Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted Metrics
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 Opt...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/7/1659 |
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| author | Hussain A. Alghamdi Midrar A. Adham Umar Farooq Robert B. Bass |
| author_facet | Hussain A. Alghamdi Midrar A. Adham Umar Farooq Robert B. Bass |
| author_sort | Hussain A. Alghamdi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e03a903703f049c7b522ec8a1e536da5 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-e03a903703f049c7b522ec8a1e536da52025-08-20T03:06:31ZengMDPI AGEnergies1996-10732025-03-01187165910.3390/en18071659Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted MetricsHussain A. Alghamdi0Midrar A. Adham1Umar Farooq2Robert B. Bass3Department of Electrical & Computer Engineering, Portland State University, Portland, OR 97201, USADepartment of Electrical & Computer Engineering, Portland State University, Portland, OR 97201, USANational Grid ESO, Wokingham RG41 5BN, UKDepartment of Electrical & Computer Engineering, Portland State University, Portland, OR 97201, USAThis 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.https://www.mdpi.com/1996-1073/18/7/1659phasor measurement unitfrequency eventfrequency event detectionfrequency responseGrey Wolf OptimizationParticle Swarm Optimization |
| spellingShingle | Hussain A. Alghamdi Midrar A. Adham Umar Farooq Robert B. Bass Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted Metrics Energies phasor measurement unit frequency event frequency event detection frequency response Grey Wolf Optimization Particle Swarm Optimization |
| title | Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted Metrics |
| title_full | Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted Metrics |
| title_fullStr | Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted Metrics |
| title_full_unstemmed | Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted Metrics |
| title_short | Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted Metrics |
| title_sort | enhancing frequency event detection in power systems using two optimization methods with variable weighted metrics |
| topic | phasor measurement unit frequency event frequency event detection frequency response Grey Wolf Optimization Particle Swarm Optimization |
| url | https://www.mdpi.com/1996-1073/18/7/1659 |
| work_keys_str_mv | AT hussainaalghamdi enhancingfrequencyeventdetectioninpowersystemsusingtwooptimizationmethodswithvariableweightedmetrics AT midraraadham enhancingfrequencyeventdetectioninpowersystemsusingtwooptimizationmethodswithvariableweightedmetrics AT umarfarooq enhancingfrequencyeventdetectioninpowersystemsusingtwooptimizationmethodswithvariableweightedmetrics AT robertbbass enhancingfrequencyeventdetectioninpowersystemsusingtwooptimizationmethodswithvariableweightedmetrics |