Power system state monitoring big data query based on multilevel index Online publication date: Thu, 12-Oct-2023
by Zeyuan Zhou; Junrong Liu; Linyan Zhou
International Journal of Energy Technology and Policy (IJETP), Vol. 18, No. 3/4/5, 2023
Abstract: The continuous operation of the power system generates a large amount of state data. By querying this data, the operation status of the power system can be judged, which is beneficial for improving the stability of the power system operation. Therefore, a multilevel index based big data query method for power system state monitoring is proposed. Firstly, density clustering algorithm is used to cluster the big data of power system status monitoring. Secondly, based on the clustering results, a distance sensitive hash algorithm is used to represent the mapping relationship of data points, and a multilevel index structure is constructed to complete the query of big data for power system status monitoring. The experimental results show that the proposed method reduces the response time of big data queries for power system status monitoring, improves query throughput and accuracy, and achieves a maximum query accuracy of 94.24%.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Energy Technology and Policy (IJETP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook