The Brief Assessment of Performances of Electrical Components Grids

In this study, we explore the vital importance of electrical measurements in the smart grid setting, especially for data-driven applications. We highlight the close connection between data quality and smart grid data mining and provide a novel fuzzy evaluation technique for multivariate electrical measurement quality evaluation in smart grids. This approach entails determining relevant quality dimensions for smart grid data and providing novel membership functions designed to assess data quality according to certain desired attributes linked to each dimension. Using actual power and current data from the IEEE 14-bus power flow analysis, we perform assessments to verify the effectiveness of the suggested membership functions. Especially, our method is adaptable as it can assess the integrity of a single smart grid dataset including electrical readings of different power system parameters, including binary parameters like the state of the interrupter. It distinguishes itself from previous techniques by assessing measurement consistency. Additionally, our proposed method demonstrates sensitivity regarding the false data distribution in a given database.