Software-Defined Network-Based Proactive Routing Strategy in Smart Power Grids Using Graph Neural Network and Reinforcement Learning
Smart power grid relies on sensors and actuators to provide continuous monitoring and precise control functions. Two types of data and command packets are associated with such field devices, namely, periodic fixed scheduling (FS) and emergency-related event-driven (ED) packets, which require differe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671123000827 |
| _version_ | 1797674930791251968 |
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| author | Md Aminul Islam Muhammad Ismail Rachad Atat Osman Boyaci Susmit Shannigrahi |
| author_facet | Md Aminul Islam Muhammad Ismail Rachad Atat Osman Boyaci Susmit Shannigrahi |
| author_sort | Md Aminul Islam |
| collection | DOAJ |
| description | Smart power grid relies on sensors and actuators to provide continuous monitoring and precise control functions. Two types of data and command packets are associated with such field devices, namely, periodic fixed scheduling (FS) and emergency-related event-driven (ED) packets, which require different levels of quality-of-service (QoS) support. However, existing routing strategies in smart power grids are not adaptive to network conditions and cannot guarantee differentiated QoS support. To overcome this limitation, we propose a software-defined network (SDN) proactive routing framework in smart grids that takes into account the current and future state of the network while making the routing decisions. The proposed framework offers the following features: (a) It sets up separate queues for ED and FS packets, with higher priority for the ED queue; (b) It predicts the future traffic condition at each switch within the network (congested or not congested) using a graph-neural-network (GNN) model that provides an accurate prediction of the traffic condition despite the sparsity of the ED events; (c) It adopts a reinforcement learning (RL) strategy that establishes an ideal route and updates the queue service rate for each switch along the route following the network’s current and predicted future congestion condition. The proposed framework is tested on the cyber layers of the IEEE 14-bus and 39-bus test systems, and compared to two benchmarks. Our results indicate the superiority of our proposed framework compared with the benchmarks. |
| first_indexed | 2024-03-11T22:07:18Z |
| format | Article |
| id | doaj.art-d109dc2074c449ebb80f60e5b597e389 |
| institution | Directory Open Access Journal |
| issn | 2772-6711 |
| language | English |
| last_indexed | 2024-03-11T22:07:18Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj.art-d109dc2074c449ebb80f60e5b597e3892023-09-25T04:12:38ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112023-09-015100187Software-Defined Network-Based Proactive Routing Strategy in Smart Power Grids Using Graph Neural Network and Reinforcement LearningMd Aminul Islam0Muhammad Ismail1Rachad Atat2Osman Boyaci3Susmit Shannigrahi4Corresponding author.; Department of Computer Science, Tennessee Technological University, Cookeville, TN, USADepartment of Computer Science, Tennessee Technological University, Cookeville, TN, USADepartment of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, QatarDepartment of Electrical and Computer Engineering, Texas A&M University, TX, USADepartment of Computer Science, Tennessee Technological University, Cookeville, TN, USASmart power grid relies on sensors and actuators to provide continuous monitoring and precise control functions. Two types of data and command packets are associated with such field devices, namely, periodic fixed scheduling (FS) and emergency-related event-driven (ED) packets, which require different levels of quality-of-service (QoS) support. However, existing routing strategies in smart power grids are not adaptive to network conditions and cannot guarantee differentiated QoS support. To overcome this limitation, we propose a software-defined network (SDN) proactive routing framework in smart grids that takes into account the current and future state of the network while making the routing decisions. The proposed framework offers the following features: (a) It sets up separate queues for ED and FS packets, with higher priority for the ED queue; (b) It predicts the future traffic condition at each switch within the network (congested or not congested) using a graph-neural-network (GNN) model that provides an accurate prediction of the traffic condition despite the sparsity of the ED events; (c) It adopts a reinforcement learning (RL) strategy that establishes an ideal route and updates the queue service rate for each switch along the route following the network’s current and predicted future congestion condition. The proposed framework is tested on the cyber layers of the IEEE 14-bus and 39-bus test systems, and compared to two benchmarks. Our results indicate the superiority of our proposed framework compared with the benchmarks.http://www.sciencedirect.com/science/article/pii/S2772671123000827Smart power gridSoftware-defined networkingReinforcement learningTraffic predictionGraph neural networkMachine learning-based routing |
| spellingShingle | Md Aminul Islam Muhammad Ismail Rachad Atat Osman Boyaci Susmit Shannigrahi Software-Defined Network-Based Proactive Routing Strategy in Smart Power Grids Using Graph Neural Network and Reinforcement Learning e-Prime: Advances in Electrical Engineering, Electronics and Energy Smart power grid Software-defined networking Reinforcement learning Traffic prediction Graph neural network Machine learning-based routing |
| title | Software-Defined Network-Based Proactive Routing Strategy in Smart Power Grids Using Graph Neural Network and Reinforcement Learning |
| title_full | Software-Defined Network-Based Proactive Routing Strategy in Smart Power Grids Using Graph Neural Network and Reinforcement Learning |
| title_fullStr | Software-Defined Network-Based Proactive Routing Strategy in Smart Power Grids Using Graph Neural Network and Reinforcement Learning |
| title_full_unstemmed | Software-Defined Network-Based Proactive Routing Strategy in Smart Power Grids Using Graph Neural Network and Reinforcement Learning |
| title_short | Software-Defined Network-Based Proactive Routing Strategy in Smart Power Grids Using Graph Neural Network and Reinforcement Learning |
| title_sort | software defined network based proactive routing strategy in smart power grids using graph neural network and reinforcement learning |
| topic | Smart power grid Software-defined networking Reinforcement learning Traffic prediction Graph neural network Machine learning-based routing |
| url | http://www.sciencedirect.com/science/article/pii/S2772671123000827 |
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