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Multi-Source and Heterogeneous Data Integration Model for Big Data Analytics in Power DCS

Authors: Wengang Chen; Ruijie Wang 0008; Run-ze Wu; Liangrui Tang; Junli Fan;

Multi-Source and Heterogeneous Data Integration Model for Big Data Analytics in Power DCS

Abstract

It is the vital significance for the strong and smart grid that big data analytics technologies apply in the power system. The multi-source and heterogeneous data integration technology based on big data platform is one of the indispensable content. As there are the problems of data heterogeneity and data islands in the dispatching and control system, a multi-source and heterogeneous data integration model is proposed for big data analytics. This model exists the data integration layer in the platform of big data analytics. The model can improve the Extract-Transform-Load (ETL) process in the big data platform according to the extracting rules and transform rules, which are made by uniform data model in the panoramic dispatching and control system. Research shows that the integrating model developed here is efficient to establish panoramic data and can adapt to various data sources by building uniform data model in the power dispatching and control system. With the development of big data technology, it is expected that the data integration model will be improved and used in more electric power applications.

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Top 10%
Average
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