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Faris, Anthony J.

Publications and source records attributed to Faris, Anthony J..

Detecting and Analyzing Power System Disturbances in PMU Data with the Open-Source Archive Walker Tool

As the number of Phasor Measurement Units (PMUs) deployed in power systems increases, so do the archives of synchrophasor measurements stored by utilities. Significant value can be extracted from these archives by using them to support studies such as frequency response analysis, small-signal stability analysis, and power plant model validation. To better enable utilities to perform these studies, the open-source Archive Walker tool was developed. This tool examines PMU data for grid disturbances and periods of interest. Once identified, the data can be further analyzed inside Archive Walker or exported to specialty tools. In this paper, the various disturbance detectors available in Archive Walker are described, and its interactions with other openly-available tools are highlighted.

Follum, James D.↗

Using Synchrophasors to Improve Bulk Power System Reliability in North America

Maintaining reliable operation of the bulk power system is a fundamental aspect of grid operation, and focuses on ensuring the system can withstand sudden disturbances or unanticipated failures of system elements such that instability, uncontrolled separation, or cascading failures will not occur. Phasor measurement units (PMUs) provide the capability for enhanced real-time monitoring and control room solutions as well as improved power system analysis in the planning horizon. With over 1,700 PMUs deployed in the North American grid, numerous PMU-based applications have been developed. This paper summarizes synchrophasor-based applications and presents utility synchrophasor experience.

PMU, oscillation monitoring, Power Plant Model Val↗

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↗