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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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| 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. |
| format | Article |
| id | doaj-art-1dec5ddc401a4c7fa6756b63a27f6c3a |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| 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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