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

Pedro Juan Rivera Torres (), Chen Chen, Jaime Macías-Aguayo, Sara Rodríguez González, Javier Prieto Tejedor, Orestes Llanes Santiago, Carlos Gershenson García and Samir Kanaan Izquierdo
Additional contact information
Pedro Juan Rivera Torres: Department of Computer Science and Automatics, Universidad de Salamanca, Patio de las Escuelas 1, 37006 Salamanca, 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

Energies, 2024, vol. 17, issue 24, 1-21

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.

Keywords: fault detection and isolation; machine learning algorithms; probabilistic Boolean networks; probabilistic Boolean network modeling; smart grids; complex network modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/24/6399/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/24/6399/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:24:p:6399-:d:1547760

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().