@inproceedings{c34e650881f14b4bbc94e56f441f2947,
title = "PMU analytics for power fault awareness and prediction",
abstract = "The analysis of electrical wave measurements on an electricity grid obtained through Phasor Measurement Units (PMUs) offers a unique opportunity to detect power system faults in real time. The goal of this study is to evaluate the use of classification and forecasting models to recognize and predict power system faults in streaming PMU data. The evaluation of these models built with simulated PMU data from a real-world power network demonstrates that both classification and forecasting can be successfully used in the detection and prediction of single line and three phase to ground faults. The results obtained indicate that the same methodology can be applied to other types of power system faults.",
keywords = "Current measurement, Electrical fault detection, Phasor measurement units, Power system fault, Voltage measurement",
author = "Wanik, {Mohd Zamri Che} and Antonio Sanfilippo and Nand Singh and Abdullah Jabbar and Zhaohui Cen",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Smart Grid Synchronized Measurements and Analytics, SGSMA 2019 ; Conference date: 20-05-2019 Through 23-05-2019",
year = "2019",
month = may,
doi = "10.1109/SGSMA.2019.8784461",
language = "English",
series = "2019 International Conference on Smart Grid Synchronized Measurements and Analytics, SGSMA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 International Conference on Smart Grid Synchronized Measurements and Analytics, SGSMA 2019",
address = "United States",
}