electric machinery and control laboratory department …linfj/pdf/lab_intro_20120307.pdfdrive and...

Electric Machinery and Control Laboratory Department of Electrical Engineering

National Central University

2012-03-01

Adviser：Professor Faa-Jeng Lin

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

Drive and Control of Wind Turbine System


Areas of Research

• Intelligent control systems including fuzzy, neural network andGA

• 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

2



Awards

• Excellent Research Award, National Science Council, Taiwan, 1993 to2000.

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

• Excellent Young Electrical Engineer Award, the Chinese ElectricalEngineering Association, Taiwan, 2000.

• The Crompton Premium Best Paper Award, the Institution ofElectrical 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, 2004.3



• Outstanding Professor of Electrical Engineering Award, the ChineseElectrical 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

4



Experimental Equipments

Wind-Turbine Emulator and Induction Generator System

DSP+CPLD

FPGA Toggle

5



Linear InductionMotor

Five Degree-of-Freedom Active Magnetic Bearing Control System

Magnetic Levitation System

Two-Axis Motion Control System


6



Three-Phase Induction Motor Drive

Linear Permanent Magnet Synchronous Motor Drive

High-Efficiency Permanent Magnet Synchronous Motor Drive

Linear InductionMotor Drive


7



Three-Axis Stage with Linear Ultrasonic Motors

Permanent Magnet Synchronous Wind-Turbine Generator

Gantry Position Stage DSP28335 Based BLDC Motor Drive System


8




LiFePO4 Battery Gird-Connected Li-ion Storage System

DSP TMS320F280359



Islanding Detection SystemPV Control System

PV Array ( 2 KW )


PV Array ( 10 KW )

10



WTG Control System M-G Set


Sensorless Drive System for PMSM Compressor dsPIC 33FJ128MC708A

11




Permanent Magnet Synchronous Motor Drive System for Light Electric Vehicle

DSP TMS320F28335

DSP-Based Electric Power Steering Motor Drive System

Six-Phase PMSM Fault Tolerant Control System

12




Dynamic Signal Analyzer

Precision LCR Meter

Programmable AC Power Sources

Digital Oscilloscope

13




DC Electronic Loads

Digital Oscilloscope (400MHz)

DC Power Supply (200V/25A)

Programmable AC Power Sources

14




Three-Phase Power Analyzer

DC Power Supply (400V/25A)


