probabilisticbooleannetworks can capture the dynamics of complex biological systems as well as other non-biological systems, such as manufacturing systems and smart grids. In this proof-of-concept manuscript, we pro...
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probabilisticbooleannetworks can capture the dynamics of complex biological systems as well as other non-biological systems, such as manufacturing systems and smart grids. In this proof-of-concept manuscript, we propose a probabilisticbooleannetwork architecture with a learning process that significantly improves the prediction of the occurrence of faults and failures in smart-grid systems. This idea was tested in a probabilisticbooleannetwork model of the WSCC nine-bus system that incorporates Intelligent Power Routers on every bus. The model learned the equality and negation functions in the different experiments performed. We take advantage of the complex properties of probabilisticbooleannetworks to use them as a positive feedback adaptive learning tool and to illustrate that these networks could have a more general use than previously thought. This multi-layered PBN architecture provides a significant improvement in terms of performance for fault detection, within a positive-feedback network structure that is more tolerant of noise than other techniques.
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