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- Title
Real-time Distributed Computing Model of Low-Voltage Flow Data in Digital Power Grid under Autonomous and Controllable Environments.
- Authors
Hancong Huangfu; Yongcai Wang; Zhenghao Qian; Yanning Shao
- Abstract
Motivated by the progress in artificial intelligence and edge computing, this paper proposes a real-time distributed computing model for low-voltage flow data in digital power grids under autonomous and controllable environments. The model utilizes edge computing through wireless offloading to efficiently process and analyze data generated by low-voltage devices in the power grid. Firstly, we evaluate the performance of the system under consideration by measuring its outage probability, utilizing both the received signal-to-noise ratio (SNR) and communication and computing latency. Subsequently, we analyze the system's outage probability by deriving an analytical expression. To this end, we utilize the Gauss-Chebyshev approximation to provide an approximate closed-form expression. The results of our experimental evaluation demonstrate the effectiveness of the proposed model in achieving real-time processing of low-voltage flow data in digital power grids. Our model provides an efficient and practical solution for the processing of lowvoltage flow data, making it a valuable contribution to the field of digital power grids.
- Publication
EAI Endorsed Transactions on Scalable Information Systems, 2023, Vol 10, Issue 5, p1
- ISSN
2032-9407
- Publication type
Article
- DOI
10.4108/eetsis.v10i4.3166