Kamalasadan presentation02

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

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
03/21/12 Sukumar Kamalasadan Ph.D. 2

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

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

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.


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.


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.


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.

Part I: Design Concepts:
Intelligent Adaptive Control : Supervisory Loop
Approach

Adaptive Controller
(Controller 1) Reference Model/ Reference
Output
Parameter Estimation
Error

Input Adaptive System
Signal Control Law Under Consideration
Plant
Output


Part I: Design Concept: Hybrid Intelligent
Control Architecture


Part I: Design Concept: System-Centric
Controllers: Design Scenarios
Fuzzy Reference Monitor Fuzzy Reference Monitor
Model Generator Model Generator

Adaptive Σ Multi-machine
Adaptive System
Σ Multi-machine Controller
Controller
System
Monitor

RBFNN
Figure 1: Scenario 1: Proposed Framework Controller
Figure 2: Scenario2: Proposed Framework
Hypothesis for System-Centric Controllers

• Changes in Modes of Operation: Fuzzy Reference Model Fuzzy Reference Model
Generator (FRMG)
Monitor
Generator (FRMG).
• Nonlinear Behavior (ability to cope up with system
nonlinearity) but the target of operation known: RBFNN Adaptive System under
Σ consideration
Controller (with supervisory learning). Controller
• Nonlinear Behavior and target unknown: Reinforcement
learning. Monitor

Challenges RBFNN Creative
• Controller’s Integrity, Design and Development Issues Controller Controller
• Implementation Issues, continuous-discrete interplay
Figure 3: Scenario 3: Proposed Framework


Part I: Design Concept: Controller 1
The Model Reference Adaptive Controller can be formulated as
U ad = θ T ω Where theta is
Adaptation Regressor
[
Θ = k θ 0 θ 1T θ 2] and omega is
T T
ω= r [ yp ω 1T ω2
T
] T
Start
•
and •
k = −γ 3ϑe sgn( K p )r (t ) θ 0 = −γ ϑe sgn( K ) y •
2 p
T
θ 1 = −γ 1e sgn( K p )ω1T Calculate error from
•T •
θ 2 = −γ 1e sgn( K p )ω 2 ω 1 = Λω + LU
T • outputs
1 ad ω 2 = Λω2 + Ly
where, e represents the error, ϑ represents the fuzzy contribution Adaptive Mechanism
γ represents the adaptive factor Calculate theta
Λ Is a stable matrix of order (n-1) X(n-1)
such that sI − Λ = Z m (s ) Calculate Omega
L LT = [ 0, 0,... 1]
Calculate control value
1) Model based design, 2) Adaptation capability, however no
memory, no learning 3) Able to expand to the next level for
plant drastic changes Continue


Part I: Switching Mechanism– Design
Concept
Fuzzy system can be represented as
r

∑r µ i i
f ( Ω) = i =1
r
= M T Pϑ = Φ * ϑ Start
∑µ
i =1
i

A reference model in a state space form will be System Auxiliary States
Wm H ( s) = ym H (t ) / r (t ) = Km H * Zm H / Rm H
Modal transitions can be included as Fuzzy Logic Scheme
Wm H ( s ) = f (Ω) * ( Km H * Zm H / Rm H ) Fuzzification
In general it can be written
as ∧ ∧
y m H (t ) = ν (Φ i ,ϑi ,Wm H ) Rule Base
 ∂eref  ∧
J = min
 ∂f 

eref = ymi (t ) − y mH (t )
Defuzzification
 
1) Multiple Model switching, 2) Stable 3) Able to work
coherently with model based adaptive controller 3) Need offline Reference Model
design and knowledge base development

Further details: Kamalasadan et al. (2004), (2005), (2006), (2007)

Part I: Design Concept:
Growing Dynamic RBFNN Controller
X1 Bias Existing Node
μ1 α11 Movement
y1
X2 .
New
μ2 Node
αp1.
. .
. . .
yp
. .
Xn Train offline- Adaptive Online
μn Bias
σ
Input Layer Hidden Layer Output Layer Sample Basis Function
μ
Static
Network
Nodal Region μ=Center positions
h=hidden neurons
Active Nodes Number of
nodes σ=Gaussian functions
Center
required for α=Weights
a Static
Movement
Network ε= Distance
y(t)
e=yi-f(xi)

Part I: Controller 2– Design Concept
node
The neuro-controller
can be written as
U nn = ∑ ακ (exp− (1 /(σκ )
j =1
2
) || Xi − µκ || 2 )
Start
εi = max{ε max γ i , ε min}, (0 < γ < 1)
Growth parameter criterion i Get System States
e i
rms = sqrt ∑ i − (nw − 1)e e)
T

j =1

RBFNN Structure
Adding hidden units: if || ei ||> e min and (|| Xi − µi ||) > ει
and erms > emin Generate Nodes

