Real-time Distributed Computing Model of Low-Voltage Flow Data in Digital Power Grid under Autonomous and Controllable Environments

Authors

  • Hancong Huangfu Foshan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangdong, China
  • Yongcai Wang Foshan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangdong, China
  • Zhenghao Qian Guangdong Power Grid, Guangzhou,China
  • Yanning Shao Guangdong Power Grid, Guangzhou,China

DOI:

https://doi.org/10.4108/eetsis.v10i4.3166

Keywords:

Distribute computing, wireless offloading, outage probability, performance analysis

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 low-voltage flow data, making it a valuable contribution to the field of digital power grids.

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Published

01-06-2023

How to Cite

1.
Huangfu H, Wang Y, Qian Z, Shao Y. Real-time Distributed Computing Model of Low-Voltage Flow Data in Digital Power Grid under Autonomous and Controllable Environments. EAI Endorsed Scal Inf Syst [Internet]. 2023 Jun. 1 [cited 2024 Jul. 22];10(5). Available from: https://publications.eai.eu/index.php/sis/article/view/3166