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Fault Detection and Isolation in Smart Grid Devices Using Probabilistic Boolean Networks

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Computational Intelligence in Emerging Technologies for Engineering Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 872))

Abstract

The area of smart power systems needs continuous improvement of its efficiency and reliability, to produce power with optimal quality in a resilient, fault-tolerant grid. Components must be highly reliable, properly maintained, and the occurrence of faults and failures has to be studied. Guaranteeing correct system operation to performance specifications involving the aforementioned activities is an active research area that applies novel methodology to the detection, classification, and isolation of faults and failures, modeling and simulating processes using predictive algorithms, with innovative AI techniques. To maintain complex power grids, predictive analytics is necessary, as employing it to plan and perform activities lowers maintenance costs and minimizes downtime. Detecting multiple faults in dynamic systems is a difficult task. Biomimetic methodologies have been applied widely in engineering systems to solve many complex problems of this field. This contribution presents a complex-adaptive bioinformatic, self-organizing framework, probabilistic Boolean networks (PBN), as a means to understand the rules that govern dynamic power systems, and to model and analyze their behavior. They are used to describe Gene Regulatory Networks, but have been recently expanding to other fields. PBNs can model system behavior, and with model checking and formal logic, assure the process’ mathematical correct-ness. They enable designers, reliability and electrical engineers, and other experts to make intelligent decisions, since PBNs self-organize into attractors that model the system’s operating modes, permit design for reliability, create intelligent fault diagnosis systems, assist the reliability engineering design process, and use data to analyze a system’s behavior, achieving predictive maintenance.

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Acknowledgements

The authors would like to acknowledge the support of University of Puerto Rico-Río Piedras, and Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAE.

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Authors and Affiliations

  1. Department of Computer Science-School of Natural Sciences, University of Puerto Rico at Río Piedras, San Juan, PR, USA

    Pedro J. Rivera-Torres

  2. Department of Automation and Computing, Universidad Tecnológica de La Habana José Antonio Echeverría, CUJAE, La Habana, Cuba

    Orestes Llanes Santiago

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  1. Pedro J. Rivera-Torres
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  2. Orestes Llanes Santiago
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Correspondence to Pedro J. Rivera-Torres .

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Editors and Affiliations

  1. CUJAE, Universidad Tecnológica de La Habana José Antonio Echeverría, Marianao, La Habana, Cuba

    Orestes Llanes Santiago

  2. University of Granada, Granada, Spain

    Carlos Cruz Corona

  3. Estado do Rio de Janeiro, Instituto Politécnico-Universidade do Estado do Rio de Janeiro, Nova Friburgo, Brazil

    Antônio José Silva Neto

  4. University of Granada, Granada, Spain

    José Luis Verdegay

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Rivera-Torres, P.J., Llanes Santiago, O. (2020). Fault Detection and Isolation in Smart Grid Devices Using Probabilistic Boolean Networks. In: Llanes Santiago, O., Cruz Corona, C., Silva Neto, A., Verdegay, J. (eds) Computational Intelligence in Emerging Technologies for Engineering Applications. Studies in Computational Intelligence, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-030-34409-2_10

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