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A Learning Probabilistic Boolean Network Model of a Smart Grid with Applications in System Maintenance

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  • Pedro Juan Rivera Torres

    (Department of Computer Science and Automatics, Universidad de Salamanca, Patio de las Escuelas 1, 37006 Salamanca, Spain
    St. Edmund’s College, University of Cambridge, Mount Pleasant, Cambridge CB3 0BN, UK
    Escuela Técnica Superior de Ingeniería Industrial de Barcelona, Universidad Politécnica de Cataluña, Av. Diagonal, 647, 08028 Barcelona, Spain)

  • Chen Chen

    (Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, UK)

  • Jaime Macías-Aguayo

    (Center for Transportation and Logistics, Massachusetts Institute of Technology, 1 Amherst Street, MIT Building E40-376, Cambridge, MA 02139, USA)

  • Sara Rodríguez González

    (Department of Computer Science and Automatics, Universidad de Salamanca, Patio de las Escuelas 1, 37006 Salamanca, Spain)

  • Javier Prieto Tejedor

    (Department of Computer Science and Automatics, Universidad de Salamanca, Patio de las Escuelas 1, 37006 Salamanca, Spain)

  • Orestes Llanes Santiago

    (Departamento de Control y Automática, Instituto Superior Politécnico José Antonio Echeverría (CUJAE), Marianao, La Havana 19390, Cuba)

  • Carlos Gershenson García

    (School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA)

  • Samir Kanaan Izquierdo

    (Escuela Técnica Superior de Ingeniería Industrial de Barcelona, Universidad Politécnica de Cataluña, Av. Diagonal, 647, 08028 Barcelona, Spain)

Abstract

Probabilistic Boolean Networks 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 Probabilistic Boolean Network 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 Probabilistic Boolean Network 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 Probabilistic Boolean Networks 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.

Suggested Citation

  • Pedro Juan Rivera Torres & Chen Chen & Jaime Macías-Aguayo & Sara Rodríguez González & Javier Prieto Tejedor & Orestes Llanes Santiago & Carlos Gershenson García & Samir Kanaan Izquierdo, 2024. "A Learning Probabilistic Boolean Network Model of a Smart Grid with Applications in System Maintenance," Energies, MDPI, vol. 17(24), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6399-:d:1547760
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    References listed on IDEAS

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    1. Pedro J. Rivera Torres & Eileen I. Serrano Mercado & Orestes Llanes Santiago & Luis Anido Rifón, 2018. "Erratum to: Modeling preventive maintenance of manufacturing processes with probabilistic Boolean networks with interventions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1953-1953, December.
    2. Pedro J. Rivera Torres & Eileen I. Serrano Mercado & Orestes Llanes Santiago & Luis Anido Rifón, 2018. "Modeling preventive maintenance of manufacturing processes with probabilistic Boolean networks with interventions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1941-1952, December.
    3. Pedro J. Rivera Torres & Eileen I. Serrano Mercado & Luis Anido Rifón, 2018. "Probabilistic Boolean network modeling and model checking as an approach for DFMEA for manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1393-1413, August.
    4. Pedro J. Rivera Torres & Eileen I. Serrano Mercado & Luis Anido Rifón, 2018. "Erratum to: Probabilistic Boolean network modeling and model checking as an approach for DFMEA for manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1415-1415, August.
    5. Pedro J. Rivera Torres & E. I. Serrano Mercado & Luis Anido Rifón, 2018. "Probabilistic Boolean network modeling of an industrial machine," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 875-890, April.
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