Dettagli Pubblicazione
| Autori: | Andrea Bobbio |
| Daniele Codetta Raiteri |
| Stefania Montani |
| Luigi Portinale |
| Area Scientifica: | Artificial Intelligence |
| Diagnosis |
| Uncertain Reasoning |
| Probabilistic Graphical Models |
| Dependability and Reliability |
| Formal Models |
| Titolo: | A dynamic Bayesian network based framework to evaluate cascading effects in a power grid |
| Apparso su: | Engineering Applications of Artificial Intelligence vol. 25(4) |
| Pagine: | 683-697 |
| Editore: | Elsevier |
| Anno: | 2012 |
| Tipo Pubblicazione: | Paper on International Journal |
| URL: | http://dx.doi.org/10.1016/j.engappai.2010.06.005 |
| Sommario: | 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. |
