A Low-Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S-Transform with Bat Algorithm
The complexity of the interconnected grid and the continuous increase of new energy sources have led to an acute problem with low-frequency oscillation (LFO) in power system. Identification and monitoring of LFO in power grid are prerequisites for effective control of low-frequency oscillation pheno...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/2088540 |
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| author | Miao Yu Jingjing Wei Shuoshuo Tian Jianqun Sun Yixiao Wu Shouzhi Zhang Jingxuan Hu |
| author_facet | Miao Yu Jingjing Wei Shuoshuo Tian Jianqun Sun Yixiao Wu Shouzhi Zhang Jingxuan Hu |
| author_sort | Miao Yu |
| collection | DOAJ |
| description | The complexity of the interconnected grid and the continuous increase of new energy sources have led to an acute problem with low-frequency oscillation (LFO) in power system. Identification and monitoring of LFO in power grid are prerequisites for effective control of low-frequency oscillation phenomena. To address matter that the traditional S-transform time-frequency window function has a fixed scale and cannot be applied to the specific local characteristics of different signals, an adaptive generalized S-transform algorithm based on a bat algorithm is proposed in this paper. It uses adjustment parameters to control the generalized Gaussian window function. The parameters are automatically adjusted by a bat algorithm adaptive optimization to find the best time-frequency characterization. Secondly, the PMU data waveform with implicit low-frequency oscillation information is converted into a two-dimensional time-frequency figure including the onset moment, frequency, and amplitude. The system enables identification and visual monitoring of low-frequency oscillations. After that, simulation experiments of New England system are conducted. The superiority of the proposed method is verified, which can greatly improve the time-frequency resolution of PMU active power data signal and has effective noise immunity. |
| format | Article |
| id | doaj-art-554339b4f58c4caa895dcbbedf63057a |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-554339b4f58c4caa895dcbbedf63057a2025-08-20T03:34:21ZengWileyJournal of Electrical and Computer Engineering2090-01552024-01-01202410.1155/2024/2088540A Low-Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S-Transform with Bat AlgorithmMiao Yu0Jingjing Wei1Shuoshuo Tian2Jianqun Sun3Yixiao Wu4Shouzhi Zhang5Jingxuan Hu6School of Mechanical-Electronic and Vehicle EngineeringSchool of Mechanical-Electronic and Vehicle EngineeringSchool of Electrical EngineeringSchool of Mechanical-Electronic and Vehicle EngineeringSchool of Mechanical-Electronic and Vehicle EngineeringSchool of Mechanical-Electronic and Vehicle EngineeringSchool of Mechanical-Electronic and Vehicle EngineeringThe complexity of the interconnected grid and the continuous increase of new energy sources have led to an acute problem with low-frequency oscillation (LFO) in power system. Identification and monitoring of LFO in power grid are prerequisites for effective control of low-frequency oscillation phenomena. To address matter that the traditional S-transform time-frequency window function has a fixed scale and cannot be applied to the specific local characteristics of different signals, an adaptive generalized S-transform algorithm based on a bat algorithm is proposed in this paper. It uses adjustment parameters to control the generalized Gaussian window function. The parameters are automatically adjusted by a bat algorithm adaptive optimization to find the best time-frequency characterization. Secondly, the PMU data waveform with implicit low-frequency oscillation information is converted into a two-dimensional time-frequency figure including the onset moment, frequency, and amplitude. The system enables identification and visual monitoring of low-frequency oscillations. After that, simulation experiments of New England system are conducted. The superiority of the proposed method is verified, which can greatly improve the time-frequency resolution of PMU active power data signal and has effective noise immunity.http://dx.doi.org/10.1155/2024/2088540 |
| spellingShingle | Miao Yu Jingjing Wei Shuoshuo Tian Jianqun Sun Yixiao Wu Shouzhi Zhang Jingxuan Hu A Low-Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S-Transform with Bat Algorithm Journal of Electrical and Computer Engineering |
| title | A Low-Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S-Transform with Bat Algorithm |
| title_full | A Low-Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S-Transform with Bat Algorithm |
| title_fullStr | A Low-Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S-Transform with Bat Algorithm |
| title_full_unstemmed | A Low-Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S-Transform with Bat Algorithm |
| title_short | A Low-Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S-Transform with Bat Algorithm |
| title_sort | low frequency oscillation identification method for power system based on adaptive generalized s transform with bat algorithm |
| url | http://dx.doi.org/10.1155/2024/2088540 |
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