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.

本研究旨在展示集成多位置高粒度同步相量测量和各种数据分析技术以执行扰动分析和增强电网态势感知的实际经验。它描述了箱线图、相关技术和聚类技术的使用。这些应用于从印度电网西部马哈拉施特拉邦电力传输有限公司的 400 kV 变电站获得的同步相量测量，以阐明电网在环境和干扰/事件条件下的行为。结果说明了相关技术如何作为系统运营商管理电网的重要决策工具。各种聚类技术的应用（如k-means聚类、层次聚类、和围绕中心点划分）到同步相量数据以确定形成的集群数量。应用内部验证技术来验证聚类算法的有效性。