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

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 sign...

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Main Authors: Pedro Juan Rivera Torres, Chen Chen, Jaime Macías-Aguayo, Sara Rodríguez González, Javier Prieto Tejedor, Orestes Llanes Santiago, Carlos Gershenson García, Samir Kanaan Izquierdo
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Energies
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Online Access:https://www.mdpi.com/1996-1073/17/24/6399
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author Pedro Juan Rivera Torres
Chen Chen
Jaime Macías-Aguayo
Sara Rodríguez González
Javier Prieto Tejedor
Orestes Llanes Santiago
Carlos Gershenson García
Samir Kanaan Izquierdo
author_facet Pedro Juan Rivera Torres
Chen Chen
Jaime Macías-Aguayo
Sara Rodríguez González
Javier Prieto Tejedor
Orestes Llanes Santiago
Carlos Gershenson García
Samir Kanaan Izquierdo
author_sort Pedro Juan Rivera Torres
collection DOAJ
description 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.
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spelling doaj-art-1dec5ddc401a4c7fa6756b63a27f6c3a2025-08-20T02:50:53ZengMDPI AGEnergies1996-10732024-12-011724639910.3390/en17246399A Learning Probabilistic Boolean Network Model of a Smart Grid with Applications in System MaintenancePedro Juan Rivera Torres0Chen Chen1Jaime Macías-Aguayo2Sara Rodríguez González3Javier Prieto Tejedor4Orestes Llanes Santiago5Carlos Gershenson García6Samir Kanaan Izquierdo7Department of Computer Science and Automatics, Universidad de Salamanca, Patio de las Escuelas 1, 37006 Salamanca, SpainDepartment of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, UKCenter for Transportation and Logistics, Massachusetts Institute of Technology, 1 Amherst Street, MIT Building E40-376, Cambridge, MA 02139, USADepartment of Computer Science and Automatics, Universidad de Salamanca, Patio de las Escuelas 1, 37006 Salamanca, SpainDepartment of Computer Science and Automatics, Universidad de Salamanca, Patio de las Escuelas 1, 37006 Salamanca, SpainDepartamento de Control y Automática, Instituto Superior Politécnico José Antonio Echeverría (CUJAE), Marianao, La Havana 19390, CubaSchool of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USAEscuela Técnica Superior de Ingeniería Industrial de Barcelona, Universidad Politécnica de Cataluña, Av. Diagonal, 647, 08028 Barcelona, SpainProbabilistic 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.https://www.mdpi.com/1996-1073/17/24/6399fault detection and isolationmachine learning algorithmsprobabilistic Boolean networksprobabilistic Boolean network modelingsmart gridscomplex network modeling
spellingShingle Pedro Juan Rivera Torres
Chen Chen
Jaime Macías-Aguayo
Sara Rodríguez González
Javier Prieto Tejedor
Orestes Llanes Santiago
Carlos Gershenson García
Samir Kanaan Izquierdo
A Learning Probabilistic Boolean Network Model of a Smart Grid with Applications in System Maintenance
Energies
fault detection and isolation
machine learning algorithms
probabilistic Boolean networks
probabilistic Boolean network modeling
smart grids
complex network modeling
title A Learning Probabilistic Boolean Network Model of a Smart Grid with Applications in System Maintenance
title_full A Learning Probabilistic Boolean Network Model of a Smart Grid with Applications in System Maintenance
title_fullStr A Learning Probabilistic Boolean Network Model of a Smart Grid with Applications in System Maintenance
title_full_unstemmed A Learning Probabilistic Boolean Network Model of a Smart Grid with Applications in System Maintenance
title_short A Learning Probabilistic Boolean Network Model of a Smart Grid with Applications in System Maintenance
title_sort learning probabilistic boolean network model of a smart grid with applications in system maintenance
topic fault detection and isolation
machine learning algorithms
probabilistic Boolean networks
probabilistic Boolean network modeling
smart grids
complex network modeling
url https://www.mdpi.com/1996-1073/17/24/6399
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