Published In
Energies
Document Type
Article
Publication Date
4-11-2025
Subjects
Phasor measurement unit, Frequency event detection, Frequency response, Grey Wolf Optimization, Particle Swarm Optimization
Abstract
This research presents a novel technique that refines the performance of a frequency event detection algorithm with four adjustable parameters based on signal processing and statistical methods. The algorithm parameters were optimized using two well-established optimization techniques: Grey Wolf Optimization and Particle Swarm Optimization. Unlike conventional approaches that apply equally weighted metrics within the objective function, this work implements variable weighted metrics that prioritize specificity, thereby strengthening detection accuracy by minimizing false-positive events. Realistic small- and large-scale frequency datasets were processed and analyzed, incorporating various events, quasi-events, and non-events obtained from a phasor measurement unit in the Western Interconnection. An analytical comparison with an algorithm that uses equally weighted metrics was performed to assess the proposed method’s effectiveness. The results demonstrate that the application of variable weighted metrics enables the detection algorithm to identify frequency non-events, thereby significantly reducing false positives reliably.
Rights
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Locate the Document
DOI
10.3390/en18071659
Persistent Identifier
https://archives.pdx.edu/ds/psu/43258
Publisher
MDPI AG
Citation Details
Alghamdi, H. A., Adham, M. A., Farooq, U., & Bass, R. B. (2025). Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted Metrics. Energies, 18(7), 1659.
