CLEMSON UNIVERSITY’S SYNCHROPHASOR

ENGINEERING EDUCATION PROGRAM (CU-SHEEP)

G. Kumar Venayagamoorthy, PhD, FIET, FSAIEE

Duke Energy Distinguished Professor &

Director & Founder of the Real-Time Power and Intelligent Systems Laboratory

The Holcombe Department of Electrical & Computer Engineering

Clemson University

E-mail: gkumar@ieee.org

http://people.clemson.edu/~gvenaya

http://rtpis.org

Acknowledgment:

US DOE: DE-OE0000660

US DOE CU-ShEEP Project

The objective of this project is to establishcollaboration with a local utility – Duke Energyand others, in establishing a SituationalIntelligence (SI) Laboratory that will be drivenfrom a Real-Time Grid Simulation Laboratoryand introduce new content in the power andenergy systems engineering research andeducation at Clemson University, especially inthe area of synchrophasor technology and itsapplication in a smart grid environment.

US DOE CU-ShEEP Project

• The SI lab is a unique research and rapid prototyping/customizing laboratory for new real-time situational intelligence (SI) and intelligent control technologies for electric power control center deployment.

• Algorithms and methods for real-time situational awareness (SA) and intelligence (SI) and visualizations for control centers are developed using in-house and third party software.

• The laboratory is focused on applications for generation, transmission, and distribution systems and microgrid control center operations.

• The SI Lab has system operator consoles besides the large control room displays.

• Many of the applications are parallelized for implementation on Clemson Palmetto cluster. Data is transmitted using a dedicated (~10-mile) high-speed fiber optic connection between the SI Lab and the Clemson data center.

• Students education and training on synchrophasor technology as part of undergraduate and graduate curricula.

• Development of new contents on synchrophasors and its applications in over ten courses offered at the undergraduate/graduate levels.

• Clemson’s synchrophasor engineering education with local utility’s participation.

• Three short courses in synchrophasor and related technologies. These courses are intended for utility and industrial professionals.

EDUCATION OUTCOMES

• Students involved with CU-ShEEP are expected to have experiential training opportunities at utilities and vendors. Short courses in synchrophasors and their applications in the control centers for non-power system, utility and industrial engineers are under developed.

• Development of future workforce of electrical engineers, computer scientists and non-power system engineers who are knowledgeable about and experienced in synchrophasor analysis.

• The project innovations/findings will be shared with other academic institutions and synchrophasor communities.

EDUCATION OUTCOMES

• Researchers and students gaining hands-on experience on real-time grid simulation, control center operations and synchrophasor measurements.

• Development of distributed computing algorithms for analyzing and predicting smart grid data.

• Applications of synchrophasor phasor data for real-time power system operations such as online generator coherency analysis; online oscillation of synchronous generators; and as tie-line bias control of a power system with high penetration of variable generation.

• Security evaluation of synchrophasor network for power system operations.

• Development of situational awareness and situational intelligence algorithms and visualization tools for enhanced control center operations.

RESEARCH OUTCOMES

Situational Awareness (SA)in a Control Center

When a disturbance happens, the operator isthinking:

• Received a new alert!• Is any limit in violation?

• If so, how bad?• Problem location?

• What is the cause?• Any possible immediate corrective or mitigative

action?• What is the action?• Immediate implementation or can it wait?

• Has the problem been addressed?• Any follow up action needed?

7

Situational Intelligence

• Integrate historical and real-time data to implement near-futuresituational awareness

Intelligence (near-future) = function(history, current status, some predictions)

• Predict security and stability limits• RT operating conditions• Oscillation monitoring• Dynamic models• Forecast load• Predict/forecast generation• Contingency analysis

• Real-time and predictive visualizations• Integrate all applications• Topology updates and geographical influence (PI and GIS – Google

earth tools)

Predictions is critical for Real-Time

Monitoring

8

Situational Intelligence Laboratory

Real-Time Grid Simulation Laboratory

Experimental Setup

11

0 2 4 6 8 10 12 14 16 180.994

0.996

0.998

1

1.002

1.004

1.006

Time [s]

speed in p

.u.

