler).
Produces effective local decisions that contribute
towards a coherent and effective overall system
(Emerging behavior).
Ability to interact and coordinate with existing design
and are adaptable (organizational behavior).
Capable of providing efficient and effective signals
based on system needs (case based approach).
Provide adaptation, learning and model

less control.
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Overview: Current Research Efforts:
Focus Areas
Hybrid intelligent control

Theoretical formulations, design
and development such as,
Issues related to stability, adaptation and global contributions in
changing plant conditions.
Reliability, robustness and adaptability.
System modeling, algorithmic development, implementation.
New and Suitable computational intelligence techniques:
Methods in online and offline learning.
Issues such as tuning, autonomous action.
Power System Control and Stability
Generator control.
Wide Area Controllers (WAC).
Control of other electric machines.
Control of energy sources, integration of Distributed Generation
(DG) with mega grid.
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Part I: Design Concept: Hybrid Architecture
for Coordinated Control
Three Design structure with System Supervision
Systems that shows parametric uncertainty;
A conventional adaptive module (such as Model Reference
Adaptive Control) to adaptively monitor system output and develop
control action.
Systems that shows modal changes;
Intelligent module to recover these changes and develop a desired
reference model trajectory. Important in the presence of multiple
modes of operations.
Systems that shows functional changes and/or influenced
by external disturbances;
An intelligent module to approximate the changing nonlinear
function such as offline/online trained neural network.
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Part I: Design Concepts
:
Intelligent Adaptive Control : Supervisory Loop
Approach
Adaptive Controller
(Controller 1)
System
Under Consideration
Plant
Output
Error
Input
Signal
Reference
Output
Adaptive
Control Law
Reference Model/
Parameter Estimation
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Part I: Design Concept: Hybrid Intelligent
Control Architecture
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Part I: Design Concept: System

Centric
Controllers: Design Scenarios
Fuzzy Reference Model
Generator (FRMG)
Monitor
Adaptive
Controller
System under
consideration
Σ
Monitor
RBFNN
Controller
Creative
Controller
Figure 1: Scenario 1: Proposed Framework
Fuzzy Reference
Model Generator
Monitor
Adaptive
Controller
Multi

machine
System
Σ
Figure 2: Scenario2: Proposed Framework
Fuzzy Reference
Model Generator
Monitor
Adaptive
Controller
Multi

machine
System
Σ
Monitor
RBFNN
Controller
Figure 3: Scenario 3: Proposed Framework
Hypothesis for System

Centric Controllers
•
Changes in Modes of Operation: Fuzzy Reference Model
Generator (FRMG).
•
Nonlinear Behavior (ability to cope up with system
nonlinearity) but the target of operation known: RBFNN
Controller (with supervisory learning).
•
Nonlinear Behavior and target unknown: Reinforcement
learning.
Challenges
•
Controller’s Integrity, Design and Development Issues
•
Implementation Issues, continuous

discrete interplay
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Part I: Design Concept: Controller 1
Is a stable matrix of order (n

1) X(n

1)
such that
The
Model
Reference
Adaptive
Controller
can
be
formulated
as
Where
theta
is
and
omega
is
and
Start
Calculate error from
outputs
Adaptive Mechanism
Calculate control value
Continue
Calculate theta
Calculate Omega
where,
e represents the error,
represents the fuzzy contribution
represents the adaptive factor
1) Model based design, 2)
Adaptation capability, however no
memory, no learning 3) Able to expand to the next level for
plant drastic changes
Adaptation
Regressor
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Part I: Switching Mechanism
–
Design
Concept
Fuzzy
system
can
be
represented
as
A
reference
model
in
a
state
space
form
will
be
Modal
transitions
can
be
included
as
In
general
it
can
be
written
as
Start
System Auxiliary States
Fuzzy Logic Scheme
Defuzzification
Reference Model
Fuzzification
Rule Base
1) Multiple Model switching, 2)
Stable 3) Able to work
coherently with model based adaptive controller 3) Need offline
design and knowledge base development
Further details: Kamalasadan et al. (2004), (2005), (2006), (2007)
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Part I: Design Concept:
Growing Dynamic RBFNN Controller
New
Node
Existing Node
Movement
y(t)
Center
Movement
Number of
nodes
required for
a Static
Network
Active Nodes
Static
Network
Nodal Region
Train offline

