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Electric Machinery and Control Laboratory

Department of Electrical EngineeringNational Central University

2012-03-01

AdviserProfessor Faa-Jeng Lin

Electric Machinery and Control Lab, Department of Electrical Engineering, National Central University, Taiwan.

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Drive and Control of Wind Turbine System


Areas of Research

Intelligent control systems including fuzzy, neural network and

GA

Ultrasonic, synchronous and induction motor servo drives(rotating and linear)

Magnetic levitation

Piezoceramic actuator

Induction generator system Nonlinear and adaptive control

Power electronics

Microgrid and smart grid

DSP-based computer control systems and computer interface Digital and analog circuits, VHDL, Spice

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Awards

Excellent Research Award, National Science Council, Taiwan, 1993 to

2000.

Outstanding Research Professor Award, Chung Yuan ChristianUniversity, Taiwan, 2000

Excellent Young Electrical Engineer Award, the Chinese Electrical

Engineering Association, Taiwan, 2000.

The Crompton Premium Best Paper Award, the Institution of Electrical Engineers (IEE), United Kingdom, 2002.

Best Paper Award, Taiwan Power Electronics Conference, Taiwan,

2004~2006, 2009, 2011.

Outstanding Research Award, National Science Council, Taiwan, 2004.

Outstanding Research Professor Award, National Dong Hwa University,

Taiwan, 2004.

Outstanding Technology Award, Precision CNC Servo Competition,

Ministry of Education, Taiwan, 2004.

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Outstanding Professor of Electrical Engineering Award, the Chinese

Electrical Engineering Association, Taiwan, 2005.

Fellow, The Institution of Engineering and Technology (IET, formerIEE) 2007.

Distinguished Professor, National Central University, Taiwan, 2008.

Project for Outstanding Researcher, National Science Council, Taiwan,

2008. Best Paper Award, Applications Competition of Matlab/Simulink,

Taiwan, 2009.

Chair Professor, National Central University, Taiwan, 2010.

Outstanding Research Award, National Science Council, Taiwan, 2010.

Outstanding Automatic Control Engineering Award, Chinese Automatic

Control Society, Taiwan, 2011.

Awards

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Experimental Equipments

Wind-Turbine Emulator and

Induction Generator System

DSP+CPLD

FPGA Toggle

5

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Linear Induction

Motor

Five Degree-of-Freedom Active

Magnetic Bearing Control System

Magnetic Levitation

System

Two-Axis Motion

Control System


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Three-Phase Induction

Motor Drive

Linear Permanent Magnet

Synchronous Motor DriveHigh-Efficiency Permanent Magnet

Synchronous Motor Drive

Linear Induction

Motor Drive


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Three-Axis Stage with Linear

Ultrasonic Motors

Permanent Magnet Synchronous

Wind-Turbine Generator

Gantry Position Stage DSP28335 Based BLDC

Motor Drive System


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LiFePO4 Battery Gird-Connected Li-ion Storage System

DSP TMS320F28035

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Islanding Detection System PV Control System

PV Array ( 2 KW )


PV Array ( 10 KW )

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WTG Control System M-G Set


Sensorless Drive System for PMSM Compressor dsPIC 33FJ128MC708A

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Permanent Magnet Synchronous Motor

Drive System for Light Electric VehicleDSP TMS320F28335

DSP-Based Electric Power Steering Motor

Drive System

Six-Phase PMSM Fault Tolerant Control System

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Dynamic Signal Analyzer

Precision LCR Meter

Programmable AC Power Sources

Digital Oscilloscope

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Experimental EquipmentsDC Electronic Loads

Digital Oscilloscope (400MHz)

DC Power Supply (200V/25A)

Programmable AC Power Sources

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Three-Phase Power Analyzer

DC Power Supply (400V/25A)


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Function Generator

Industrial Computer


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Digital Filter

and

Differentiator

Rectifier

3-phase

220V

60Hz

-

L+

C

++

PWM

Inverter

Ramp Comparison

Current Control

Coordinate

Translator

SIN/COS

Generator

-

+

-

PositionLoop

Controller

Limiter

v

h

*

*

ds

qs

i

i

+Secondary

Time-Constant

Estimator

LIM

SpeedLoop

Controller

rTecos esin

aV bV

aT bT cT

ai

bi

d

v

ee

ev

slv

vn ppn

*

ai*

bi*

ci

*qsi

*

dsi*

dr

*v

d

*d

r

m

r

TL

drTs*1 )(

r

T

Research AchievementsRecurrent-Fuzzy-Neural-Network Control Linear Induction Motor Servo Drive Using Genetic Algorithm

