Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMR
Low frequency oscillations (LFOs) in power systems usually fall into two types, i.e., forced oscillations and natural oscillations. Waveforms of the two are similar, but the suppression methods are different. Therefore, it is important to accurately identify LFO type. In this paper, a method for dis...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/14/2762 |
| _version_ | 1798026785586151424 |
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| author | Shuang Feng Jianing Chen Yi Tang |
| author_facet | Shuang Feng Jianing Chen Yi Tang |
| author_sort | Shuang Feng |
| collection | DOAJ |
| description | Low frequency oscillations (LFOs) in power systems usually fall into two types, i.e., forced oscillations and natural oscillations. Waveforms of the two are similar, but the suppression methods are different. Therefore, it is important to accurately identify LFO type. In this paper, a method for discriminating LFO type based on multi-dimensional features and a feature selection algorithm combining ReliefF and minimum redundancy maximum relevance algorithm (mRMR) is proposed. Firstly, 53 features are constructed from six aspects—time domain, frequency domain, energy, correlation, complexity, and modal analysis—which comprehensively characterize the multidimensional features of LFO. Then, the optimal feature subset with greater relevance and less redundancy is extracted by ReliefF-mRMR. In order to improve the classification performance, a modified Support Vector Machine (SVM) with Genetic Algorithm (GA) optimizing the key parameters is adopted, which is conducted in MATLAB. Finally, in 179-bus system, the samples of LFOs are generated by the Power System Analysis Toolbox (PSAT) and the accuracy of the LFO type identification model is verified. In ISO New England and East China power grid, it is proven that the proposed method can accurately identify LFO type considering the influences of noise, oscillation mode, and data incompletion. Hence, it has good robustness, noise immunity, and practicability. |
| first_indexed | 2024-04-11T18:40:56Z |
| format | Article |
| id | doaj.art-9f427a77429043df965b3e9ae695f8a8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:40:56Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-9f427a77429043df965b3e9ae695f8a82022-12-22T04:08:58ZengMDPI AGEnergies1996-10732019-07-011214276210.3390/en12142762en12142762Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMRShuang Feng0Jianing Chen1Yi Tang2School of Electrical Engineering, Southeast University, Nanjing 210096, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210096, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210096, ChinaLow frequency oscillations (LFOs) in power systems usually fall into two types, i.e., forced oscillations and natural oscillations. Waveforms of the two are similar, but the suppression methods are different. Therefore, it is important to accurately identify LFO type. In this paper, a method for discriminating LFO type based on multi-dimensional features and a feature selection algorithm combining ReliefF and minimum redundancy maximum relevance algorithm (mRMR) is proposed. Firstly, 53 features are constructed from six aspects—time domain, frequency domain, energy, correlation, complexity, and modal analysis—which comprehensively characterize the multidimensional features of LFO. Then, the optimal feature subset with greater relevance and less redundancy is extracted by ReliefF-mRMR. In order to improve the classification performance, a modified Support Vector Machine (SVM) with Genetic Algorithm (GA) optimizing the key parameters is adopted, which is conducted in MATLAB. Finally, in 179-bus system, the samples of LFOs are generated by the Power System Analysis Toolbox (PSAT) and the accuracy of the LFO type identification model is verified. In ISO New England and East China power grid, it is proven that the proposed method can accurately identify LFO type considering the influences of noise, oscillation mode, and data incompletion. Hence, it has good robustness, noise immunity, and practicability.https://www.mdpi.com/1996-1073/12/14/2762low frequency oscillationsmultidimensional featurestype identificationReliefFmRMRGA-SVM |
| spellingShingle | Shuang Feng Jianing Chen Yi Tang Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMR Energies low frequency oscillations multidimensional features type identification ReliefF mRMR GA-SVM |
| title | Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMR |
| title_full | Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMR |
| title_fullStr | Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMR |
| title_full_unstemmed | Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMR |
| title_short | Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMR |
| title_sort | identification of low frequency oscillations based on multidimensional features and relieff mrmr |
| topic | low frequency oscillations multidimensional features type identification ReliefF mRMR GA-SVM |
| url | https://www.mdpi.com/1996-1073/12/14/2762 |
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