Performance and reliability analysis of computer systems: an example-based approach using the SHARPE software package
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Bayesian Networks for Reliability Analysis of Complex Systems
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
A Loading-Dependent Model of Probabilistic Cascading Failure
Probability in the Engineering and Informational Sciences
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Reliability modeling for the advanced electric power grid
SAFECOMP'07 Proceedings of the 26th international conference on Computer Safety, Reliability, and Security
Engineering Applications of Artificial Intelligence
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In recent years, the growing interest toward complex critical infrastructures and their interdependencies have solicited new efforts in the area of modeling and analysis of large interdependent systems. Cascading effects are a typical phenomenon of dependencies of components inside a system or among systems. The present paper deals with the modeling of cascading effects in a power grid. In particular, we propose to model such effects in the form of dynamic Bayesian networks (DBN) which can be derived by means of specific rules, from the power grid structure expressed in terms of series and parallel modules. In contrast with the available techniques, DBN offer a good trade-off between the analytical tractability and the representation of the propagation of the cascading event. A case study taken from the literature, is considered as running example.