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Fault-Tolerant and Secure Intelligent Vehicle Highway System Software - a Safety Prototype

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Title: Fault-Tolerant and Secure Intelligent Vehicle Highway System Software - a Safety Prototype Author: Rob Mensching Last modified by – PowerPoint PPT presentation

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Title: Fault-Tolerant and Secure Intelligent Vehicle Highway System Software - a Safety Prototype


1
Distributed Control of FACTS Devices Using a
Transportation Model
Bruce McMillin Computer Science Mariesa
Crow Electrical and Computer Engineering Universi
ty of Missouri-Rolla Rolla, MO 65409-0040
2
Outline
  • FACTS Devices
  • Max Flow
  • Suitability of Max Flow to Power System
  • Distributed Max Flow
  • Fault Tolerance of Distributed Max Flow

3
Project Motivation
  • Due to large unidirectional power flows,
    transmission grids are becoming increasingly
    susceptible to cascading failures
  • Decentralized network control is necessary to
    rebalance power flow and contain the extent of
    the cascade

4
  • FACTS devices offer a decentralized
    network-embedded control mechanism

5
Project Objective
  • Develop an effective distributed FACTS control
    algorithm to mitigate cascading grid failures,
    either intentional or unintentional
  • Make the developed algorithms fault-tolerant
    using formal methods based on power system
    specifications

6
Approach
  • The embedded controllers will execute
    graph-theory-based
  • max flow distributed algorithms to identify
    critical transmission corridors and adjust power
    flow accordingly to avoid cascading failures

7
Outline
  • FACTS Devices
  • Max Flow
  • Suitability of Max Flow to Power System
  • Distributed Max Flow
  • Fault Tolerance of Distributed Max Flow

8
Example
9
(No Transcript)
10
Max-Flow
  • Assign an initial flow to all arcs
  • Mark the source and sink
  • Search for a node that can be labeled. If none is
    found, flow is maximum, stop.
  • Backtrack the path computing the minimum ?ij
    used. Go to previous step.

11
10
12
Loss of Line B-D
  • Load at bus D must be reduced from 20 to 15
  • Load at bus C must be reduced from 30 to 27

13
Outline
  • FACTS Devices
  • Max Flow
  • Suitability of Max Flow to Power System
  • Distributed Max Flow
  • Fault Tolerance of Distributed Max Flow

14
Suitability of Transportation Model (max flow)to
Power Systems?
  • Losses and Reactive Power?
  • Experimental Verification
  • No difference at steady state from max flow
  • A few percent difference between max flow
    calculations and load-flow analysis after a
    contingency using FACTS devices

15
  • In general, lines are not all maximally loaded.
    The power flow can then be re-directed to new
    transmission corridors.
  • Where re-direct?
  • How much to re-direct?
  • How account for KCL?
  • Control/communication between decision-making
    devices?

16
Placement of FACTS Devices
  • Experimentally
  • Delete a line
  • Run Max Flow servicing loads increasing line
    capacities by reverse augmentation to a maximum
    of 20.
  • Using Load Flow analysis, place FACTS devices to
    eliminate overloaded lines.
  • Go to step 1

17
Placement of FACTS Devices
18
Resulting System Configuration
19
Resulting Line Overloads (gt20)
20
Outline
  • FACTS Devices
  • Max Flow
  • Suitability of Max Flow to Power System
  • Distributed Max Flow
  • Fault Tolerance of Distributed Max Flow

21
Distributed Max flow
  • Multiple source (generator)
  • Concurrent flow-augmenting probes
  • FACTS devices communicate by message passing
    along the direction of the flow augmentation
  • Each FACTS device computes the flow for a
    partition of lines (using Chaco from Sandia)
  • Multiple Computers, Open Communication Lines,
    Distributed Software

22
Outline
  • FACTS Devices
  • Max Flow
  • Suitability of Max Flow to Power System
  • Distributed Max Flow
  • Fault Tolerance of Distributed Max Flow

23
Vulnerabilities
  • Computer System Failure
  • Programming Errors
  • Hackers (Security Intrusions)

24
Software Correctness?
  • Distributed Computing System
  • Verification (Development Time)?
  • Complexity
  • Model Checking and Theorem Proving
  • Testing
  • Test Cases
  • Monitoring
  • Assertion Testing.

25
Proposed Idea
Combine assertions from formal verification with
run-time checking (monitoring).
26
Proposed Approach
  • Distributed run-time assertion checking
  • focuses on the unique execution in progress -
    guarantees that the current execution meets its
    specifications regardless of underlying hardware
    or system confidence

27
Embedded Monitoring
  • Assertions are predicates are a collected global
    state of events
  • If an event happens before another they can be
    partially ordered
  • Lamport Logical Clock
  • Each event has a logical timestamp Cevent
  • The most current event is the one with the
    largest timestamp.
  • Timestamps are forced to increase on a message
    receive so that message sends precede message
    receives.

28
Underlying Theory
  • Correctness is defined by theorems about the
    program. Theorems are easily translated into
    assertions for monitoring.
  • For the assertions to be correct, a program code
    action, a, must not interfere with the truth of
    an assertion, P
  • (ltP pre(a)gt a ltPgt).
  • In a distributed system, this truth must be
    preserved over all interleavings of processes.
  • Using timestamps, the monitoring is guaranteed to
    correctly reflect the distributed programs
    state.

29
Failure Scenario
  • Distributed Multiple Source Max Flow
  • Correctness is defined by KCL at each node
  • FACTS devices B and C faulty
  • Attempt to Overload line B-C (flow20)

30
Failure Scenario
31
System Framework
32
Status and Results
  • Simple Max Flow is an effective formalism to
    balance power flow
  • Detects Faults
  • Need to measure performance and fault tolerance
    levels.
  • Real-Time algorithm needs to respond before
    cascading failure occurs.
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