Kamalasadan presentation02

Next Generation Adaptive and Intelligent Algorithms for the Control of Complex

and Dynamic Systems

Dr. Sukumar KamalasadanDepartment of Engineering and Computer Technology

University of West Florida

Pensacola, FL-32514

Sukumar Kamalasadan Ph.D. 203/21/12

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


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)

SystemUnder Consideration

PlantOutput

Error

InputSignal

ReferenceOutput

AdaptiveControl Law

Reference Model/ Parameter Estimation


Part I: Design Concept: Hybrid Intelligent Control Architecture


Part I: Design Concept: System-Centric Controllers: Design Scenarios

Fuzzy Reference Model Generator (FRMG) Monitor

Adaptive Controller

System under considerationΣ

Monitor

RBFNNController

CreativeController

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

RBFNNController

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


Part I: Design Concept: Controller 1

[ ]TTTk 210 θθθ=Θ ][ TTTpyr 21 ωωω =

Λ Is a stable matrix of order (n-1) X(n-1) such that )(sZsI m=Λ−

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

)()sgn(3 trKek pϑγ−=•

Tp

T

Ke 111 )sgn( ωγθ −=•

Tp

T

Ke 212 )sgn( ωγθ −=•

yKe p )sgn(20 ϑγθ −=•

adLU+Λ=•

11 ωω Ly+Λ=•

22 ωω

ωθ TadU =

L [ ]1,...0,0=TL

ϑ

γ

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


Part I: Switching Mechanism– Design Concept

ϑϑµ

µ*)(

1

1 Φ===Ω∑

∑

=

= PMr

f T

r

ii

r

iii

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),,()( HmiiHm Wty ϑν Φ=∧∧

∂

∂=

f

eJ refmin )()( tytye mHmiref

∧−=

HHHHH RmZmKmtrtymsWm /*)(/)()( ==

)/*(*)()( HHHH RmZmKmfsWm Ω=

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)


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 NetworkNodal Region

Train offline- Adaptive Online

μ1

μ2

μn

.

.

.

.

.

....

Bias

Bias

y1

yp

μσ

Sample Basis Function

X1

X2

Xn

α11

αp1

Input Layer Hidden Layer Output Layer

μ=Center positionsh=hidden neuronsσ=Gaussian functionsα=Weightsε= Distancee=yi-f(xi)


Part I: Controller 2– Design Concept

∑=

=

=

−−

<<,i

j

Twrms

i

ii

eenie1

minmax

))1(sqrt

)10(,max γεγεε

∑=

−−=node

j

inn XU1

22 )||||))/(1((exp κκκ µσα

minrms

min

ee and

||)(|| and||||

>>−> ιεµiii Xeeif

The neuro-controller can be written as

Adding hidden units:

111 ][ −

−− += iiTiiiii BPBRBPK

Tuning laws are

Where P is positive definite matrix and B is the gradient

Growth parameter criterion

Add new unit with α(h+1)=ei, μ(h+1)=Xi,

σ(h+1)=k||Xi-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

iii eKWW += − )1(


Part I: Creative ControllerDHP based controller

Action update

Critic Error


Part I: Under nonlinear Optimal Condition??

Plant

Critic NetworkAction Network

Supervisory Learning (Earlier Designs)

Scheduler Block (a=kaE+(1-k)aS)

Vref

+

+

X(t)

A(t)

Exploration

Shaping (Prediction)

J(t)1.0

TDL Transport lag

X(t)

X(t)

X(t-Δt)

F-1(X,Xd)

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)

)1/())1((())(),((max[))(( rtXJtutXUtXJ +>+<+=

Dynamic Programming, Given U (utility function), solve the BellmanEquation to get J; use J to calculate optimal actions


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 M

echan

ism

Controller Output

Limit Reached?

Return

Disturbances/Uncertainties/

Constraints

Start

Multi-machine Power System

Is error > Threshold

Adaptive Controller

Is Output Desirable?

Model

Fuzzy ModelGenerator

RBFNN Controller

Input or Output Constraints?

Creative Control

Return

J function m

inim

ization

No

No

No

No

Yes

Yes

Yes

Yes


Part II: Control of Synchronous Generator Single Machine Infinite Bus System (SMIB)

]i i i[ qf d ∆ = ωδx

Pref

Governor

Z=Re+jXeGT

Exciter

CPSS

MRAC

FRMG

AVR

VtVref

- + Ut

Σ

Δω

+

+

+Upss

Uad

),(0

),()(

),(),(1

yxg

xhty

uxzxfx

HH

m

jjjHHH

==

+= ∑=

•

θ

θθ

Generator Model

Representation of System Dynamics

Idt

dLGIRIV −−−= ω

−=

q

f

d

V

V

V

V

+

+=

eq

f

ed

RR

R

RR

R

00

00

00

( )[ ]fqfqdqde IIkMIILLT +−=3

1

=

q

f

d

I

I

I

I

−+−

+=

0)(

000

00

fed

eq

kMLL

LL

G

+

+=

eq

ff

fed

LL

LkM

kMLL

L

00

0

0

qqdeded ILILIRVV ωδ +++−= ∞sin3deqeqeq ILILIRVV ωδ +++= ∞

cos3 emB TTDH −=− ωωω 2


Part II: Design and Implementation:Modeling of Power System Components (SMIB)

Conventional Power System Stabilizer (CPSS) Model

11 sT+

21 sT+

11 sT+

21 sT+

wsT+1

wsT+1Kstab

+/-0.8 p.u.

Exciter Model

IEEE Type I exciter

( )[ ]fdrtAe

fd EVVKE −−=τ1

[ ]

−

−+

−−−

+−

=

−

−

100000

02666

00

00)(

00

11

m

q

f

d

BB

dq

B

qf

B

qdq

f

d

T

VL

I

I

I

H

D

H

IL

H

IkM

H

IL

GRL

I

I

I

δω

ωωωω

ω

δω

T1=0.2 T2=0.2 Tw=10s Kstab=8


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

nω

nω


Part II: Design and Implementation: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 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.

RBFNNΔPΔVf

TDL

ΔVtδ


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


Part II: Design and ImplementationCase 1: Simulation Results

System Operating ConditionsTime (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

−

0

0472.1

1

748.0

2349.2

2296.1

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.



Vol

tage

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



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



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 NetworkAction Network

Supervisory Learning (Earlier Designs)

Scheduler Block (a=kaE+(1-k)aS)

Vref

++

X(t)

A(t)

Exploration

Shaping (Prediction)

J(t)1.0

TDL Transport lag

X(t)

X(t)

X(t-Δt)F-1(X,Xd)

TDL


Part II: Control of Two area Power System


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



Smart Controllers


43

Part III: Wide Area Monitoring and Control



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

STATCOMP45 P25 P78 P16P46

Vref

Vref

Vref

ω2 ω3 ω4


Part III: Wide Area Monitoring and Control: Scalability: Supervisory Loop Approach

Energy Management

CenterANN Agent

IntelligentControl

IntelligentControlIntelligent

Control

IntelligentControl

IntelligentControl

IntelligentControl

IntelligentControl

IntelligentControl

PMU

Agent

PMU

PMU

PMU

PMUPMU

PMU

PMU

Voltage StabilityAssessment Tool

Area 1 Area 2

Area 3 Area 4Wide Area Controller


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


Micro-grid and Interface


Fuel Cell Model



PV System Model



Islanding Mode Connected to the Grid



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

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.


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.

Kamalasadan presentation02

Technology

Transcript of Kamalasadan presentation02