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Results for “PMU, Machine Learning, Big Data, SPARK, Event Detection”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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2 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Cloud Based Analytical Framework for Synchrophasor Data Analysis

The paper presents initial results from the development of a cloud-based, Big Data analysis platform 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. Actual examples of power system events detection 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.

PMU, Machine Learning, Big Data, SPARK, Event Dete↗
DOE OSTI2020

Machine Learning for Synchrophasor Analysis

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.

20 FOSSIL-FUELED POWER PLANTS↗