Due to the advancements of electrical networks, the operators are able to employ a gamut of information for assessing the state of the infrastructure facilitating diagnosis of potential malfunctions appearing in one or more components of the grid. This paper presents a cognitive fault diagnosis framework for smart grids (SG) which exploits the temporal and functional relationships existing within the datastreams coming from the nodes of the network. The protection of SGs can rely not only on conventional techniques (e.g. circuit breakers) but also on processing information which is available thanks to the information and communication layer. We propose a framework which is able to autonomously learn the model of the nominal state using the respective data by means of hidden Markov models operating in the parameter space of linear time-invariant models. Subsequently, the framework is able to detect data not belonging to the nominal state and localize the potential fault at the cognitive level. The isolation is based on a graph representation of the SG revealing the correlations among the nodes based on the Granger causality. We conducted thorough experiments on the IEEE-9 bus system model achieving encouraging results in terms of false positive/negative rate, and detection/isolation delay.

Fault diagnosis for smart grids in pragmatic conditions

NTALAMPIRAS, STAVROS
2016

Abstract

Due to the advancements of electrical networks, the operators are able to employ a gamut of information for assessing the state of the infrastructure facilitating diagnosis of potential malfunctions appearing in one or more components of the grid. This paper presents a cognitive fault diagnosis framework for smart grids (SG) which exploits the temporal and functional relationships existing within the datastreams coming from the nodes of the network. The protection of SGs can rely not only on conventional techniques (e.g. circuit breakers) but also on processing information which is available thanks to the information and communication layer. We propose a framework which is able to autonomously learn the model of the nominal state using the respective data by means of hidden Markov models operating in the parameter space of linear time-invariant models. Subsequently, the framework is able to detect data not belonging to the nominal state and localize the potential fault at the cognitive level. The isolation is based on a graph representation of the SG revealing the correlations among the nodes based on the Granger causality. We conducted thorough experiments on the IEEE-9 bus system model achieving encouraging results in terms of false positive/negative rate, and detection/isolation delay.
Cognitive fault diagnosis; hidden Markov models; linear time-invariant modelling; Computer Science (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1004309
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