grid integration of distributed generation and statcom systems

REAL TIME HARDWARE IMPLEMENTATION OF POWER CONVERTERS FOR GRID INTEGRATION OF DISTRIBUTED GENERATION AND STATCOM

SYSTEMS

Ishan JaithwaDr Shuhui Li || Dr Tim Haskew || Dr Rachel Fraizer

RANGE Electric

MY DEFENCE

• Simulation of STATCOM model for 50V using

Conventional Control Direct Current Vector Control Neural Network Control

• Hardware verification of STATCOM / AC/DC/AC CONVERTER AND FILTER model for 50V using d SPACE and OPALRT systems

Conventional Control Direct Current Vector Control Neural Network Control

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SIMULATION

HARDWARE

SIMULATION

CONVENTIONAL CONTROL (50V) DCC (50V)

NEURAL NETWORK

CONTROL (50V)DCC (200 kV)

NEURAL NETWORK

(200kV)

HOW I PROCEED

HARDWARE EXPERIMENT

D SPACE

Conventional Direct vector Control

Neural Network Control

OPAL RT

Conventional

Open Loop Test

Direct vector Control

Neural Network Control

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STATCOM

A STATic COMpensator compensates reactive power and provide voltage support to an ac system. A traditional STATCOM consists of

• Energy storage device • AC power system • Voltage source converter (VSC), and a • Control system

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AC/DC/AC

Maximum Energyextraction

Grid integration

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HARMONICS !!!FILTERS

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+

-

Vdc

va_gcc

vb_gcc

vc_gcc

iaRfLf

ib

ic

va

vc

vb

• First-order filter

• Attenuation of 20 dB/decade over the whole frequency range.

• GCC switching frequency must be high in order to sufficiently attenuate the GCC harmonics.

L FILTER

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C

+

-

Vdc

iaRf Lf

ib

ic

va

vc

vb

ia1

ib1

ic1

vca

vcb

vcc

va_gcc

vb_gcc

vc_gcc

• Second-order filter

• 40 dB/decade attenuation

• Better damping behaviors than the L filter

• Suited to configurations in which the load impedance across C is relatively high at and above the switching frequency.

LC FILTER

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C

+

-

Vdc

iaRgLgRinv Linv

ib

ic

va

vc

vb

ia1

ib1

ic1

vca

vcb

vcc

va_gcc

vb_gcc

vc_gcc

LCL FILTER

• 60dB/decade for frequencies above the resonant frequency

• Good current ripple attenuation even with small inductance values

• Lower GCC switching frequency can be used

• Provides better decoupling between the filter and the grid impedance

• Provides lower current ripple across the grid inductor

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25kV 690V

AC/DC DC/DCDC/DC

AC/DC

Controller Controller

AC/DC DC/DC

Controller

AC/DC DC/DC

Controller

AC/DC DC/AC

ControllerAC/DC

DC/ACController

DMS

Energy StorageThe Grid

Solar

WindFuel cell

Microturbine

MGGC

Charging Station for EV

APPLICATION- SMART GRID

The CONTROL

CONVENTIONALCONTROL

DIRECT CURRENT VECTOR CONTROL

NEURAL NETWORKCONTROL

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3/2

3/2

PWM

Voltage angle

calculation

2/3PI

, ,a b cv

, ,a b ci

,v

,i

e

dv

*1dv

*1qv

*1v

*1v

*1, 1, 1a b cv

dv

qv

*di

*qi

*dcV dcV

diqi

R

L

pRC

dcV

L

PI

PI

L

eje

eje

eje

PI*

busV

Bus Voltage Magnitude Calculation

busV*qi

CONVENTIONAL CONTROL

Fast inner Current Loop: Id, IqSlow Outer Voltage Loop: Vdc, bus Voltage, Reactive power

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DIRECT VECTOR CONTROL

3/2

3/2

2/3di

diqi

, ,a b cv

, ,a b ci

R

L

pRC

dcV

PWM

Voltage angle

calculation

,v

e

dv

,i

*1, 1, 1a b cv*

1v*

1v

*1dv

*1qv

R

R

PI

PI

PI

dcV

qi

L

L

*dcV

*qi

*di

eje

eje

eje

PI

*busV

busV*qi

Bus Voltage Magnitude Calculation

• d-axis current for active or dc capacitor voltage control

• q-axis current for reactive power or grid voltage support control

real power or dc link voltage control

reactive power control

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NEURAL NETWORK CONTROL

• Randomly generating a sample initial state idq(j)

• Randomly generating a sample reference dq current

• Training the action network based on the optimization principle

• Repeating the process until a stop criterion is reached.

