Modeling Method of Power Grid CIM Model Based on Graph Data Model
Lecture Notes on Data Engineering and Communications Technologies, ISSN: 2367-4520, Vol: 122, Page: 535-543
2023
- 1Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations1
- Citation Indexes1
Conference Paper Description
With the expansion of power system scale and more frequent operation adjustments, people are increasing the need for real-time analysis and calculations. Graph data model is a new type of data model derived from parallel and analytical processing of a large number of data information on the mobile Internet that has appeared in recent years. Its data model provides users with an intuitive expression of grid topology, and can easily realize the parallel query of the data. This paper aims to study the power grid CIM model modeling method based on the graph data model. On the basis of analyzing the classes and relationships in the graph data model, CIM model and CIM model, a power grid CIM model based on the graph data model is constructed, which mainly includes the power grid equipment asset model and the power grid topology model are two parts, and then the parallel network topology analysis algorithm is proposed and implemented. Finally, the test verification shows that the model and algorithm in this paper can speed up the network topology analysis speed and improve the power grid calculation efficiency.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85133698466&origin=inward; http://dx.doi.org/10.1007/978-981-19-3632-6_64; https://link.springer.com/10.1007/978-981-19-3632-6_64; https://dx.doi.org/10.1007/978-981-19-3632-6_64; https://link.springer.com/chapter/10.1007/978-981-19-3632-6_64
Springer Science and Business Media LLC
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