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An Approach To Improving The Physical And Cyber Security Of A Bulk Power System With FACTS

FACTS. An Approach To Improving The Physical And Cyber Security Of A Bulk Power System With FACTS. Natural Faults. Mariesa Crow & Bruce McMillin School of Materials, Energy & Earth Resources Department of Computer Science University of Missouri-Rolla Stan Atcitty

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An Approach To Improving The Physical And Cyber Security Of A Bulk Power System With FACTS

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  1. FACTS An Approach To Improving ThePhysical And Cyber Security Of ABulk Power System With FACTS Natural Faults Mariesa Crow & Bruce McMillin School of Materials, Energy & Earth Resources Department of Computer Science University of Missouri-Rolla Stan Atcitty Power Sources Development Department Sandia National Laboratory FACTS PhysicalAttack Funded through the DOE Energy Storage Program

  2. Problem Motivation • Prevent Cascading failures: • 2003 Blackout • Causes • Physical & Cyber contingencies • Deliberate disruption • Hackers • Terrorist Activity

  3. Proposed Solution Flexible AC Transmission Systems (FACTS) • Power Electronic Controllers • Means to modify the power flow through a particular transmission corridor • Integration with energy storage systems

  4. US FACTS Installations Vermont Electric/ STATCOM/ 130 MVA/ Mitsubishi NYPA/ Convertible Static Compensator/ 200 MVA AEP/ Unified Power Flow Controller /100 MVA/ EPRI San Diego G&E/ STATCOM/100 MVA Mitsubishi Northeast Utilities/ STATCOM/ 150 MVA/ Areva (Alstom) TVA STATCOM/ 100MVA EPRI Eagle Pass (Texas) Back-to-back HVDC 37 MVA/ ABB Austin Energy STATCOM/ 100MVA ABB CSWS (Texas) STATCOM/ 150 MVA / W-Siemens

  5. Communication and coordination Scheduling - Distributed Long-Term control Interaction – Local Dynamic control Vulnerabilities of the combined physical/ cyber system Recovery and protection from physical faults and/or cyber attacks and/or human error Decentralized Infrastructures

  6. Identify cascading failure scenarios for test systems

  7. G G G G G SpornE S.Tiffin Howard West End 41 40 42 Cascading Scenario Outage 48-49 NwLibrty 39 WMVernon EastLima 37 44 38 43 54 34 N.Newark Rockhill S.Kenton 50 45 51 Zanesvll 48 36 35 Sterling Philo WLima 47 49 46 Summerfl 67 W.Lancst Crooksvl 66 62 MuskngumN Natrium Sargents 65 Trenton 73 64 MuskngumS Kammer 24 CollCrnr 68 23 69 72 Hillsbro SpornW 71 NPortsmt TannrsCk Portsmth Portsmth 70 Bellefnt 75 74 SthPoint

  8. G G G G G SpornE S.Tiffin Howard West End 41 40 42 Cascading Scenario Outage 48-49 NwLibrty 39 WMVernon EastLima 37 44 38 43 54 34 N.Newark Rockhill S.Kenton 50 45 51 Zanesvll 48 36 35 Sterling Philo WLima 47 49 46 Summerfl 67 W.Lancst Crooksvl 66 62 MuskngumN Natrium Sargents 65 Trenton 73 64 MuskngumS Kammer 24 CollCrnr 68 23 69 72 Hillsbro SpornW 71 NPortsmt TannrsCk Portsmth Portsmth 70 Bellefnt 75 74 SthPoint

  9. G G G G G SpornE S.Tiffin Howard West End 41 40 42 Cascading Scenario Outage 48-49 NwLibrty 39 WMVernon EastLima 37 44 38 43 54 34 N.Newark Rockhill S.Kenton 50 45 51 Zanesvll 48 36 35 Sterling Philo WLima 47 49 46 Summerfl 67 W.Lancst Crooksvl 66 62 MuskngumN Natrium Sargents 65 Trenton 73 64 MuskngumS Kammer 24 CollCrnr 68 23 69 72 Hillsbro SpornW 71 NPortsmt TannrsCk Portsmth Portsmth 70 Bellefnt 75 74 SthPoint

  10. G G G G G SpornE S.Tiffin Howard West End 41 40 42 Cascading Scenario Outage 48-49 NwLibrty 39 WMVernon EastLima 37 44 38 43 54 34 N.Newark Rockhill S.Kenton 50 45 51 Zanesvll 48 36 35 Sterling Philo WLima 47 49 46 Summerfl 67 W.Lancst Crooksvl 66 62 MuskngumN Natrium Sargents 65 Trenton 73 64 MuskngumS Kammer 24 CollCrnr 68 23 69 72 Hillsbro SpornW 71 NPortsmt TannrsCk Portsmth Portsmth 70 Bellefnt 75 74 SthPoint

  11. FACTS Placement and Control

  12. FACTS Control • Distributed Long-Term control algorithms for FACTS settings • Run by each processor in each FACTS • Alternatives • Max-flow algorithms • Local optimizations • Agent-based framework • Assessment • Reduction of Overloads • Computability

  13. FACTS Placement • Placement • Place few FACTS in a large network for maximum benefit Evolutionary Algorithms (EAs) will be used to place FACTS devices in the network

  14. Performance Index Metric Gradient Descent on PI Metric vs. Maximum Flow

  15. FACTS Interaction Laboratory (FIL)

  16. FIL Overview • Construct a Laboratory System to Study and Mitigate • Cascading Failures • Deleterious effects of interacting power control devices • Cyber Vulnerabilities • Hardware in the Loop (HIL) • Real-time Simulation Engine • Simulate Existing Power Systems • Inject Simulated Faults • Interconnected laboratory-scale UPFC FACTS Device • Measure actual device interaction

  17. 230 kV 345 kV 500 kV 35 33 32 30 31 74 80 79 66 75 FACTS 78 72 76 69 v v 77 82 81 36 84 85 86 83 162 112 161 156 157 114 11 5 167 155 165 44 159 158 6 45 160 115 166 163 18 17 118 13 8 12 7 108 119 138 139 109 9 107 14 37 110 104 63 64 103 147 3 143 4 154 146 102 142 56 48 153 151 145 136 49 47 140 152 19 150 141 149 57 42 43 50 16 15 FACTS Interaction LaboratoryArchitecture FACTS/ESS A/D D/A 10 KVA Simulation Engine (multiprocessor) A/D D/A A/D D/A FACTS/ESS FACTS/ESS Network

  18. HIL Laboratory Interface Machine 1 FACTS1 D/A output Controllable Load A/D input Machine 2 D/A output FACTS2 Controllable Load A/D input Machine 3 FACTS3 Power System Simulation Engine D/A output Controllable Load A/D input

  19. FACTS – Flexible AC Transmission System Prototype Device

  20. FACTS Interaction Laboratory UPFC Simulation Engine HIL Line

  21. Cyber Fault Detection

  22. Fault Tolerance • Define correct operation of the power system with FACTS/ESS • Embed as executable constraints into each FACTS/ESS computer • FACTS/ESS check each other during operation of distributed control algorithms

  23. Cyber Fault Injection • Attempt to confuse the FACTS embedded computers • Attempt to disrupt the communication between FACTS embedded computers • Confuse the power system’s operation

  24. Error Coverage of Distributed Executable Correctness Constraints(Maximum Flow Algorithm)

  25. System Dynamic Control

  26. Power Network Embedded With FACTS Devices Tie-line flow CONTROL AREA A CONTROL AREA B A decentralized power network embedded with FACTS devices can be viewed as a hybrid dynamical system (Differential-algebraic-discrete-event). While the FACTS devices offer improved controllability, their actions in a decentralized power network can cause deleterious interactions among them.

  27. Performance of FACTS controllers with ideal observability Uncontrollable modes in generator speeds due to device interactions

  28. Project Benchmarks • Construction of HIL • Demonstration of Cascading Failures • Placement and Control • Hardware/Software Architecture • Cyber Fault Detection • Dynamic Control • Visualization

  29. Special Thanks • Imre Gyuk – DOE Energy Storage • Stan Atcitty – Sandia National Lab • John Boyes – Sandia National Lab

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