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

  • Pedro J. Rivera-TorresEmail author
  • Orestes Llanes Santiago
Part of the Studies in Computational Intelligence book series (SCI, volume 872)


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.


Smart power systems Fault detection and isolation Probabilistic Boolean networks Reliability Failure modes 



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. 1.Department of Computer Science-School of Natural SciencesUniversity of Puerto Rico at Río PiedrasSan JuanUSA
  2. 2.Department of Automation and ComputingUniversidad Tecnológica de La Habana José Antonio EcheverríaCUJAECuba

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