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0.45 0.5 0.55 0.6 0.65-20

0

20

40

60

q-ax

is c

urre

nt (A

)

Time (sec)

neuralreferenceconventionalDCC

Comparison of Neural Controller with Conventional Standard and DCC Vector Control Methods

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SIMULATION

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GRID

CONVERTER

CONTROLLER

PWM

CONVENTIONAL CONTROL

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V dc ~ 50V

RESULTS

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Id ~ 50V

Iq ~ 50V

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GRID TRANSMISSION LINE CONVERTER

FAULT LOAD CONTROLLER

DIRECT CURRENT VECTOR CONTROL

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V dc ~ 50V

RESULTS

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Id ~ 50V

Iq ~ 50V

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GRIDTRANSMISSION LINE

CONVERTER

FAULT LOAD

CONTROLLER

PWM

NEURAL NETWORK

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V dc ~ 50V

RESULTS

Iq/Id ~ 50V

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DCC Vdc ~ 200kV

NEURAL NETWORK Vdc ~ 200kV

RESULTS at 200kVECE

DCC Iq/Id ~ 200kV

NEURAL NETWORK Iq/Id ~ 200kV

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HARWDARE

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d SPACE UNIT

DISPLAY

SELECTOR

VARIABLE

CONTROL WINDOW

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d SPACE CONTROL DESK

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OPAL RT UNIT

MASTER CONSOLE

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OPAL RT SIMULATOR

RESISTANCE

CONVERTER

ISOLATORS

INDUCTANCE

GRID

D SPACE

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LAB VOLT UNIT

Component Parameter Value

The gridLine voltage and

current 120/208V – 5A

Frequency 60Hz120/208V

transmission Line cable connection

Inductance/phase 0.7Ω, 25mH – 25A dc max

Parallel Resistance/phase 170 Ω

Component Parameter Value

VSC converter

Dc bus 420V – 10Apower 24V, 0.16A 50/60Hz

Switching Control 0/5V, 0-20KHz

Grid-filter Resistance 0.6 ΩInductance 25mH

CapacitorResistance Rp 700 ΩCapacitance 16000 µF

Reference voltage 50V

Approach Controller Gain (kp / ki)

ConventionalCurrent loop 0.895 / 53.073

dc voltage 0.049 / 0.07

DCCCurrent loop 1.363 / 44.49

dc voltage 0.08 / 105

NeuralCurrent loop 0.6815 / 22.245Dc voltage 0.008 / 105

Parameters of D-STATCOM controllerNetwork data

Parameters of individual STATCOM components

Grid Voltage

PARAMETERSECE

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POWER CONVERTER BOARD

INVERTER 2INVERTER 1DC BUS

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PWM FOR CONVERTER BOARD

d SPACESTATCOM

AC/DC/AC&

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MODELGRID VOLTAGE

GRID CURRENT

CONTROLLER

PWM

PROTECTIONDC VOLTAGE

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GUI INTERFACE FOR D SPACEECE

CONVENTIONALV dc ~ 50V

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Id ~ 50V

Iq ~ 50V

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DIRECT CURRENT VECTOR CONTROL

V dc ~ 50V

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Id ~ 50V

Iq ~ 50V

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0 20 40 60 80 100 120 140 160 180 20030

40

50

60

70

Time (sec)

Volta

ge (V

)NEURAL NETWORK CONTROLLER

V dc ~ 50V

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0 20 40 60 80 100 120 140 160 180 200-0.5

0

0.5

1

1.5

Time (sec)

d-ax

is c

urre

nt (A

)

Id Id-ref

0 20 40 60 80 100 120 140 160 180 200-2

-1

0

1

Time (sec)

q-ax

is c

urre

nt (A

)