15




Function Generator

Industrial Computer

Digital Oscilloscope16



Digital Filterand

Differentiator

Rectifier

3-phase220V60Hz

-

L+

C

++

PWMInverter

Ramp ComparisonCurrent Control

CoordinateTranslator

SIN/COSGenerator

-

+

-

PositionLoop

Controller

Limiter

v

h

*

*

ds

qs

ii

+ SecondaryTime-Constant

Estimator

LIM

SpeedLoop

Controller

rTecosesin

aV bV

aT bT cT

ai

bid

v

ee

ev

slvvn p

pn

*ai

*bi

*ci

*qsi

*dsi*

dr

*v

d

*d

rm

r

TL

drTs *1 )(

rT

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

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



d

md

_

Recurrent Fuzzy Neural Network

Controller

*qsi

meReference

Model*d

LIM Drive System

FKDMs

1s1

+

_dvFe

)(sH p

LF

DeltaAdaptation

Law

oiijijko w,,m,w 4

GeneticAlgorithms

rmw ,,,

dtd mpe

mpeme

mempe

+

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

18



Control block of FPGA-based LIM servo drive

Fuzzy Sliding-Mode Control Linear Induction Motor Drive Using FPGA

bi

LIM

encoder

rectifier

3-phase220V60Hz

L

C-

+PWM

inverter

rampcomparison

currentcontrol

aiaT bT cT

*ai

*bi

*ci

encoder signal

command generator

fuzzy inference mechanism

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

1x2x

1x2x

S

S

fK

field-oriented control system

v co-ordinate translator

& 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


19



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

byicyi

DSP-Based Control Computer

D/AConverter

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

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

LCD

FlashEPROM

TMS320C32

ParallelPort

EncoderInterface

axibxicxi

ayibyicyi

Y-Axis Motor Drive

Y-Axis PMLSM

X-Axis PMLSM

Linear Scale&

Hall Sensor


DSP-based two-axis motion control system 20



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

ReferenceModel

AuxiliaryController

)(tU

d

Adaptation Laws

RobustController

Field-Oriented ControlPMLSM Servo Drive

SlidingSurface

dtd

eFuzzy

Neural NetworkEstimator

Adaptive UncertaintyEstimation

mdd +

-

+

-

+

S

FNNW

)(tu

rU

RFNN Sliding-Mode Control System

)(ˆ tH

smW ˆ ,ˆ ,ˆ smW ˆ ,ˆ ,ˆ

ddtd e


RFNN sliding-mode control system21



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

aT

bT cT

aT

bT cT

dci

dcC

aibi

pLaT bT cT

aT bT cT

uu vi ,vv vi ,ww vi ,

viui

ci

aibi

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


iTi

dsr

qs

dtd

dcv

*abv


Control block of induction generator system with RFNN control22



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

rulelayer

membershiplayer

inputlayer

i

k

o

wjk3

wko4

yi1

y j2

yk3

yo4

xi1

oiw

z1

oiw

z1

output layer

j


Structure of four-layer RFNN 23



Synchronous Motor Drive System Using DSP and CPLD

Inverter

PWM1PWM2PWM3PWM4PWM5PWM6

ai

bi

ci

dsi

qsi

U

V

W

*aV*

bV*

cV

*dsi

*qsi

EncoderInterface Circuit

u,v,w

a,b,zEncoder &Hall Signal

Detector

Rotor InitialAngle Detector

WSiUSi

r

eh

DSP TMS320LF2407

r

*r

Rectifier

CPLD ALTERA10K100ARC240-1

SpeedLoop

Controller

+

PI

PI*

qsV

*dsV

0

-

-

+

-

+

+

-

3-phase220V60Hz

SIN/COSGenerator

A/D

A/D

32

23

SPWM

CurrentProcessCircuit

Filter Circuit


System configuration of field-oriented control PMSM servo drive 24



Synchronous Motor Drive System Using DSP and CPLD


System block of PMSM servo drive 25



X-Y-θ Motion Control Stage Using Linear Ultrasonic Motors

System block of X-Y-θ motion control stage

Motor Drives

Pentium 4 Memory

Interface


LUSMEncoderX

*xV

YX

PC

EncoderY Encoder

*yV

*V

LUSM

LUSM`

YX

X-Axis

Y-axisӨ-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-axisӨ-Axis

Servo Control Cards 3

EncoderI/OADC


26




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

TU dmd

+

_

)(1 tz )(2 tz

)(1 tz

md

v

StabilizingFunction &

)(2 tz

Single-axis Motion Mechanism Using LUSM Drive

dtd

dtd

Nominal ModelDesign

hU

nU

d

md

+

v

SAFNN

AdaptationLaw

Γ

Compensator & Adaptation Law

+

)(1 tz

)(2 tz

)(2 tz

W

rU

)(1 tz

-


27


Electric Machinery and Control Lab, Department of Electrical Engineering, National Central University, Taiwan.28Structure of Sugeno type adaptive fuzzy neural network (SAFNN)

11k 1

2k lk 1lk 2

11lk 1

2lk nk1

nk2

Membership Layer

Rule Layer

TS Layer

Output Layer

Input Layer

)(1 tz )(1 tz

H

W μ

a

r

Q

Γ





Magnetic Levitation System Using DSP

signals of laser sensor

magnetposition

electromechanical plant

control effort

magnetic current

PC

actuator

DSP card

System block of magnetic levitation apparatus


29



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

SlidingSurface

dtdmx

+

-

rx

Input / Output

Electronics

Electro-Mechanical

Plant

Magnetic Levitation System

SMCRBFN

Update Law

e

. . . .. . .