Adaptive Online
μ
1
μ
2
μ
n
.
.
.
.
.
.
.
.
.
Bias
Bias
y
1
y
p
μ
σ
Sample Basis Function
X
1
X
2
X
n
α
11
α
p1
Input Layer
Hidden Layer
Output Layer
μ
=Center positions
h=hidden neurons
σ
=Gaussian functions
α
=Weights
ε
= Distance
e=y
i

f(x
i
)
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Part I: Controller 2
–
Design Concept
The
neuro

controller
can
be
written
as
Adding
hidden
units
:
Tuning
laws
are
Where
P
is
positive
definite
matrix
and
B
is
the
gradient
Growth
parameter
criterion
Add
new
unit
with
α
(h+1)
=e
i
,
μ
(h+1)=X
i,
σ
(h+1)=kX
i

μ

1) Function approximation based design, 2) Learn offline,
Adaptation online, associative memory 3) nonlinear and
supervisory learning 4) Unique algorithm that can grow and
prune and provide sequential learning 5) Able to expand to the
next level for optimal control/reinforcement learning
Start
Get System States
RBFNN Structure
Calculate distance and Output
Update Weight and
Generate Control Value
Generate Nodes
Calculate Centers and radii
Grow or prune?
Growing and
Pruning Stage
No
Yes
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Part I: Creative Controller
DHP
based
controller
Action
update
Critic
Error
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Part I: Under nonlinear Optimal Condition??
Plant
Critic Network
Action Network
Supervisory Learning
(Earlier Designs)
Scheduler Block
(a=ka
E
+(1

k)a
S
)
V
ref
+
+
X(t)
A(t)
Exploration
Shaping (Prediction)
J(t)
1.0
TDL
Transport lag
X(t)
X(t)
X(t

Δ
t
)
F

1
(X,X
d
)
TDL
Nonlinear dynamic programming for Reinforce Learning (RL)
RBFNN based supervisory learning (SL)
Coherency
Supervised Actor

Critic Reinforcement Learning Evolved
from (Rosentein, Barto et al, 2004)
Dynamic Programming, Given U (utility function), solve the Bellman
Equation to get J; use J to calculate optimal actions
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Part I: Overall algorithmic functional flowchart
Current Status
Performed theoretical analysis including
stability while switching for MRAC with
FRMG block.
Developed algorithms for adaptive
controller and design basis for FRMG.
Performed theoretical analysis including
stability for MRAC

FRMG block with
Supervisory Learning (SL), RBFNN
controller.
Developed algorithms for a novel RBFNN
controller.
Developed the novel supervisory loop
based algorithms.
Current theoretical Work
Analyze the strategy for creative controller
using dynamic programming in presence of
optimal conditions
Adaptive Mechanism
Controller Output
Limit Reached?
Return
Disturbances/
Uncertainties/
Constraints
Start
Multi

machine Power
System
Is error >
Threshold
Adaptive
Controller
Is Output Desirable?
Model
Fuzzy Model
Generator
RBFNN
Controller
Input or Output
Constraints?
Creative Control
Return
J function
minimization
No
No
No
No
Yes
Yes
Yes
Yes
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Part II: Control of Synchronous Generator
Single Machine Infinite Bus System (SMIB)
P
ref
Governor
Z=Re+jXe
G
T
Exciter
CPSS
MRAC
FRMG
AVR
V
t
V
ref

+
U
t
Σ
Δω
+
+
+
U
ps
s
U
ad
Generator Model
Representation of System Dynamics
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Part II: Design and Implementation:
Modeling of Power System Components (SMIB)
Conventional Power System Stabilizer (CPSS) Model
K
stab
+/

0.8 p.u.
Exciter Model
IEEE Type I exciter
T
1
=0.2 T
2
=0.2 T
w
=10s K
stab
=8
Sukumar Kamalasadan Ph.D.
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Part II: Design and Implementation:
Fuzzy Reference Model Generator Design
Knowledge Base Design
The membership function of the load torque is
defined over a domain interval of [0, 1.2].
The membership function of the electric power
is defined over a domain interval of [0, 1.5].
The membership function of is defined over
a domain interval of [0, 1.5].
Each membership function is covered by five
fuzzy sets.
The fuzzy rules are derived by studying and
simulating the response of the process.
25 fuzzy rules are used to perform the fuzzy
switching to evaluate the value of
Sukumar Kamalasadan Ph.D.
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Part II: Design and Implementation:
RBFNN Design and off