System configuration of indirect field-oriented control LIM servo drive 17

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d

md

_

Recurrent Fuzzy

Neural Network

Controller

*qsi

me

Reference

Model

*d

LIM Drive System

FKDMs

1

s

1

+

_

dvFe

)(sHp

LF

Delta

Adaptation

Law

oiijijko w,,m,w 4

GeneticAlgorithms

rmw ,,,

dtd mpe

mpe

me

me

mpe

+

Control block of LIM servo drive with GA-based RFNN controller

Recurrent-Fuzzy-Neural-Network Control Linear Induction Motor Servo Drive Using Genetic Algorithm

Research Achievements

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Control block of FPGA-based LIM servo drive

Fuzzy Sliding-Mode Control Linear Induction Motor Drive Using FPGA

bi

LIM

encoder

rectifier

3-phase

220V

60Hz

L

C-

+

PWM

inverter

ramp

comparison

current

control

ai

aT bT cT

*

ai*

bi*

ci

encoder

signal

command

generator

fuzzy inferencemechanism

encoder

interface

24 MHz

CLK

D/A

converter

data & D/A

control signal

*

qsi

*

dsi

FPGA

timing control

integral switching

surface

sliding-mode

position controller

generator

1x

2x

1x

2x

S

S

fK

field-oriented control system

v co-ordinatetranslator

&

generator

)sin( e )cos( e

egenerator

fuzzy sliding-mode control& generator

data & D/A

controller

*

ai

*

bi

*

ci

1x

1x 2x

12

1212

12

12

12

12

12

1212

12

12

2

12


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Robust Fuzzy-Neural-Network Sliding-Mode Control for Two-Axis Motion Control System

Linear Scale

&Hall Sensor

X-Axis Motor

Drive

X-Y Table Using PMLSMs

axibxicxi

ayi

byi

cyi

DSP-Based Control Computer

D/A

Converter

1 2 3A4 5 6 B7 8 9 C* 0B#FD

1 2 3A4 5 6 B7 8 9 C* 0B#FD

LCD

Flash

EPROM

TMS

320C32

Parallel

Port

Encoder

Interface

axibxicxi

ayibyicyi

Y-Axis MotorDriveY-Axis

PMLSM

X-Axis

PMLSM

Linear Scale

&

Hall Sensor


DSP-based two-axis motion control system 20

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Robust Fuzzy-Neural-Network Sliding-Mode Control for Two-Axis Motion Control System

ReferenceModel

Auxiliary

Controller

)(tU

d

Adaptation

Laws

Robust

Controller

Field-Oriented ControlPMLSM Servo Drive

Sliding

Surface

dt

d

e

Fuzzy

Neural NetworkEstimator

Adaptive

Uncertainty

Estimation

mdd+

-

+

-

+

S

FNNW

)(tu

rU

RFNN Sliding-Mode Control System

)( tH

smW

,, smW ,,

ddt

d e


RFNN sliding-mode control system 21

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Frequency Control Induction Generator System Using Recurrent-Fuzzy-Neural-Network

aT

bT

cT

aT

bT

cT

dci

dcC

ai

bi

pL

aT

bT

cT

aT

bT

cT

uu vi ,

vvvi ,

ww vi ,

viui

ci

ai

bi

Load

aT bT cT

*ui

*wi

*vi

aT bT cT

*ai

*ci

*bi

e

sl

e

r

r

*qsi

dcP RFNN

*outP

dcvdci*dsi

*qsi

RFNN *dcP

3

*

uvv

Isolation and

Drive Circuits

Coordinate

Transformation

PMSM IG

converter

*dsi

Fuzzy

Modeling*dsi

uvv

Isolation and

Drive Circuits

Current Control

Circuits

Current Control

Circuits

Coordinate

Transformation

iT

i

dsr

qs

dtd

dcv

*

abv


Control block of induction generator system with RFNN control 22

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Frequency Control Induction Generator System Using Recurrent-Fuzzy-Neural-Network

rule

layer

membership

layer

input

layeri

k

o

wjk3

wko4

yi1

yj2

yk3

yo4

xi1

oiw

1

oiw

z1

output

layer

j


Structure of four-layer RFNN 23

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Synchronous Motor Drive System Using DSP and CPLD

