Machine Learning for Synchrophasor Analysis

Ren, Huiying; Hou, Zhangshuan; Wang, Heng; Etingov, Pavel V.

doi:10.2172/1673617

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DOE OSTI · 1673617

Machine Learning for Synchrophasor Analysis

Ren, Huiying·Hou, Zhangshuan·Wang, Heng·Etingov, Pavel V.

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Abstract

The report presents results from the development of a cloud-based, Big Data analysis framework for power systems. The computational pipeline uses the Apache Spark framework running in an OpenStack cloud infrastructure. A real-world phasor measurement unit (PMU) dataset has been used to carry out the analysis. Several Machine Learning (ML) methods have been developed and implemented for event and anomaly detection and classification. Actual examples of power system events detection and analysis using synchrophasor data are presented. It has been shown that applications of the cloud-based computing environment and the Apache Spark framework enable a significant increase in the computational efficiency of large-scale PMU data analysis.

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Ren, Huiying, Hou, Zhangshuan, Wang, Heng, Etingov, Pavel V.. 2020-09-01. Machine Learning for Synchrophasor Analysis. https://doi.org/10.2172/1673617

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