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On-line solution of constrained optimization problems is an essential requirement for a secure, reliable and economic smart grids operation. In this context, the stream of data acquired by the grid sensors should be promptly analyzed in order to identify proper control actions aimed at mitigating the effect of system perturbations, or adapting the grid state to new load and/or generation patterns...
System fault detection and recovery deals with a decision problem under uncertainty in which we first attempt to isolate a fault according to information we collect regarding the system behavior, and after to recovery from the failure by the application of some recovery actions. In this paper we propose a method which makes use of Bayesian networks to reason under uncertainty and decision analysis...
Fault identification represents the preliminary step in solving or preventing a failure in reliable systems. The identification passes through the recording and analysis of events used as probes of underlying fault. Sometimes those events are incomplete, they can have in any order and novel in part. In this paper we propose a model based on Bayesian reasoning as means to isolate compatible faults.
Reliability of complex systems requires to take into account possible failures and strategies to detect and recover from system faults. This leads designers to consider models and algorithms capable of simulating and verifying fault detection, isolation and recovery (FDIR) strategies in different scenarios, characterized by uncertainty and partial information. Different solutions have been proposed...
FDIR functionalities are investigated since the very beginning of a space mission and play a relevant role in the definition of its autonomy, reliability and availability objectives. In this paper, an analytical methodology derived from the Timed Failure Propagation Graph (ETFPG) is proposed. TPFG is a causal model that captures the temporal aspects of failure propagation in a wide variety of engineering...
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