Inverter

PWM1PWM2

PWM3

PWM4PWM5PWM6

ai

bi

ci

dsi

qsi

U

V

W

*

aV

*

bV

*

cV

*

dsi

*

qsi

Encoder

Interface

Circuit

u,v,w

a,b,z

Encoder &

Hall Signal

Detector

Rotor Initial

Angle Detector

WSiUSi

r

e

h

DSP TMS320

LF2407

r

*

r

Rectifier

CPLD ALTERA

10K100ARC240-1

Speed

Loop

Controller

+

PI

PI

*

qsV

*

dsV0

-

-

+

-

+

+

-

3-phase

220V

60Hz

SIN/COS

Generator

A/D

A/D

32

23

SPWM

Current

Process

Circuit

Filter

Circuit


System configuration of field-oriented control PMSM servo drive 24

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Synchronous Motor Drive System Using DSP and CPLD


System block of PMSM servo drive 25

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X-Y- Motion Control Stage Using Linear Ultrasonic Motors

System block of X-Y- motion control stage

Motor Drives

Pentium 4 Memory

Interface


LUSMEncoderX

*

xV

YX

PC

EncoderY

Encoder

*

yV*

V

LUSM

LUSM`

YX

X-Axis

Y-axis

-Axis

Servo Control Cards 3

EncoderI/OADC

Motor Drives

Pentium 4 Memory

Interface


LUSMEncoderX

*

xV

YX

PC

EncoderY

Encoder

*

yV*

V

LUSM

LUSM`

YX

X-Axis

Y-axis

-Axis

Servo Control Cards 3

EncoderI/OADC


26

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Robust Sugeno type adaptive fuzzy neural network (SAFNN) backstepping control system

TU dmd

+

_

)(1 tz )(2 tz

)(1 tz

md

v

Stabilizing

Function &)(2 tz

Single-axis Motion Mechanism

Using LUSM Drive

dtd

dtd

Nominal Model

Design

hU

nU

d

md

+

v

SAFNN

AdaptationLaw

Compensator &

Adaptation Law

+

)(1 tz

)(2 tz

)(2 tz

W

rU

)(1 tz

-


27

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28Structure of Sugeno type adaptive fuzzy neural network (SAFNN)

1

1k1

2kl

k1l

k21

1

lk

1

2

lk nk1

nk2

Membership Layer

Rule Layer

TS Layer

Output Layer

Input Layer

)(1 tz )(1 tz

H

W

a

r

Q



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Magnetic Levitation System Using DSP

signals of laser sensor

magnet

position

electromechanical plant

control effort

magnetic current

PC

actuator

DSP card

System block of magnetic levitation apparatus


29

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Intelligent sliding-mode control system using a radial basis function network (SMCRBFN)

Sliding

Surface

dt

dmx

+

-

rx

Input /

Output

Electronics

Electro-

Mechanical

Plant

Magnetic Levitation System

SMCRBFN

Update Law

e

. . . .. . .

dt

d

FE ,

)( FeP)( EeK

)(tS )(tU rx

RBFN

Compensated

Controller

)(tUc

)(tUs

++

Magnetic Levitation System Using DSP


30

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F.A.M.B. Drives

Mo tion C ontrol Cards

Pentium 4 Memory

A DCI/OD A C

Interface

Digital Oscilloscop e

*

rxV

PC

yr

zllrr yxyx ,,,,

xrxlyl z

rxFryFzF

lxF

lyF

*

ryV*lxV

*

lyV

*

zV

zllrr yxyx ,,,,

Inverter

Induction Motor

z

y

x

Position Sensor

Ax ial B earing

L ef t R adial B e aring R igh t Radial B earing

Axial Disk

F.A.M.B. Drives

Mo tion C ontrol Cards

Pentium 4 Memory

A DCI/OD A C

Interface

Digital Oscilloscop e

*

rxV

PC

yr

zllrr yxyx ,,,,

xrxlyl z

rxFryFzF

lxF

lyF

*

ryV*lxV

*

lyV

*

zV

zllrr yxyx ,,,,

Inverter

Induction Motor

z

y

x

Position Sensor

Ax ial B earing

L ef t R adial B e aring R igh t Radial B earing

Axial Disk

Five Degree-of-Freedom Active Magnetic Bearing System Using PC

System block of five degree-of-freedom active magnetic bearing


31

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Adaptive complementary sliding-mode control system using Hermite neural network