line learning
Training of RBFNN network
At first a Pseudo Random power deviation
Δ
P
ref
and
exciter input deviation while CPSS in place (
Δ
V
field
)
is generated using Matlab
®
environment. The input
are saved
.
These signals are then fed to the generator model.
The resulting output speed deviation in
δ
and the
output terminal voltage deviation (
Δ
V
t
) are saved.
These values are time delayed by one, two and three
time periods. These time delayed signals are the
inputs to the RBFNN network.
Initially 10 hidden neurons are used and 2000 such
samples are included.
RBFNN then estimate speed deviations and terminal
voltage deviation for the subsequent period
(projection).
The output is then compared with the generator
output. The difference is the error signal.
The error signals are used to calculate change in
weight, width and RBFNN centers.
At this point the nodes growth and
pruning is not performed.
These steps are repeated until the
error is minimized to a threshold value
Once the error reaches the threshold
value the network is used for online
post

control phase learning.
RBFNN
Δ
P
Δ
V
f
TDL
Δ
Vt
δ
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Part II: Design and Implementation:
Control and Model Development
Algorithmic Implementation
Step 1
: Plant output is used to calculate the regression
vector.
Step 2
: The output of the plant being fed to the error
block and the error between the plant output and the
reference model output is used to update the adaptive
mechanism. Adaptive vector theta is calculated
.
Step 3
: The FRMG monitoring changes in Pe and Te and
calculating values for omega at each time stamp. Based
on the error dynamics and the monitor block this is fed to
the reference model to update the model parameter
.
Step 4:
Input is being fed to RBFNN and the network
output is calculated
.
Step 5:
Gradient is fed back to RBFNN and W is updated.
Step 6:
Based on this error, centers and width are
updated
.
Step 7:
MRAC control signal is calculated based on the
delayed input from adaptive mechanism and applied to
the plant along with RBFNN signal
.
Step 8:
Reference model is updated
based on the fuzzy tuning and
requirements of the plant investigating
the monitor module
.
Step
9:
Error calculations are
performed
RBFNN
Sukumar Kamalasadan Ph.D.
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Part II: Design and Implementation
Case 1: Simulation Results
System Operating Conditions
Time
(sec)
Disturbance
0.1
Three Phase Fault
10
25% Mechanical Power
Increase
Case 1
Power =0.83 pu.
Power Factor= 0.85
lag.
Terminal
Voltage=1.062 pu.
State Initial Conditions
As the power system stress is not known
a multiple disturbance profile is used. It
can cause small signal or transient
instability.
The purpose is to assess the stability and
the deviation of all parameters.
Main parameters under observation are
angle, speed, voltage and power.
Small signal stability can cause local
mode oscillations and this test can show

case oscillatory or non

oscillatory
instability.
Figures shows that oscillations are
greater in the presence of PSS alone and
the Adaptive with FRMG could damp
these oscillations effectively.
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Part II: Design and Implementation
Case 1: Simulation Results
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Part II: Design and Implementation
Case 1: Simulation Results
Voltage in p.u.
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Part II: Design and Implementation
Case 1: Simulation Results
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Part II: Design and Implementation
Case 1: Simulation Results
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Part II: Design and Implementation
Case 2: Simulation Results
Case 2
In this experiment two intelligent loops viz, FMRG augments the MRAC and
RBFNN based neuro

controller is being used for Multiple Input Multiple Output
control of the system. The system is running under the following specifications:
Power =0.28 pu.
Power Factor= 0.24 lag.
Terminal Voltage=1.062 pu.
Conclusions:
Different operating points behaved differently. In the first case, RBFNN did not
provide much control contribution. With a change in operating point, the
contribution was noticeable. This confirms the need for such control.
In all these case system supervision concept performed better than individual
control.
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Part II: Design and Implementation
Case 2: Simulation Results
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Part II: Design and Implementation
Case 2: Simulation Results
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Part II: Design and Implementation
Case 2: Simulation Results
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Part II: Control of Two Machine Infinite Bus
System
Sukumar Kamalasadan Ph.D.
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Part II: Control of Two Machine Infinite Bus
System
Case 3
Both machines P=0.8 and Q=0.4 p.u.
100ms short circuit in bus 2

3
Machine Parameters
Machine Operating points
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Part II: Control of Two Machine Infinite Bus
System
100ms short circuit in bus 2

3
36
Part II: Control of Two area Power System
3/8/2009
University of West Florida,
Copyright © 2009
Power System
Plant
Critic Network
Action Network
Supervisory Learning
(Earlier Designs)
Scheduler Block
(a=ka
E
+(1

k)a
S
)
V
ref
+
+
X(t)
A(t)
Exploration
Shaping (Prediction)
J(t)
1.0
TDL
Transport lag
X(t)
X(t)
X(t

