Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted Metrics

Hussain A. Alghamdi (), Midrar A. Adham, Umar Farooq and Robert B. Bass
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Hussain A. Alghamdi: Department of Electrical & Computer Engineering, Portland State University, Portland, OR 97201, USA
Midrar A. Adham: Department of Electrical & Computer Engineering, Portland State University, Portland, OR 97201, USA
Umar Farooq: National Grid ESO, Wokingham RG41 5BN, UK
Robert B. Bass: Department of Electrical & Computer Engineering, Portland State University, Portland, OR 97201, USA

Energies, 2025, vol. 18, issue 7, 1-26

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.

Keywords: phasor measurement unit; frequency event; frequency event detection; frequency response; Grey Wolf Optimization; Particle Swarm Optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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