CELLULAR COMPUTATIONAL NETWORKS FOR ......CCN Luitel B, Venayagamoorthy GK, “Decentralized...

ISGT 2014 Panel Presentation

CELLULAR COMPUTATIONAL NETWORKS FOR SITUATIONAL INTELLIGENCE IN SMART GRIDS

G. Kumar Venayagamoorthy, PhD, FIET, FSAIEEDuke Energy Distinguished Professor &

Director & Founder of the Real-Time Power and Intelligent Systems LaboratoryThe Holcombe Department of Electrical & Computer Engineering

Clemson University

E-mail: [email protected]://people.clemson.edu/~gvenaya

http://rtpis.org

NSF: EFRI #1238097, IIP # 1312260, and ECCS #1231820, #1216298, & #1232070


Cellular computational networks (CCNs) consists of computational units connected to each other in a distributed manner.

CCNs are suited to model systems with temporal and spatial dynamics.

Cellular Computational Networks

111 1 1( ) ( ( ), ( ),..., ( ), ( ))N

i i n n

Nss i SS n nSS SS

O k f O k O k O k u k


Decentralized Asynchronous Learning - CCNs

Luitel B, Venayagamoorthy GK, “Decentralized Asynchronous Learning in Cellular Neural Networks”, IEEE Transactions on Neural Networks, November 2012, vol. 23. no. 11, pp. 1755-1766,


Cellular Computational Networks

CCN



Wide Area Predictive Monitoring Systems (WAPMS)

• Each cell represents one generator of a multi‐machine power system ‐Each cell predicts speed. deviation of one generator

• The cells are connected to each other in the same way as the components in the physical system.

• Nearest neighbors topology is used (n=2) to reduce complexity. G1

1 5 6

7 8 9

10 11 3

42

25km10km

25km10km

110km 110km

G2 G4

G3

)(1 kVrefG )(2 kVrefG )(4 kVrefG )(3 kVrefG)(1 kG )(2 kG )(4 kG )(3 kG

)1(1

kG )1(2

kG )1(3

kG)1(4

kG

Z-1

)(1 kG

)(1 kVrefG

)(2 kVrefG

)(2 kG

)(4 kVrefG

)(4 kG

)(3 kG

)(3 kVrefG

Z-1Z-1

Z-1Z-1

C2

C1 C3

C4


C7

C3C2

C5

C4

C6

C9

C8 C1

C11

C10

C12

C13

C16

C15

C14

Scalable Online CCN based Monitoring Systems



Scalable Online Monitoring Systems

C7

C3C2

C5

C4

C6

C9

C8 C1

C11

C10

C12

C13

C16

C15

C14MLP SRN


Asynchronous Learning in CCNs



Scalable WAPMS based on CCN



August 14, 2003 Blackout

Regular Night August 14, 2003

• > 60 GW of load loss; • > 50 million people affected;• Import of ~2GW caused reactive

power to be consumed;• Eastlake 5 unit tripped;• Stuart-Atlanta 345 kV line tripped;• MISO was in the dark;• A possible load loss (up to 2.5 GW)• Inadequate situational awareness.


Situational Awareness (SA)


Situational Intelligence• Integrate historical and real-time data to implement near-future

situational awarenessIntelligence (near-future) =

function(history, current status, some predictions)

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

• Advanced RT and predictive visualizations

Predictions is critical for

Real-Time Monitoring


Online CCN based Monitoring Systems


Online CCN based Monitoring Systems

G4

0.1258 1 0.1166 1

0.2671 0.0497 0.2729 0.045

0.2671 0.0497 0.2729 0.045

0.6606 0.0124 0.642 0.0191

0.6606 0.0124 0.642 0.0191

1.0977 0.0395 1.102 0.0334

1.0977 0.0395 1.102 0.0334

G30.1728 1 0.1409 10.2594 0.0549 0.2625 0.06770.2594 0.0549 0.2625 0.06770.6659 ‐0.0071 0.5862 10.6659 ‐0.0071 0.6675 ‐0.01690.8357 1 0.6675 ‐0.01691.0866 0.0382 1.1102 0.04971.0866 0.0382 1.1102 0.04971.5552 0.0614 1.5753 0.04551.5552 0.0614 1.5753 0.0455

G60.1159 1 0.1072 10.2678 0.0564 0.2646 0.07480.2678 0.0564 0.2646 0.07480.6478 0.0152 0.6518 ‐0.00260.6478 0.0152 0.6518 ‐0.00261.1176 0.0507 1.1395 0.04951.1176 0.0507 1.1395 0.04951.6009 0.0682 1.4669 11.6009 0.0682 1.527 0.04621.6891 0.2584 1.527 0.0462

G150.0891 ‐0.4184 0.0973 ‐0.43060.0891 ‐0.4184 0.0973 ‐0.43060.4567 0.0318 0.4538 0.02030.4567 0.0318 0.4538 0.02030.7859 0.0801 0.8494 0.03840.7859 0.0801 0.8494 0.03840.8993 0.025 0.9144 0.15370.8993 0.025 0.9144 0.15371.2611 ‐0.0831 1.3902 0.03151.2611 ‐0.0831 1.3902 0.0315

G120.1626 ‐0.0588 0.1466 ‐0.37170.1626 ‐0.0588 0.1466 ‐0.37170.3911 0.161 0.3676 0.11890.3911 0.161 0.3676 0.11890.8112 0.6316 0.7301 0.07790.8112 0.6316 0.7301 0.07791.093 ‐0.04 1.1251 ‐0.01851.093 ‐0.04 1.1251 ‐0.0185

1.2984 ‐0.0368 1.4635 0.00081.2984 ‐0.0368 1.4635 0.0008

CCN: speedNet

Real‐Time Power and Intelligent Systems Lab (http://rtpis.org) 16

C7

C3C2

C5

C4

C6

C9

C8 C1

C11

C10

C12

C13

C16

C15

C14


17

C1

C11

C10

C12

C14C10C15C16

C10C12

C2C10C13C16

C3C10

C2

Computational Network for Generator G10


18

Generator G10 Responses



Summary

• Advanced computational and information technologies are needed for planning and optimization, fast control of power system, processing of field data and fast coordination across the grid.

• The CCN is a scalable high performance learning system for situational intelligence, and distributed energy management and control for smart grids.

• Foresight (from predictions) through insight (data) will results in situational awareness and intelligence.


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 [email protected]

February 21, 2014

CELLULAR COMPUTATIONAL NETWORKS FOR ......CCN Luitel B, Venayagamoorthy GK, “Decentralized...

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