Add new unit with α(h+1)=ei, μ(h+1)=Xi, Calculate Centers and radii

σ(h+1)=k||Xi-μ||
Calculate distance and Output
Tuning laws are W = W ( i − 1) + Kiei
Update Weight and
Generate Control Value
Ki = Pi − 1Bi[ Ri + Bi T Pi − 1Bi ] −1
Where P is positive definite matrix and B is the gradient No
Grow or prune?
1) Function approximation based design, 2) Learn offline,
Adaptation online, associative memory 3) nonlinear and Yes
supervisory learning 4) Unique algorithm that can grow and Growing and
prune and provide sequential learning 5) Able to expand to the Pruning Stage
next level for optimal control/reinforcement learning

Part I: Creative Controller
DHP based controller
Critic Error

Action update


Part I: Under nonlinear Optimal Condition??
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)
Shaping (Prediction)
X(t-Δt) Transport lag
TDL
Supervisory Learning F-1(X,Xd) Scheduler Block
(Earlier Designs) (a=kaE+(1-k)aS)
Vref X(t)
X(t) Exploration Plant
+
J(t)
Action Network + Critic Network 1.0
A(t)
X(t) TDL

Dynamic Programming, Given U (utility function), solve the Bellman
Equation to get J; use J to calculate optimal actions
J ( X (t )) = max[U ( X (t ), u (t ))+ < ( J ( X (t + 1)) > /(1 + r )


Part I: Overall algorithmic functional flowchart
Current Status Start

Multi-machine Power
 Performed theoretical analysis including Disturbances/ System
Uncertainties/
stability while switching for MRAC with Constraints Return

FRMG block. Is error >
Threshold No
 Developed algorithms for adaptive

Adaptive Mechanism
Yes
controller and design basis for FRMG.
 Performed theoretical analysis including Model Adaptive
Controller
RBFNN
Controller
stability for MRAC-FRMG block with
Fuzzy Model
Supervisory Learning (SL), RBFNN Generator
Controller Output

controller. Is Output Desirable?
Yes
Developed algorithms for a novel RBFNN

J function minimization
 No
Yes
controller. Limit Reached?

No
 Developed the novel supervisory loop
No
based algorithms. Input or Output
Constraints?

Current theoretical Work Yes
Creative Control

 Analyze the strategy for creative controller Return

using dynamic programming in presence of
optimal conditions

Part II: Control of Synchronous Generator
Single Machine Infinite Bus System (SMIB)
Representation of System Dynamics
m
Governor
•
x H = f H ( x,θ ) + ∑ z H j ( x,θ )u j Δω
j =1 Pref
y H (t ) = hH ( x, θ ) T G Z=Re+jXe
0 = g ( x, y ) Vt
- + V
Generator Model ref Ut
Exciter
x = [δ ∆ω id if iq] + +
d AVR Σ CPSS
V = − RI − ωGI − L I Upss
dt +
 Vd  Uad MRAC
 Rd + Re 0 0  Id 
  R= 0  I = I 
V = − V f   Rf 0   f FRMG
 0  Iq 
 Vq   0 Rq + Re 
  
 
 0 0 Lq + Le   L d + Le kM f 0 
1
3
[
Te = ( Ld − Lq ) I d I q + kM f I q I f ] 
G= 0 0
− ( Ld + Le ) − kM f
0 
 
L =  kM f Lf 0 


 0  
 0
 0 L q + Le 


Vq = 3V∞ cos δ + Re I q + Le I q + ωLe I d 
Vd = − 3V∞ sin δ + Re I d + Le I d + ωLq I q 2 Hω Bω − Dω = Tm − Te



Part II: Design and Implementation:
Modeling of Power System Components (SMIB)
I d  
 0 0 I
   
   [− L −1
( R + ωG ) ] 0   d   −1 
0 I
I f    f  − L V 
Iq  = 
 0 0  I  +  
   Ld I q kM f I q Lq I d D  q   
 ω  − 6 Hω
 −
6 Hω B 6 Hω B
−
2 Hω B
0  ω   Tm 
 δ      
0  δ   − 1 
B
   0  0 0 0 

Conventional Power System Stabilizer (CPSS) Model Exciter Model
1 + sT 1 1 + sT 1 1 + sTw IEEE Type I exciter
Kstab
1 + sT 2 1 + sT 2 1 + sTw
+/-0.8 p.u.