dtd

FE ˆ ,ˆ

)ˆ(ˆ FeP)ˆ(ˆ EeK

)(tS )(tU rx

RBFN

Compensated Controller

)(tUc

)(tU s

++

Magnetic Levitation System Using DSP


30



F.A .M.B . D rives

M o tio n C on tro l C ard s

Pentium 4 M em o ry

A DCI/OD A C

In te rface

D ig ita l Osci llo scop e

*rxV

P C

yr

zllrr yxyx ,,,,

xrxlyl z

r xFryFzF

lxFlyF

*ryV

*lxV

*lyV

*zV

zllrr yxyx ,,,,Inv er ter

Ind uc tion M otor

z

y

x

Po sit io n Senso r

A xial B earingL ef t R adial B earing R igh t Radial B earing

A xial D isk

F.A .M.B . D rives

M o tio n C on tro l C ard s

Pentium 4 M em o ry

A DCI/OD A C

In te rface

D ig ita l Osci llo scop e

*rxV

P C

yr

zllrr yxyx ,,,,

xrxlyl z

r xFryFzF

lxFlyF

*ryV

*lxV

*lyV

*zV

zllrr yxyx ,,,,Inv er ter

Ind uc tion M otor

z

y

x

Po sit io n Senso r

A xial B earingL ef t R adial B earing R igh t Radial B earing

A xial D isk

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

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


31



Adaptive complementary sliding-mode control system using Hermite neural network

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


32



Y1-axis PMLSM

Y2-axis PMLSM

X-axis PMLSM

qsxi X-axis motor drive

Y1-axis motor drive

2qsyi

1qsyi

D/Aconverter

flashEPROM

TMS320VC33

parallelport

encoderinterface

DSP-based control computer

Linear Scale

Y2-axis motor drive

Linear Scale

Linear Scale

Y1-axis PMLSM

Y2-axis PMLSM

X-axis PMLSM

qsxi X-axis motor driveX-axis motor drive

Y1-axis motor driveY1-axis motor drive

2qsyi

1qsyi

D/Aconverter

flashEPROM

TMS320VC33

parallelport

encoderinterface

D/Aconverter

flashEPROM

TMS320VC33

parallelport

encoderinterface

DSP-based control computer

Linear Scale

Y2-axis motor drive

Linear Scale

Linear Scale

Gantry Position Stage Using DSP


System block of gantry position stage33



Gantry Position Stage Using DSP


iu_ 1yd

2ydmd

Dual Linear Motors Servo System

T

Edtd Ξ

dtd *E

β

1ye2ye

E

E

Ξ

Ξ

*E

CSMCSFNN

niA

+

+ +CSMCSFNNiu

+

_

+

+

1niB

dtd

tdd

2

2

dtd

iλ

dtd

iλ

+

2

RiuRobust

Controller (32)

Adaption Laws(33)-(36)&(37)

TT

TT

TT

TT

TT

TT

TT

TT

+

_

_+

id

md

id

iS

*ie2

*iieλ

md

Cii SS

iλ

*ie

*ie

*ie

iH

Cross-Coupled Position Tracking Error

*ie

+Sliding Surfaces

(16)&(18)iu

_ 1yd

2yd1yd

2ydmd

Dual Linear Motors Servo SystemDual Linear Motors Servo System

T

Edtddtd ΞΞ

dtddtd *E

β

1ye2ye

E

E

Ξ

Ξ

*E

CSMCSFNN

niAniA

+

+ +CSMCSFNNiu

+

_

+

+

1niB1niB

dtddtd

tdd

2

2

tdd

2

2

dtddtd

iλiλ

dtddtd

iλiλ

+

2

RiuRobust

Controller (32)

Adaption Laws(33)-(36)&(37)

TT

TT

TT

TT

TT

TT

TT

TT

TT

TT

TT

TT

TT

TT

TT

TT

+

_

_+

id

md

id

iS

*ie2

*iieλ

md

Cii SS

iλ

*ie

*ie

*ie

iH

Cross-Coupled Position Tracking Error

*ie

+Sliding Surfaces

(16)&(18)

Cross-coupled synchronous control using Sugeno type fuzzy neural network estimator34



