@article {1292662,
	title = {Design and implementation of a data-driven approach to visualizing power quality},
	journal = {IEEE Transactions on Smart Grid},
	volume = {114},
	year = {2020},
	abstract = {Numerous underlying causes of power-quality (PQ) disturbances have enhanced the application of situational awareness to power systems. This application provides an optimal overall response for contingencies. With measurement data acquired by a multi-source PQ monitoring system, we propose an interactive visualization tool for PQ disturbance data based on a geographic information system (GIS). This tool demonstrates the spatio{\textendash}temporal distribution of the PQ disturbance events and the cross-correlation between PQ records and environmental factors, leveraging Getis statistics and random matrix theory. A methodology based on entity matching is also introduced to analyze the underlying causes of PQ disturbance events. Based on real-world data obtained from an actual power system, offline and online PQ data visualization scenarios are provided to verify the effectiveness and robustness of the proposed framework.},
	url = {https://ieeexplore.ieee.org/document/9057569},
	author = {Xiao, Fei and Tianguang Lu and Qian Ai and Wang, Xiaolong and Chen, Xinyu and Fang, Sidun and Qiuwei Wu}
}