Five Degree-of-Freedom Active Magnetic Bearing System Using PC


32

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Y1-axis PMLSM

Y2-axis PMLSM

X-axis PMLSM

qsxiX-axis motor drive

Y1-axis motor drive

2qsyi

1qsyiD/A

converter

flash

EPROM

TMS320VC33

parallel

port

encoder

interface

DSP-based control

computer

Linear Scale

Y2-axis motor drive

Linear Scale

Linear Scale

Y1-axis PMLSM

Y2-axis PMLSM

X-axis PMLSM

qsxiX-axis motor driveX-axis motor drive

Y1-axis motor driveY1-axis motor drive

2qsyi

1qsyiD/A

converter

flash

EPROM

TMS320VC33

parallel

port

encoder

interface

D/A

converter

flash

EPROM

TMS320VC33

parallel

port

encoder

interface

DSP-based control

computer

Linear Scale

Y2-axis motor drive

Linear Scale

Linear Scale

Gantry Position Stage Using DSP


System block of gantry position stage

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System block of sensorless drive system for PMSM compressor

Sensorless Drive System for PMSM Compressor Using dsPIC


35

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Control block of high frequency signal injection method


Speed

Controller

Current

Controlleriqs*

ids*

=0

High Frequency

Voltage Injection

+

+vd*

vq*

SPWM

Voltage

Source

Inverter

+ Vdc -

PMSM

abc

LPF

LPF

Signal Processing

And

Rotor Speed / Flux angle

Estimater

wre*

wre

re^

re^

dq

dq

abc^

Speed

Controller

Current

Controlleriqs*

ids*

=0ids*

=0

High Frequency

Voltage Injection

+

+vd*

vq*

SPWM

Voltage

Source

Inverter

+ Vdc -

PMSM

abc

LPF

LPF

Signal Processing

And

Rotor Speed / Flux angle

Estimater

wre*

wre

re^

re^

re^re

^

dq

dq

abc^

36

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Control block of high frequency signal injection method using PID neural network


37

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System block of mother-board type intelligent light electric vehicle


Elman Neural Network

&

Probabilistic Fuzzy

Neural Network

Mother-Board Type Intelligent Light Electric Vehicle Technology

38

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SV

PWM

SV

PWM

Inverter

Six-Phase

PMSM

Encoder

PI

DC Link

1/2

A/D

QEPp/2

shift

shift

Speed

Calculator

+

-

+

-

+

-

+

-

+

-

r

re

Compensator

+

+

i

Vehicle

DynamicModel

ECE-40motorT

vtK1

*i

*

r

DSP 2812

Torque Meter

PMSG

Three-Phase

Resistive Load

0

*

1qi

*

2qi

*

1di

*

2di

1V

1V

2V

2V

yixi

A/Dbi

ai

abc

xyz

biai

ci

ziyix

i

dq

dq

dq

dq

2qi2di

1qi1di

PI

PI

PI

TCFC

Coordinate Transformation

Coordinate

Transformation*qi

Block diagram of in-wheel motor drive system using six-phase

PMSM


TMS320F2812

39

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Research AchievementsMother-Board Type Intelligent Light Electric Vehicle Technology

Control block of in-wheel motor drive system using probabilistic fuzzy neural network compensator40

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41


Structure of probabilistic fuzzy neural network (PFNN)

layer 1

(input layer)

layer 2

(membership layer)

layer 4

(rule layer)

layer 5

(output layer)

e e

iy

ix

j

kP

Il

Ol

lw

klw

jlwlayer 3

(probabilistic layer)


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dci

aT

a

T

bT

b

T

cT

c

T

dcV

dcC

pL

Drive Circuits

aT bT cT

Utility System

RLC load

TMS320

F2812

DSP

TMS320

F2812

DSP

uvV

CAN BusGSM or IEC61850

Wind Turbine

Generator

Storage

Microgrid Control Center

'

ui'

vi'

wi

uu vi ,

vvvi ,

wwvi ,

QP,

QP,

AC/DCConverter

DC/DC

Converter

Local Dispatched Control Center

TMS320

F2812

TMS320

F2812

DC/DC

Converter

TMS320

F2812

Photovoltaic

20kW

Measurement Signal

'

uv

'

vv

'

wv

Measurement Signal

SS1

SS2

Power Flow

Power Flow

Power Flow

( V and f )

