An Optimization Method for the Calculation of SCADA Main Grid's Theoretical Line Loss Based on DBSCAN
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초록
In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studiedby researchers in the related domain areas. It has become an important research topic to effectively and reliablyfind the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has becomean important research topic. This paper analyzes the data composition of the smart grid, and explains the powermodel in two smart grid applications, followed by an analysis on the application of each parameter in densitybasedspatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out forthe processing effects of the boxplot method, probability weight analysis method and DBSCAN clusteringalgorithm on the big data driven power grid. According to the comparison results, the performance of theDBSCAN algorithm outperforming other methods in processing effect. The experimental verification showsthat the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantlyimproving the accuracy and reliability of the calculation result of the main grid’s theoretical line loss.
Abstract
In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studiedby researchers in the related domain areas. It has become an important research topic to effectively and reliablyfind the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has becomean important research topic. This paper analyzes the data composition of the smart grid, and explains the powermodel in two smart grid applications, followed by an analysis on the application of each parameter in densitybasedspatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out forthe processing effects of the boxplot method, probability weight analysis method and DBSCAN clusteringalgorithm on the big data driven power grid. According to the comparison results, the performance of theDBSCAN algorithm outperforming other methods in processing effect. The experimental verification showsthat the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantlyimproving the accuracy and reliability of the calculation result of the main grid’s theoretical line loss.
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