An Optimization Method for the Calculation of SCADA Main Grid's Theoretical Line Loss Based on DBSCAN

Hongyi Cao, Qiaomu Ren, Xiuguo Zou, Shuaitang Zhang and Yan Qian
Volume: 15, No: 5, Page: 1156 ~ 1170, Year: 2019
10.3745/JIPS.02.0119
Keywords: Boxplot Method, DBSCAN Clustering Algorithm, Main Grid, SCADA, Theoretical Line Loss
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Abstract
In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied by researchers in the related domain areas. It has become an important research topic to effectively and reliably find the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has become an important research topic. This paper analyzes the data composition of the smart grid, and explains the power model in two smart grid applications, followed by an analysis on the application of each parameter in densitybased spatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out for the processing effects of the boxplot method, probability weight analysis method and DBSCAN clustering algorithm on the big data driven power grid. According to the comparison results, the performance of the DBSCAN algorithm outperforming other methods in processing effect. The experimental verification shows that the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantly improving the accuracy and reliability of the calculation result of the main grid’s theoretical line loss.

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Cite this article
IEEE Style
H. Cao, Q. Ren, X. Zou, S. Zhang and Y. Qian, "An Optimization Method for the Calculation of SCADA Main Grid's Theoretical Line Loss Based on DBSCAN," Journal of Information Processing Systems, vol. 15, no. 5, pp. 1156~1170, 2019. DOI: 10.3745/JIPS.02.0119.

ACM Style
Hongyi Cao, Qiaomu Ren, Xiuguo Zou, Shuaitang Zhang, and Yan Qian. 2019. An Optimization Method for the Calculation of SCADA Main Grid's Theoretical Line Loss Based on DBSCAN, Journal of Information Processing Systems, 15, 5, (2019), 1156~1170. DOI: 10.3745/JIPS.02.0119.