Control Signal

CAN Bus

System block of high-efficiency vertical-axis wind power generation and hybrid generative power

technologies using PV and wind power

Research AchievementsHigh-Efficiency Vertical-Axis Wind Power Generation and Hybrid Generative Power Technologies

Using PV and Wind Power Via DSP

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Control block of current-controlled three-phase voltage source inverter usingd-axis disturbance signal

injection method using wavelet fuzzy neural network (WFNN)


*

usi-

us

i

)( gf ffQinv k

+

+

-+

+

-

*P

*Q

)(T

*

vsi

*

wsidei

*

qei -

-

+

+

+

vsi

wsi

*

dei

disti

PLL

abc

dq

abc

dq

Power

Caculating

Unit

PI PI PI

unv

vnvwnv

usi

vsi

wsi

qv

dv

qi

diP

Q

inv

inv

PI or WFNN

aT

aT

bT

bT

cT

cT

_+

_+

_+

PI or WFNN

*

usi*

usi-

us

ius

i

)( gf ffQinv k

+

+

-+

+

-

*P

*P

*Q

*Q

)(T )(T

*

vsi*

vsi

*

wsi

*

wsideide

i

*

qei*

qei -

-

+

+

+

vsivsi

wsiwsi

*

dei*

dei

distidisti

PLL

abc

dq

abc

dq

Power

Caculating

Unit

PI PI PI

unvunv

vnvvnvwnvwnv

usiusi

vsivsi

wsiwsi

qvqv

dvdv

qiqi

didiP

Q

inv

inv

PI or WFNNPI or WFNN

aT

aT

bT

bT

cT

cT

_+

_+

_+

aT

aT

bT

bT

cT

cT

_+

_+

_+

_+

_+

_+

PI or WFNNPI or WFNN

Active Islanding Detection System

43

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44

Research AchievementsActive Islanding Detection System

Structure of wavelet fuzzy neural network (WFNN)

WF6

WF5 WF8WF7 WF9WF2WF1 WF4WF3

1

1x1

2x

5

oy

4

ky

3

ky

2

jy

1iy

5

kw

k

k

WFkikw

ik

4

kw

3

jkw

Layer 1

(Input Layer)

Layer 2

(Membership Layer)

Layer 5

(Output Layer)

Layer 3

(Rule Layer)

Layer 4

(Wavelet and

Consequent Layer)

1

1x1

2x

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Inverter

Inverter

WTG

Converter

IPCTCP/IP

RouterIPC

PCC

Resistive

load

(2kW)

RLC

load

(2kW)

Inverter

Static Switch

Model of

microturbine

(2kW)

Wind Turbine Generator

Photovoltaic

Industrial Personal Computer

Point of Common Coupling

LiFePO4 Storage

VA,IA

VB,IB

TCP/IP

Local dispatched control center

Microgrid control center

InverterStorage

(4kW)

IPC

PV

(2kW)

(2kW)

DSP

28035

MPPT

LVRT

System Block of Microgrid

45

D i d C l f Wi d T bi S

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Research AchievementsGird-Connected Li-ion Storage System

46Control block of Li-ion storage system using TSK probabilistic fuzzy neural network (TSK PFNN)

D i d C t l f Wi d T bi S t

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Electric Machinery and Control Lab, Department of Electrical Engineering, National Central University, Taiwan.47


Structure of TSK probabilistic fuzzy neural network (TSK PFNN)

Gird-Connected Li-ion Storage System

layer 2(Membership Layer)

layer 6

(Output Layer)

layer 3

(Probabilistic Layer)

layer 4

(Rule Layer and

TSK-Type Fuzzy

Inference

Mechanism)

layer 5

(Consequent Layer)

layer 1

(Input Layer)

e e

1P

11c 21c 12c 22c 19c 29c

1T 2T 9T

2P 3P 16P 17P 18P

1x 2x

2

1

y 22

y 23y

2

4y

2

5y

2

6y

4

1y4

2y4

9y

5

1y5

2y 59y

6

oy

1

1y1

2y


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Grid-Connected Wind Turbine Generation Using Squirrel Cage Induction Generator (SCIG)

48

Control block of SCIG drive system using wavelet fuzzy neural network (WFNN) compensator


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Research AchievementsGird-Connected Photovoltaic (PV) System

49

Control block of PV system using intelligent control methodologies

Documents

lab_intro_20120307