TY - GEN

T1 - A fuzzy Markov model for scalable reliability analysis of advanced metering infrastructure

AU - Zonouz, Saman A.

AU - Berthier, Robin

AU - Haghani, Parisa

PY - 2012

Y1 - 2012

N2 - The capabilities of smart meters and the potential vulnerabilities they introduce make the Advanced Metering Infrastructure (AMI) a critical components of the Smart Grid. A virus propagating among meters and massively issuing remote disconnect commands could have catastrophic consequences. To understand and assess the risk posed by a given set of meter devices getting compromised, we introduce a novel modeling approach based on fuzzy Markov chain. Markov modeling is one of the main approaches used to analyze the reliability of critical systems. Markov models are usually analyzed using mathematical approaches or Monte-Carlo Simulation. For large and complex systems, traditional Markov models suffer from the state explosion problem and quickly become intractable. The key idea of our approach to solve this issue in the context of the smart grid is to leverage the hierarchical topology of AMI to build a high-level model where components represent Neighborhood Area Networks (NAN) instead of individual meters. The fuzzy Markov formalism enables our solution to take into account gradual component degradation. We propose a Fuzzy Markov Model (FMM) for reliability analysis of a Smart Grid. The reliability of each NAN is represented by a number in [0, 1] which denotes the state of that component in the FMM.We define the transition probabilities between states of the fuzzy Markov model based on the fuzzy time-to-failure value for each NAN. We utilize the Fuzzy Monte Carlo Simulation (FMCS) approach to calculate the fuzzy time-to-failure value of each NAN. Our proposed method enables us to assess the reliability of the entire AMI at the meter level while keeping the model manageable.

AB - The capabilities of smart meters and the potential vulnerabilities they introduce make the Advanced Metering Infrastructure (AMI) a critical components of the Smart Grid. A virus propagating among meters and massively issuing remote disconnect commands could have catastrophic consequences. To understand and assess the risk posed by a given set of meter devices getting compromised, we introduce a novel modeling approach based on fuzzy Markov chain. Markov modeling is one of the main approaches used to analyze the reliability of critical systems. Markov models are usually analyzed using mathematical approaches or Monte-Carlo Simulation. For large and complex systems, traditional Markov models suffer from the state explosion problem and quickly become intractable. The key idea of our approach to solve this issue in the context of the smart grid is to leverage the hierarchical topology of AMI to build a high-level model where components represent Neighborhood Area Networks (NAN) instead of individual meters. The fuzzy Markov formalism enables our solution to take into account gradual component degradation. We propose a Fuzzy Markov Model (FMM) for reliability analysis of a Smart Grid. The reliability of each NAN is represented by a number in [0, 1] which denotes the state of that component in the FMM.We define the transition probabilities between states of the fuzzy Markov model based on the fuzzy time-to-failure value for each NAN. We utilize the Fuzzy Monte Carlo Simulation (FMCS) approach to calculate the fuzzy time-to-failure value of each NAN. Our proposed method enables us to assess the reliability of the entire AMI at the meter level while keeping the model manageable.

UR - http://www.scopus.com/inward/record.url?scp=84860870443&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860870443&partnerID=8YFLogxK

U2 - 10.1109/ISGT.2012.6175770

DO - 10.1109/ISGT.2012.6175770

M3 - Conference contribution

AN - SCOPUS:84860870443

SN - 9781457721588

T3 - 2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012

BT - 2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012

T2 - 2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012

Y2 - 16 January 2012 through 20 January 2012

ER -