This document discusses the real-time hardware implementation of power converters for grid integration of distributed generation and STATCOM systems. It summarizes the author's defense, which includes simulating and testing a STATCOM model at 50V using conventional control, direct current vector control, and neural network control. It then discusses simulating and testing the models at higher voltages of 200kV. The author proposes proceeding with hardware experiments using dSPACE and OPAL-RT systems to test the different control methods. The document provides details on the simulation models, hardware setup, control strategies, and results.

1. 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

2. 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
SIMULATION
HARDWARE

3. 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

4. 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

5. AC/DC/AC
Maximum Energy
extraction
Grid
integration

7. HARMONICS !!!
FILTERS

8. +
-
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.

9. 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.

10. C
+
-
Vdc
iaRgLgRinv Linv
ib
ic
va
vc
vb
ia1
ib1
ic1
vca
vcb
vcc
va_gcc
vb_gcc
vc_gcc
• 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

11. 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
Controller
AC/DC
DC/AC
Controller
DMS
Energy
Storage
The Grid
Solar
Wind
Fuel cell
Microturbine
MGGC
Charging
Station for EV
APPLICATION- SMART GRID

12. The CONTROL
CONVENTIONAL
CONTROL
DIRECT CURRENT
VECTOR CONTROL
NEURAL NETWORK
CONTROL

13. 3/2
3/2
PWM
Voltage
angle
calculation
2/3
PI
, ,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
di
qi
R
L
pR
C
dcV
L



PI


PI
 


 
L
ej
e 
ej
e 
ej
e 
PI
*
busV
Bus Voltage
Magnitude
Calculation


busV
*
qi
Fast inner Current Loop: Id, Iq
Slow Outer Voltage Loop:
Vdc, bus Voltage, Reactive power

14. 3/2
3/2
2/3
di
di
qi
, ,a b cv
, ,a b ci
R
L
pR
C
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
ej
e 
ej
e 
ej
e 
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

15. • 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.

16. 0.45 0.5 0.55 0.6 0.65
-20
0
20
40
60
q-axiscurrent(A)
Time (sec)
neural
reference
conventional
DCC

17. SIMULATION

18. GRID
CONVERTER
CONTROLLER
PWM
CONVENTIONAL CONTROL

19. V dc ~ 50V
RESULTS

20. Id ~ 50V
Iq ~ 50V

21. GRID TRANSMISSION LINE
CONVERTER
FAULT
LOAD CONTROLLER
DIRECT CURRENT VECTOR CONTROL

22. V dc ~ 50V
RESULTS

23. Id ~ 50V
Iq ~ 50V

24. GRID
TRANSMISSION LINE
CONVERTER
FAULT
LOAD
CONTROLLER
PWM
NEURAL NETWORK

25. V dc ~ 50V
RESULTS
Iq/Id ~ 50V

26. DCC Vdc ~ 200kV
NEURAL NETWORK Vdc ~ 200kV
RESULTS at 200kV

27. DCC Iq/Id ~ 200kV
NEURAL NETWORK Iq/Id ~ 200kV

28. HARWDARE

29. d SPACE UNIT

30. DISPLAY
SELECTOR
VARIABLE
CONTROL
WINDOW
d SPACE CONTROL DESK

31. OPAL RT UNIT

32. MASTER CONSOLE
OPAL RT SIMULATOR

33. RESISTANCE
CONVERTER
ISOLATORS
INDUCTANCE
GRID
D SPACE
LAB VOLT UNIT

34. Component Parameter Value
The grid
Line voltage and
current
120/208V – 5A
Frequency 60Hz
120/208V
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 – 10A
power 24V, 0.16A 50/60Hz
Switching Control 0/5V, 0-20KHz
Grid-filter
Resistance 0.6 Ω
Inductance 25mH
Capacitor
Resistance Rp 700 Ω
Capacitance 16000 µF
Reference voltage 50V
Approach Controller Gain (kp / ki)
Conventional
Current loop 0.895 / 53.073
dc voltage 0.049 / 0.07
DCC
Current loop 1.363 / 44.49
dc voltage 0.08 / 105
Neural
Current loop 0.6815 / 22.245
Dc voltage 0.008 / 105
Parameters of D-STATCOM controllerNetwork data
Parameters of individual STATCOM components
Grid Voltage
PARAMETERS

35. POWER CONVERTER BOARD

36. INVERTER 2INVERTER 1DC BUS
PWM FOR CONVERTER BOARD

37. d SPACE
STATCOM
AC/DC/AC
&

38. MODEL
GRID VOLTAGE
GRID CURRENT
CONTROLLER
PWM
PROTECTIONDC VOLTAGE

39. GUI INTERFACE FOR D SPACE

40. CONVENTIONAL
V dc ~ 50V

41. Id ~ 50V
Iq ~ 50V

42. DIRECT CURRENT VECTOR CONTROL
V dc ~ 50V

43. Id ~ 50V
Iq ~ 50V

44. NEURAL NETWORK CONTROLLER
V dc ~ 50V

45. Id ~ 50V
Iq ~ 50V

46. d SPACE
FILTERS

47. MODEL
GRID VOLTAGE
GRID CURRENT
CONTROLLER
PWM
PROTECTIONDC VOLTAGE

48. +
-
Vdc
va_gcc
vb_gcc
vc_gcc
iaRfLf
ib
ic
va
vc
vb
Vdc ~ 50V
L FILTER

49. Iq ~ 50V
Id ~ 50V

50. Vgrid ~ 50V
I grid ~ 50V

51. C
+
-
Vdc
iaRf Lf
ib
ic
va
vc
vb
ia1
ib1
ic1
vca
vcb
vcc
va_gcc
vb_gcc
vc_gcc
Vdc ~ 50V
LC FILTER

52. Iq ~ 50V
Id ~ 50V

53. Vgrid ~ 50V
Igrid ~ 50V

54. C
+
-
Vdc
iaRgLgRinv Linv
ib
ic
va
vc
vb
ia1
ib1
ic1
vca
vcb
vcc
va_gcc
vb_gcc
vc_gcc
Vdc ~ 50V
LCL FILTER

55. Iq ~ 50V
Id ~ 50V

56. Vgrid ~ 50V
Igrid ~ 50V

57. RT LAB

58. PWM MAIN CONTROLLER
MEASUREMENT
CONTROL
COMMUNICATION
RT LAB MASTER UNIT

59. EXTERNAL
INPUT
COMMUNICATION
PWM
START/STOP
SIGNAL
RT LAB CONSOLE

61. RCPWM

62. PWM out

63. RC EVENTS

64. RESULTS
Iq ~ 50V
Vdc ~ 50V

65. RANGE Electric

66. 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 Fu
Electrical 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 Haskew
Department Head, ECE, University of Alabama
ADVISORS
MENTORS
Innovation
Counsel at
American
Chemical Society
Financial and
Technology
Industry Executive
TEAM
RANGE Electric

67. WHAT DO WE WANT ?
A SELF COMPETING CONTROLLER……..!
EFFICIENT, GOOD BUT SLOW AND
PARAMETER DEPENDENT CONTROLLER……!
INTELLIGENT, SELF LEARNING &
SUPER FAST CONTROLLER ……………

68. 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)
PUBLICATIONS

69. 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.