Δ
t
)
F

1
(X,X
d
)
TDL
37
3/8/2009
University of West Florida, Copyright © 2009
Part II: Control of Two area Power System
Sukumar Kamalasadan Ph.D.
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Part III: Other Research Projects and
Directions (Five year plan)
Smart Grid Applications
Real

time test bed for power system modeling and control.
Various projects.
Wide Area Monitoring and Control based on scalable
intelligent supervisory loop concept.
Theory, development and simulation studies.
Distributed Power Generation and Grid Interface.
Integrating Fuel

Cell and Micro

Turbine Models.
Control system development and assessment.
39
Part III: Smart Grid Applications
3/8/2009
University of West Florida, Copyright © 2009
40
3/8/2009
University of West Florida, Copyright © 2009
Smart Controllers
Part III: Smart Grid Applications
41
3/8/2009
University of West Florida, Copyright © 2009
Part III: Smart Grid Applications
42
3/8/2009
University of West Florida, Copyright © 2009
Part III: Smart Grid Applications
43
Part III: Wide Area Monitoring and Control
3/8/2009
University of West Florida, Copyright © 2009
Sukumar Kamalasadan Ph.D.
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10/29/2013
Part III: Wide Area Monitoring and Control
Wide Area Controller (WAC)
Bus 1
Bus 9
Bus 2
Bus 7
Bus 10
Bus 6
Bus 12
Bus 8
Bus 3
Bus 11
Bus 4
Bus 5
Infinite Bus
Gen 2
Gen 4
Gen 3
STATCOM
P
45
P
25
P
78
P
16
P
46
V
ref
V
ref
V
ref
ω
2
ω
3
ω
4
Sukumar Kamalasadan Ph.D.
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Part III: Wide Area Monitoring and Control:
Scalability: Supervisory Loop Approach
Energy
Management
Center
ANN Agent
Intelligent
Control
Intelligent
Control
Intelligent
Control
Intelligent
Control
Intelligent
Control
Intelligent
Control
Intelligent
Control
Intelligent
Control
PMU
Agent
PMU
PMU
PMU
PMU
PMU
PMU
PMU
Voltage Stability
Assessment Tool
Area 1
Area 2
Area 3
Area 4
Wide Area Controller
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Part III: Distributed Power Generation and
Grid Interface: Concept
Objectives
Intelligent control of distributed Generation
Control (measurement) strategies of voltage and speed of the DG
system based on intelligent controllers (agents)
Integration of renewable energy based power generation to the grid
Development of test bed and hardware in the loop experiments based
on simulations
Practical Implementation and Integration of the proven research
activities to power distribution grid and testing
General Conceptual Implementation of DG Grid Interface
Control station:
Supervisory controller for DG system including protection
Coordination with nearest substation
Database for power flow, generation and load dynamics
Intelligent agents interaction
47
Part III: Distributed Power Generation
and Grid Interface
3/8/2009
University of West Florida, Copyright © 2009
Micro

grid and Interface
48
3/8/2009
University of West Florida, Copyright © 2009
Fuel Cell Model
Part III: Distributed Power Generation
and Grid Interface
49
3/8/2009
University of West Florida, Copyright © 2009
PV System Model
Part III: Distributed Power Generation
and Grid Interface
50
3/8/2009
University of West Florida, Copyright © 2009
Islanding Mode
Connected to the Grid
Part III: Distributed Power Generation
and Grid Interface
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Current Research Support and Future
Considerations
Current Support
National Science Foundation CAREER Grant
(2008

2012)
Internal Grant from the University of West Florida
(UWF)
(2008

2009)
Under Consideration
Office of Naval Research (ONR)
NSF Power Control and Adaptive Network (PCAN)
NSF Course, Curriculum and Lab Improvement (CCLI)
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Research Collaborations
Areas
Mathematical Modeling of
physical systems such as power
systems, energy systems,
avionics and robotics.
Developing computer algorithms
in the form of control,
optimization, identification of
systems through mathematical
models
Developing computational
intelligence based (neural
network, fuzzy systems,
biologically inspired
computational intelligent
techniques) algorithms that can
augment traditional controllers.
Applying control, optimization
and identification algorithms for
dynamic systems models.
Real

time implementations
People
Graduate students who
are interested in these
area
Research faculty who
are interested in
collaborations.
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Summary
Intelligent Adaptive Controllers based on the supervisory
loop concept can be expanded to agent based control and
monitoring.
This approach is found to be scalable and useful for power
system control, identification and optimization.
Intelligent tool in the form of agents can be developed and
feasible for dynamic voltage stability assessment and
improvements.
These approaches are expandable to modular
technologies, DG control and grid interface, distribution
system and in reconfigurable and survivable modes.
For modern power system, these techniques would have
significant impact especially in the areas of power system
control, stability, reliability and security.
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