Sponsor
This research was supported by Portland General Electric
Published In
9th IEEE Conference on Technologies for Sustainability, 2022
Document Type
Post-Print
Publication Date
2022
Abstract
Power system balancing authorities are routinely affected by sudden frequency fluctuations. These frequency events can precipitate cascading outages and cause damage to both customer-owned and utility equipment. In this document, we describe an Algorithm Evaluation Environment that uses a suite of metrics to evaluate an algorithm and quantify its efficacy. Using the Algorithm Evaluation Environment, a detection algorithm can be tuned to best match the definition of a frequency event as defined by experts within the context of their own balancing area. We demonstrate the utility of the Algorithm Evaluation Environment using a regression-based frequency event detection algorithm. This algorithm can detect frequency events within a short period of time after the onset of an event. The algorithm has four parameters that can be adjusted, making it highly tunable and therefore suitable for demonstration of the Algorithm Evaluation Environment.
Rights
© 2022 IEEE
Persistent Identifier
https://archives.pdx.edu/ds/psu/37540
Citation Details
Keene, Sean; Hanks, Landon; and Bass, Robert B., "A Means for Tuning Primary Frequency Event Detection Algorithms" (2022). Electrical and Computer Engineering Faculty Publications and Presentations. 679.
https://archives.pdx.edu/ds/psu/37540
Description
This is the author’s version of a work that was accepted for publication in 9th IEEE Conference on Technologies for Sustainability, 2022. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in 9th IEEE Conference on Technologies for Sustainability, 2022.