Analysis and Processing of Power Distribution Data Based on Edge Computing

Luo, Anqin; Chen, Zhimin; Yuan, Jianan; Xie, Fang

doi:10.1109/icccs52626.2021.9449107

Cited by 3 publications

(1 citation statement)

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“…Due to the rapid increase of massive data and the influence of data complexity, the upstream terminal monitoring data and power grid operation data, as well as the downstream cloud computing processing information, bring great pressure to the communication transmission layer and seriously restrict the promotion of PD-IoT (Zhang et al, 2018). In-depth exploration of the application potential of edge computing has become a major research focus, and more and more distribution network data information terminal processing, marginal computing, and local solutions have become an important way (Khalifa et al, 2018;Luo et al, 2021). However, the marginalization integration of complex multi-source heterogeneous data brings new challenges to efficient edge computing in the PD-IoT mode.…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Data Processing and Fusion Method for Power Distribution Internet of Things Based on Edge Intelligence

Yuan

Kou

et al. 2022

Front. Energy Res.

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With the rapid advancement of the Energy Internet strategy, the number of sensors within the Power Distribution Internet of Things (PD-IoT) has increased dramatically. In this study, an edge intelligence-based PD-IoT multi-source data processing and fusion method is proposed to solve the problems of confusing storage and insufficient fusion computing performance of multi-source heterogeneous distribution data. First, a PD-IoT multi-source data processing and fusion architecture based on edge smart terminals is designed. Second, the multi-source sensor data in the distribution network is unified in dimension and magnitude. By introducing the Box–Cox transform to improve the data offset problem in the Z-score normalization process, a multi-source heterogeneous data processing method for distribution networks based on the Box–Cox transform Z-score is proposed. Then, the conflicting phenomena of DS inference methods in data source fusion are optimally handled based on the PCA algorithm. A multi-source data fusion model based on DS inference with conflict optimization is constructed to ensure the effective fusion of distribution data sources from different domains. Finally, the effectiveness of the proposed method is verified by an experimental analysis of an IEEE39 node system in a regional distribution network in China.

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Section: Introductionmentioning

confidence: 99%