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: | |
| Otros Autores: | , , |
| Publicado: |
MDPI AG
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3188826258 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1996-1073 | ||
| 024 | 7 | |a 10.3390/en18071659 |2 doi | |
| 035 | |a 3188826258 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231459 |2 nlm | ||
| 100 | 1 | |a Alghamdi, Hussain A |u Department of Electrical & Computer Engineering, Portland State University, Portland, OR 97201, USA; <email>midrar@pdx.edu</email> (M.A.A.); <email>robert.bass@pdx.edu</email> (R.B.B.) | |
| 245 | 1 | |a Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted Metrics | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Machine learning | ||
| 653 | |a Accuracy | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Hypothesis testing | ||
| 653 | |a Metastasis | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Statistical methods | ||
| 653 | |a Signal processing | ||
| 653 | |a Support vector machines | ||
| 653 | |a Statistical analysis | ||
| 700 | 1 | |a Adham, Midrar A |u Department of Electrical & Computer Engineering, Portland State University, Portland, OR 97201, USA; <email>midrar@pdx.edu</email> (M.A.A.); <email>robert.bass@pdx.edu</email> (R.B.B.) | |
| 700 | 1 | |a Umar Farooq |u National Grid ESO, Wokingham RG41 5BN, UK; <email>ixumer@gmail.com</email> | |
| 700 | 1 | |a Bass, Robert B |u Department of Electrical & Computer Engineering, Portland State University, Portland, OR 97201, USA; <email>midrar@pdx.edu</email> (M.A.A.); <email>robert.bass@pdx.edu</email> (R.B.B.) | |
| 773 | 0 | |t Energies |g vol. 18, no. 7 (2025), p. 1659 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3188826258/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3188826258/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3188826258/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |