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

Hancong Huangfu; Yongcai Wang; Zhenghao Qian; Yanning Shao

doi:10.4108/eetsis.v10i4.3166

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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