IqIq-ref

Id ~ 50V

Iq ~ 50V

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d SPACEFILTERS

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MODELGRID VOLTAGE

GRID CURRENT

CONTROLLER

PWM

PROTECTIONDC VOLTAGE

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+

-

Vdc

va_gcc

vb_gcc

vc_gcc

iaRfLf

ib

ic

va

vc

vb

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Vdc ~ 50V

L FILTER

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Iq ~ 50V

Id ~ 50V

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Vgrid ~ 50V

I grid ~ 50V

C

+

-

Vdc

iaRf Lf

ib

ic

va

vc

vb

ia1

ib1

ic1

vca

vcb

vcc

va_gcc

vb_gcc

vc_gcc

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Vdc ~ 50V

LC FILTER

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Iq ~ 50V

Id ~ 50V

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Vgrid ~ 50V

Igrid ~ 50V

C

+

-

Vdc

iaRgLgRinv Linv

ib

ic

va

vc

vb

ia1

ib1

ic1

vca

vcb

vcc

va_gcc

vb_gcc

vc_gcc

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Vdc ~ 50V

LCL FILTER

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Iq ~ 50V

Id ~ 50V

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Vgrid ~ 50V

Igrid ~ 50V

RT LAB

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PWM MAIN CONTROLLER

MEASUREMENTCONTROL

COMMUNICATION

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RT LAB MASTER UNIT

EXTERNAL INPUT

COMMUNICATIONPWM START/STOP SIGNAL

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RT LAB CONSOLE

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RCPWM

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PWM out

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RC EVENTS

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RESULTS

Iq ~ 50V

Vdc ~ 50V

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RANGE Electric

Kenneth J Polk

Lynette Horton

Ishan Jaithwa•Electrical Engineering, MS, University of Alabama

Joshua Stoddard•Mechanical Engineering and STEM path to the MBA, Student at the University of Alabama

Xingang FuElectrical Engineering, Phd, University of Alabama

Dr Shuhui Li -INVENTOR•Associate professor, ECE, University of Alabama

Dr Rachel Frazier•Research Engineer, AIME, University of Alabama

DR Tim A HaskewDepartment Head, ECE, University of Alabama

AD

VISO

RS

MENTORS

Innovation Counsel at American Chemical Society

Financial and Technology Industry Executive

TEAMRANGE Electric

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WHAT DO WE WANT ?

A SELF COMPETING CONTROLLER……..!

EFFICIENT, GOOD BUT SLOW AND PARAMETER DEPENDENT CONTROLLER……!

INTELLIGENT, SELF LEARNING & SUPER FAST CONTROLLER ……………

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Ishan Jaithwa, S Li , X Fu, J Stoddard, “Hardware Experiment Evaluation of STATCOMs using Artificial Neural Networks” (Preparing to submit)

Ishan Jaithwa, J Stoddard, S. Li “Hardware Experiment Evaluation of STATCOMs using Conventional and Direct-Current Vector Control Strategies” (under review).

S. Li, Ishan Jaithwa, R Suftah, X Fu “Direct-Current Vector Control of Three-Phase Grid-Connected Converter with L, LC and LCL Filters” (reviewd and under revision).

S. Li1, X Fu, M Fairbank, Ishan Jaithwa, E Alonso, and D C. Wunsch “Simulation and Hardware Validation for Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks” (under review).

X Fu, S. Li, and Ishan Jaithwa,“ Neural Network Vector Control for Single-Phase PV Grid Converters ,” (Preparing to submit)

PUBLICATIONSECE

REFERENCES[1] N.G. Hingorani, “Flexible AC Transmission Systems”, IEEE Spectrum, Vol. 30, No. 4, 1993, pp. 41-48.[2] A. R. Bergen and V. Vittal, Power System Analysis, 2nd Ed. Upper Saddle River, NJ: Prentice Hall, 2000.[3] E. Acha, C.R. Fuerte-Esquivel, H. Ambriz-Perez, and C. Angeles-Camacho, “FACTS

– Modeling and Simulation in Power Networks,” Chichester, England: John Wiley & Sons Inc., 2004.[4] C. Schauder and H. Mehta, “Vector analysis and control of advanced static VAR compensators,” IEE Proceedings-C, vol. 140, no. 4, pp. 299-306, Jul. 1993.[5] Pablo García-González and Aurelio García-Cerrada, “Control system for a PWM-based STATCOM,” IEEE Trans. on Power Delivery, vol. 15, no. 4, pp. 1252- 1257, Oct. 2000.[6] Pranesh Rao, M. L. Crow, and Zhiping Yang, “STATCOM control for power system voltage control applications,” IEEE Trans. on Power Delivery, vol. 15, no. 4, pp. 1311-1317, Oct. 2000.[7] S. Li, L. Xu, T.A Haskew, “Control of VSC based STATCOM using conventional and

direct current vector control strategies”, International Journal of Electric Power & Energy Systems (Elsevier), Vol. 45, Issue 1, Feb. 2013, pp. 175-186.

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