Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices
The area of smart power grids needs to constantly improve its efficiency and resilience, to provide high quality electrical power in a resilient grid, while managing faults and avoiding failures. Achieving this requires high component reliability, adequate maintenance, and a studied failure occurren...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/3652441 |
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| author | Pedro Juan Rivera Torres Carlos Gershenson García María Fernanda Sánchez Puig Samir Kanaan Izquierdo |
| author_facet | Pedro Juan Rivera Torres Carlos Gershenson García María Fernanda Sánchez Puig Samir Kanaan Izquierdo |
| author_sort | Pedro Juan Rivera Torres |
| collection | DOAJ |
| description | The area of smart power grids needs to constantly improve its efficiency and resilience, to provide high quality electrical power in a resilient grid, while managing faults and avoiding failures. Achieving this requires high component reliability, adequate maintenance, and a studied failure occurrence. Correct system operation involves those activities and novel methodologies to detect, classify, and isolate faults and failures and model and simulate processes with predictive algorithms and analytics (using data analysis and asset condition to plan and perform activities). In this paper, we showcase the application of a complex-adaptive, self-organizing modeling method, and Probabilistic Boolean Networks (PBNs), as a way towards the understanding of the dynamics of smart grid devices, and to model and characterize their behavior. This work demonstrates that PBNs are equivalent to the standard Reinforcement Learning Cycle, in which the agent/model has an interaction with its environment and receives feedback from it in the form of a reward signal. Different reward structures were created to characterize preferred behavior. This information can be used to guide the PBN to avoid fault conditions and failures. |
| format | Article |
| id | doaj-art-47d27450db5c46c48462d9f5a86adf7a |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-47d27450db5c46c48462d9f5a86adf7a2025-02-03T01:23:09ZengWileyComplexity1099-05262022-01-01202210.1155/2022/3652441Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid DevicesPedro Juan Rivera Torres0Carlos Gershenson García1María Fernanda Sánchez Puig2Samir Kanaan Izquierdo3Centro de Ciencias de La Complejidad (C3)Centro de Ciencias de La Complejidad (C3)Centro de Ciencias de La Complejidad (C3)Bioinformatics and Biomedical Signals LaboratoryThe area of smart power grids needs to constantly improve its efficiency and resilience, to provide high quality electrical power in a resilient grid, while managing faults and avoiding failures. Achieving this requires high component reliability, adequate maintenance, and a studied failure occurrence. Correct system operation involves those activities and novel methodologies to detect, classify, and isolate faults and failures and model and simulate processes with predictive algorithms and analytics (using data analysis and asset condition to plan and perform activities). In this paper, we showcase the application of a complex-adaptive, self-organizing modeling method, and Probabilistic Boolean Networks (PBNs), as a way towards the understanding of the dynamics of smart grid devices, and to model and characterize their behavior. This work demonstrates that PBNs are equivalent to the standard Reinforcement Learning Cycle, in which the agent/model has an interaction with its environment and receives feedback from it in the form of a reward signal. Different reward structures were created to characterize preferred behavior. This information can be used to guide the PBN to avoid fault conditions and failures.http://dx.doi.org/10.1155/2022/3652441 |
| spellingShingle | Pedro Juan Rivera Torres Carlos Gershenson García María Fernanda Sánchez Puig Samir Kanaan Izquierdo Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices Complexity |
| title | Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices |
| title_full | Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices |
| title_fullStr | Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices |
| title_full_unstemmed | Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices |
| title_short | Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices |
| title_sort | reinforcement learning with probabilistic boolean network models of smart grid devices |
| url | http://dx.doi.org/10.1155/2022/3652441 |
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