05

1015

20

0

5

10

15

201

2

3

4

5

6

7

8

Time [s]Generator index

Gro

up index

Group

index

Offline

Clustering

during

0~18s

Online

Clustering

at 8s

Online

Clustering

at 10s

Online

Clustering

at 15s

1 G1, G8 G1,G8 G1,G2,G3,

G8,G10,G11,G12,G

13

G1,G8

2 G2,G3 G2,G3 G4,G5,G6,

G7,G9

G2,G3

3 G4,G5,G6,

G7,G9

G4,G5,G6,

G7

G14,G15,

G16

G4,G5,G6,

G7,G9

4 G10,G11 G9 G10,G11,

G12,G13

5 G12,G13 G10,G11 G14,G15,

G16

6 G14,G15,

G16

G12,G13

7 G14,G15,

G16

Online Coherency Analysis of Synchronous Generators in a Power System

100ms three phase fault at bus 8

Online Oscillation Monitoring of Synchronous Generators

Area 3

G7

G6

G9

G4

G5

G3

G8 G1

G2

G13

G12

G11

G10

G16

G15

G14

59

23

61

29

58

22

28

26

60

25

53

2

24

21

16

56

57

19

20

55

10

13

15

14

17

27

12

11

6

547

5

4

18

3

8

1

47

48 40

62

30

9

37

65

64

36

34

38

35

33

63

43

45

39

50

51

52

68

67

49

46

42

41

31

66

Area 1 Area 2

Area 4

New England Test System New York Power System

44

32

Area

5

-------------------------

6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11375.5

376

376.5

377

377.5

378

378.5

time (s)

angula

r s

peed (

rad/s

)

6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11376

376.2

376.4

376.6

376.8

377

377.2

time (s)

angula

r speed (

rad/s

)

Prony’s algorithm run on PMU data parsed on a PC

Modal damping and

frequencies

Modal damping and

frequencies

Modal damping and

frequencies

Gen. 1

-------- --------

Gen. 8 Gen. 16

Tie-Line Bias Control in a Power System with Large PV Plants

0 20 40 60 80 100 120160

160.5

161

161.5

162

162.5

163

163.5

164

164.5

165

Time in Seconds

PV

Outp

ut

Pow

er

in M

W

0 20 40 60 80 100 120130

135

140

145

Tie

-Lin

e P

ow

er

Flo

w in M

W

Time in Seconds

0 50 100 150 2000

50

100

150

200

250

300

Time (s)

Pow

er

(MW

)

0 50 100 150 200

200

250

300

350

400

Time (s)

Pow

er

(MW

)

0 20 40 60 80 100 12059.99

59.995

60

60.005

60.01

Time in Seconds

Are

a 1

PM

U F

requency in H

z

0 20 40 60 80 100 12059.98

59.99

60

60.01

60.02

60.03

Time in Seconds

Are

a 2

PM

U F

requency in H

z

Summary

• CU-ShEEP will educate our students and workforce in the

knowledge of synchrophasor technology and control center

operations.

• The project innovations/findings will be shared with other

academic institutions and synchrophasor communities through

workshops, panels at conferences and NAPSI meetings, and

publications.

• Possible areas of collaboration:

• Applications of situational awareness and situational

intelligence algorithms and visualization tools to other

utilities’ synchrophasor/micro-synchrophasor data from

transmission/distribution system, respectively.

• Custom distributed algorithms for other utility networks.

14

Thank You!G. Kumar Venayagamoorthy

Director and Founder of the Real-Time Power and Intelligent Systems Laboratory &Duke Energy Distinguished Professor of Electrical and Computer Engineering

Clemson University, Clemson, SC 29634

http://rtpis.org gvenaya@clemson.edu

March 13, 2014