Synergism of synchrophasor measurements and data analytics for enhancing situational awareness of power grid. (July 2021)

• Record Type: Journal Article
• Authors: Kulkarni, Amit R.; Ballal, Makarand S.
• Is Part Of: Computers & electrical engineering. Volume 93(2021)
• Publication Date: 2021-07
• Subjects: Box plot -- Connectivity index -- Correlation technique -- Dunn index -- Hierarchical clustering -- k-means clustering, Partitioning around medoids -- Silhouette width -- Smartgrid -- Wide area measurement system; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107231
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: This study aims to demonstrate the real-world experience of integrating multi-location high-granularity synchrophasor measurements and various data analysis techniques to perform disturbance analysis and enhance the situational awareness of a power grid. It describes the utilisation of box plots, correlation techniques, and clustering techniques. These are applied to synchrophasor measurements obtained from 400 kV substations of the Maharashtra State Electricity Transmission Company Limited in the western part of the Indian grid to elucidate the behaviour of the grid under ambient and disturbance/event conditions. The results illustrate how correlation technique can serve as important decision-making tool for system operators in managing the grid. The application of various clustering techniques (such as k-means clustering, hierarchical clustering, and partitioning around medoids) to synchrophasor data to determine the number of clusters formed is also discussed. Internal validation techniques are applied to verify the effectiveness of the clustering algorithms. Graphical abstract: Image, graphical abstract