| Grant number: | 18/19150-6 |
| Support Opportunities: | Scholarships abroad - Research |
| Start date: | December 01, 2018 |
| End date: | January 31, 2019 |
| Field of knowledge: | Engineering - Electrical Engineering - Power Systems |
| Principal Investigator: | Carlos Dias Maciel |
| Grantee: | Carlos Dias Maciel |
| Host Investigator: | Marco Aiello |
| Host Institution: | Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| Institution abroad: | University of Stuttgart, Germany |
| Associated research grant: | 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment, AP.TEM |
Abstract Complex systems are composed of different elements, but in opposition to other systems, their overall behavior appears as a non-trivial combination of their parts. As they are organized without any general rule, such phenomena are seen both in nature and in modern engineering systems, and their behavior is related to the connectivity and dynamics of the elements. Resilience is related to a system's ability to be robust to loss of performance, to overcome the consequences of damage after harmful events and to be improved for future damage occurrences. Increasing the complexity of systems imposes constraints on observability and modeling, and their variables and the internal process need to be treated as random variables. Pearl (2016) shows that the search for dependence between these processes indicates the concepts of causality and the possibility of a deeper understanding of the behavior of the system. The use of probabilistic models has become one of the main approaches to evaluating complex systems - using the prior information of the systems along with collected data (or evidence) can determine the possible causes of failures and future actions. The use of graphical models to represent causality information is known as Bayesian (dynamic) RBD networks. Dynamic Bayesian networks are graphical models that use an acyclic graph directed towards the representation of cause and effect models and their temporal evolution. The learning of Bayesian networks presents many challenges, since these models have a super exponential growth, necessitating computationally intense algorithms and using parallel approaches. The present project aims at the use of RBD to model the performance of critical systems, especially the energy distribution, aiming at a better use of the concepts of resilience. (AU) | |
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