State fusion evaluation and fusion compression method of multi-source sensors in power distribution Internet of things

Liu, Juan; Lu, Liping; Ju, Dengfeng; Jia, Yuxi; Qin, Jianhua; Dai, Jianzhuo; Wang, Junxin; Xiao, Xiaolong; Xiang, Chenmeng

doi:10.1109/ihmsc49165.2020.00044

Cited by 1 publication

(1 citation statement)

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“…For a long time, the PD-IoT huge amounts of data analysis of mining have been considered in some previous works using different methods, Dashtdar et al (2021) for the distribution network, such as data source and data analysis, and designed four levels of distribution network operation data analysis system architecture. Liu et al (2020) and Li et al (2021a) proposed an application method of edge computing technology in the internet of things of distribution network and deeply analyzed the shortcomings of the practical application of edge computing technology in the distribution industry. Han et al (2021) proposed an application method of edge computing architecture in the smart power grid model and, combined with intelligent terminal data and operational measurement data, elaborated on the significance of edge computing in data security and efficiency analysis.…”

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%