cellular computational networks for ......ccn luitel b, venayagamoorthy gk, “decentralized...
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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
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ISGT 2014 Panel Presentation
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
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ISGT 2014 Panel Presentation
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,
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ISGT 2014 Panel Presentation
Cellular Computational Networks
CCN
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,
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ISGT 2014 Panel Presentation
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
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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
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ISGT 2014 Panel Presentation
C7
C3C2
C5
C4
C6
C9
C8 C1
C11
C10
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Scalable Online CCN based Monitoring Systems
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,
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ISGT 2014 Panel Presentation
Scalable Online Monitoring Systems
C7
C3C2
C5
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C8 C1
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C14MLP SRN
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ISGT 2014 Panel Presentation
Asynchronous Learning in 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,
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ISGT 2014 Panel Presentation
Scalable WAPMS based on CCN
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,
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ISGT 2014 Panel Presentation
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.
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ISGT 2014 Panel Presentation
Situational Awareness (SA)
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ISGT 2014 Panel Presentation
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
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ISGT 2014 Panel Presentation
Online CCN based Monitoring Systems
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ISGT 2014 Panel Presentation
Online CCN based Monitoring Systems
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ISGT 2014 Panel Presentation
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
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CCN: speedNet
Real‐Time Power and Intelligent Systems Lab (http://rtpis.org) 16
C7
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ISGT 2014 Panel Presentation
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Computational Network for Generator G10
ISGT 2014 Panel Presentation
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Generator G10 Responses
ISGT 2014 Panel Presentation
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ISGT 2014 Panel Presentation
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.
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ISGT 2014 Panel Presentation
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