System block of sensorless drive system for PMSM compressor

Sensorless Drive System for PMSM Compressor Using dsPIC


35




Control block of high frequency signal injection method


Speed

Controller

Current

Controlleriqs*

ids* =0

High Frequency

Voltage Injection

++vd*

vq*SPWM

VoltageSourceInverter

+ Vdc -

PMSM

abc

LPF

LPF

Signal Processing

And

Rotor Speed / Flux angle

Estimater

wre*

wre

θre

^

θre

^

dq

dq

abc^

Speed

Controller

Current

Controlleriqs*

ids* =0ids* =0

High Frequency

Voltage Injection

++vd*

vq*SPWM

VoltageSourceInverter

+ Vdc -

PMSM

abc

LPF

LPF

Signal Processing

And

Rotor Speed / Flux angle

Estimater

wre*

wre

θre

^θre

^

θre

^θre

^

dq

dq

abc^

36




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


37



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




SVPWM

SVPWM

Inverter

Six-PhasePMSM

Encoder

PI

DC Link

1/2

A/D

QEPp/2

shift

shift

SpeedCalculator

+-

+-

+-

+

-

+-

r

re

Compensator

+

+i

VehicleDynamicModel

ECE-40motorT

v tK1

*i

*r

DSP 2812

Torque Meter

PMSG

Three-PhaseResistive Load

0

*1qi

*2qi

*1di

*2di

1V1V

2V2V

yixi

A/Dbiai

abc

xyz

biai

ci

ziyixi

dq

dq

dq

dq

2qi2di

1qi1di

PI

PI

PI

TCFC


Coordinate Transformation *

qi

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


TMS320F2812

39



Research Achievements Mother-Board Type Intelligent Light Electric Vehicle Technology

Control block of in-wheel motor drive system using probabilistic fuzzy neural network compensator 40


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

jlw layer 3(probabilistic layer)




dci

aT

aT

bT

bT

cT

cT

dcV

dcC

pL

Drive Circuits

aT bT cT

Utility System

RLC load

TMS320F2812

DSPTMS320

F2812

DSP

uvV

CAN BusGSM or IEC61850

Wind Turbine Generator

Storage

Microgrid Control Center

'ui

'vi

'wi

uu vi ,

vv vi ,

ww vi ,

QP ,

QP,

AC/DCConverter

DC/DCConverter

Local Dispatched Control Center

TMS320F2812

TMS320F2812

DC/DCConverter

TMS320F2812

Photovoltaic20kW

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 Achievements High-Efficiency Vertical-Axis Wind Power Generation and Hybrid Generative Power Technologies

Using PV and Wind Power Via DSP

42



Control block of current-controlled three-phase voltage source inverter using d-axis disturbance signalinjection method using wavelet fuzzy neural network (WFNN)


*usi - usi

)( gf ffQinv k

++

-+

+ -

*P*Q

)(T

*vsi

*wsidei

*qei -

-+

+

+

vsi

wsi*dei

disti

PLL

abcdq

abcdq

Power Caculating

Unit

PI PI PI

unvvnvwnv

usivsiwsi

qv

dv

qi

diP

Q

inv

inv

PI or WFNN

aT

aT bT

bT cT

cT

_+ _+ _+

PI or WFNN

*usi*usi - usiusi

)( gf ffQinv k

++

-+

+ -

*P*P*Q*Q

)(T )(T

*vsi*vsi

*wsi*wsideidei

*qei*qei -

-+

+

+

vsivsi

wsiwsi*dei*dei

distidisti

PLL

abcdq

abcdq

Power Caculating

Unit

PI PI PI

unvunvvnvvnvwnvwnv

usiusivsivsiwsiwsi

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



Research Achievements Active Islanding Detection System

Structure of wavelet fuzzy neural network (WFNN)

WF6

WF5 WF8WF7 WF9WF2WF1 WF4WF3

11x 1

2x

5oy

4ky

3ky

2jy

1iy

5kw

k

k

WFkikw

ik

4kw

3jkw

Layer 1(Input Layer)

Layer 2(Membership Layer)

Layer 5(Output Layer)

Layer 3(Rule Layer)

Layer 4(Wavelet and

Consequent Layer)

11x 1

2x




Inverter

Inverter

WTG

Converter

IPCTCP/IP

RouterIPC

PCC

Resistiveload

(2kW)

RLCload

(2kW)

Inverter

Static Switch

Model of microturbine

(2kW)

Wind Turbine GeneratorPhotovoltaicIndustrial Personal ComputerPoint of Common CouplingLiFePO4 Storage

VA,IA

VB,IB

TCP/IP

Local dispatched control center

Microgrid control center

InverterStorage(4kW)

IPC

PV(2kW)

(2kW)

DSP28035

MPPTLVRT

System Block of Microgrid

45



Research Achievements Gird-Connected Li-ion Storage System

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




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

21y 2

2y 23y 2

4y 25y 2

6y

41y 4

2y 49y

51y 5

2y 59y

6oy

11y 1

2y



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

48Control block of SCIG drive system using wavelet fuzzy neural network (WFNN) compensator



Research Achievements Gird-Connected Photovoltaic (PV) System

49Control block of PV system using intelligent control methodologies

electric machinery and control laboratory department …linfj/pdf/lab_intro_20120307.pdfdrive and...

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