Energy-Efficient Method Based on Dynamic Topology Switching and Reliability in SDNs

Wang, Ying; An, Hengbin; Ba, Junhua; Yu, Peng; Feng, Yining; Zhang, Wei; Kadoch, Michel; Cheriet, Mohamed

doi:10.1109/tsusc.2021.3116325

Cited by 1 publication

(1 citation statement)

References 39 publications

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“…It has been widely used in EPCN to achieve intelligent perception, efficient control, and decision-making in the power grid. With the continuous expansion of EPCN scale and the improvement of informatization, integration, and intelligence, the deep coupling between power systems and information systems has led to increasingly complex and heterogeneous network topologies [7][8] . In addition, in EPCN, critical nodes and links often bear a substantial volume of service data, resulting in increased data transmission latency, inadequate traffic collaboration capability, low resource utilization, and potential service security risks [9][10] .…”

Section: Introductionmentioning

confidence: 99%

Traffic collaboration optimization method for power communication network based on segmentation learning

Kang,

Wu,

Zhang

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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The rapid development of the energy internet, the deep integration of the power system and information system, and the emergence of various new services in the distribution network have led to an explosion of service data. Traditional routing optimization methods are inadequate in satisfying the efficiency and reliability requirements of the power communication network for data transmission. This paper presents a collaborative optimization method for enhancing the traffic in the power communication network based on segmentation learning. Firstly, we propose a power service data transmission routing architecture in EPCN, where multiple routes between the source and destination nodes are available. Secondly, a segmentation learning-based traffic collaboration optimization algorithm for EPCN is proposed, which divides the traffic to explore the transmission performance of multiple routes within one optimization, thereby reducing data congestion at critical nodes. Finally, simulation results demonstrate that the proposed algorithm outperforms in terms of data transmission delay and routing optimization speed.

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