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Counting-Based Reliability Estimation for Power-Transmission Grids.
Leonardo Duenas-Osorio, Kuldeep S. Meel, Roger Paredes and Moshe Y. Vardi.
In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), February 2017.
Modern society is increasingly reliant on the functionality of infrastructure facilities and utility services. Consequently, there has been surge of interest in the problem of quantification of system reliability, which is known to be #P-complete. Reliability also contributes to the resilience of systems, so as to effectively make them bounce back after contingencies. Despite diverse progress, most techniques to estimate system reliability and resilience remain computationally expensive. In this paper, we investigate how recent advances in hashing-based approaches to counting can be exploited to improve computational techniques for system reliability. The primary contribution of this paper is a novel framework, RelNet, that provides provably approximately correct (PAC) estimates for arbitrary networks. We then apply RelNet to ten real world power transmission grids across different cities in the U.S. and are able to obtain, to the best of our knowledge, the first theoretically sound a priori estimates of reliability between several pairs of nodes of interest. Such estimates will help managing uncertainty and support rational decision making for community resilience.
@inproceedings{DMPV17,
title={Counting-Based Reliability Estimation for Power-Transmission Grids},
author={
Duenas-Osorio, Leonardo and Meel, Kuldeep S. and Paredes, Roger and Vardi,
Moshe Y.
},
year={2017},
booktitle=AAAI,
month=feb,
bib2html_dl_pdf={../Papers/AAAI17.pdf},
bib2html_pubtype={Refereed Conference},
bib2html_rescat={Counting},
abstract={
Modern society is increasingly reliant on the functionality of
infrastructure facilities and utility services. Consequently, there
has been surge of interest in the problem of quantification of
system reliability, which is known to be #P-complete. Reliability also
contributes to the resilience of systems, so as to effectively make them
bounce back after contingencies. Despite
diverse progress, most techniques to estimate system reliability and
resilience remain computationally expensive. In this paper, we investigate
how recent advances in hashing-based approaches to counting can
be exploited to improve computational techniques for system
reliability.
The primary contribution of this paper is a novel
framework, RelNet, that provides provably approximately correct (PAC)
estimates for arbitrary networks.
We then apply RelNet to ten real world power transmission grids across
different cities in the U.S. and are able to obtain, to the best of
our knowledge, the first theoretically sound a priori estimates of
reliability between several pairs of nodes of interest. Such estimates will
help managing uncertainty and support rational decision making for community
resilience.
},
}
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