E fd =
1
τe
[
K A (Vt − Vr ) − E fd ]
T1=0.2 T2=0.2 Tw=10s Kstab=8

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 ω n 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 ω n


RBFNN Design and off-line learning
Training of RBFNN network
 At first a Pseudo Random power deviation ΔPref and
exciter input deviation while CPSS in place (ΔVfield ) 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 (ΔVt) are saved.
 These values are time delayed by one, two and
three time periods. These time delayed signals are ΔP ΔVt
the inputs to the RBFNN network. RBFNN
ΔVf δ
 Initially 10 hidden neurons are used and 2000 such
samples are included. TDL
 RBFNN then estimate speed deviations and terminal  At this point the nodes growth and
voltage deviation for the subsequent period pruning is not performed.
(projection).  These steps are repeated until the
 The output is then compared with the generator error is minimized to a threshold value
output. The difference is the error signal.  Once the error reaches the threshold
 The error signals are used to calculate change in value the network is used for online
weight, width and RBFNN centers. post-control phase learning.

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 RBFNN
output is calculated.
 Step 5: Gradient is fed back to RBFNN and W is
updated.  Step 8: Reference model is updated
 Step 6: Based on this error, centers and width are based on the fuzzy tuning and
updated. requirements of the plant investigating
 Step 7: MRAC control signal is calculated based on the the monitor module.
delayed input from adaptive mechanism and applied to  Step 9: Error calculations are
the plant along with RBFNN signal. performed


Part II: Design and Implementation
Case 1: Simulation Results
Case 1 System Operating Conditions
Time Disturbance
 As the power system stress is not known (sec)
a multiple disturbance profile is used. It 0.1 Three Phase Fault
can cause small signal or transient 10 25% Mechanical Power
instability. Increase

 The purpose is to assess the stability and
the deviation of all parameters.
 Main parameters under observation are
angle, speed, voltage and power.
•Power =0.83 pu. − 1.2296
 Small signal stability can cause local 2.2349 
•Power Factor= 0.85  
mode oscillations and this test can show- lag. 0.748 
 
case oscillatory or non-oscillatory •Terminal 1 
instability. 1.0472 
Voltage=1.062 pu.  
•State Initial Conditions 0
 

 Figures shows that oscillations are
greater in the presence of PSS alone and
the Adaptive with FRMG could damp
these oscillations effectively.

Voltage in p.u.


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.


Part II: Control of Two Machine Infinite Bus
System


System
Case 3
Machine Parameters

Both machines P=0.8 and Q=0.4 p.u.
100ms short circuit in bus 2-3

Machine Operating points


System

100ms short circuit in bus 2-3

Part II: Control of Two area Power System

Power System

Shaping (Prediction)
X(t-Δt) TDL Transport lag
Supervisory Learning
F-1(X,XdScheduler Block
)
(Earlier Designs)
(a=kaE+(1-k)aS)
Vref Plant X(t)
X(t) Exploration +
J(t)
Action Network + Critic Network 1.0
A(t)
X(t) TDL
University of West Florida,
Copyright © 2009 3/8/2009 36

Part II: Control of Two area Power System

University of West Florida, Copyright © 2009
3/8/2009 37

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.


Part III: Smart Grid Applications

3/8/2009 39


Smart Controllers

3/8/2009 40


3/8/2009 41


3/8/2009 42

Part III: Wide Area Monitoring and Control

3/8/2009 43

Part III: Wide Area Monitoring and Control

Wide Area Controller (WAC)

Bus 9
P45 P25 P78 P16 P46 ω2 ω3 ω4 STATCOM
Bus 1 Bus 4
Infinite Bus Vref

Bus 5

Bus 2 Bus 10 Gen 2 Vref
Bus 3
Vref
Gen 4 Bus 11
Bus 6 Bus 12
Gen 3
Bus 7 Bus 8


Part III: Wide Area Monitoring and Control:
Scalability: Supervisory Loop Approach
Intelligent Area 1 PMU Area 2
Control PMU Intelligent
Control

Intelligent PMU
PMU Intelligent
Control
Intelligent PMU Control
PMU Intelligent
Control Control
Agent

PMU Energy PMU
Intelligent Intelligent
Control Area 3 Management Area 4 Control
ANN Agent Center Wide Area Controller
Voltage Stability
Assessment Tool 45
03/21/12 Sukumar Kamalasadan Ph.D.

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


Part III: Distributed Power Generation
and Grid Interface

Micro-grid and Interface

3/8/2009 47

and Grid Interface

Fuel Cell Model

3/8/2009 48

and Grid Interface

PV System Model

3/8/2009 49

and Grid Interface

Islanding Mode Connected to the Grid

3/8/2009 50

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)


Research Collaborations
 Areas  People
 Mathematical Modeling of  Graduate students who
physical systems such as power
systems, energy systems, are interested in these
avionics and robotics.
 Developing computer algorithms
area
in the form of control,
optimization, identification of
 Research faculty who
systems through mathematical are interested in
models
 Developing computational collaborations.
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


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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