Imagen de Portada

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

Guardado en:
Publicado en:Energies vol. 18, no. 7 (2025), p. 1659
Autor principal: Alghamdi, Hussain A
Otros Autores: Adham, Midrar A, Umar Farooq, Bass, Robert B
Publicado:
MDPI AG
Materias:
Machine learning
Accuracy
Wavelet transforms
Hypothesis testing
Metastasis
Fourier transforms
Optimization techniques
Genetic algorithms
Statistical methods
Signal processing
Support vector machines
Statistical analysis
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen: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.
ISSN:1996-1073
DOI:10.3390/en18071659
Fuente:Publicly Available Content Database