Online Monitoring of Low-Frequency Oscillation Based on the Improved Analytical Modal Decomposition Method
With increasing complexity of the power system, and the gradually expanding scales, the probability of low-frequency oscillation (LFO) is increasing, and its performance is more diverse. New oscillation types appear, such as close modes oscillation and intermittent modes oscillation. Due to the freq...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9272341/ |
| _version_ | 1811209870771224576 |
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| author | Yan Zhao Jinsong Zhang Qi Zhao |
| author_facet | Yan Zhao Jinsong Zhang Qi Zhao |
| author_sort | Yan Zhao |
| collection | DOAJ |
| description | With increasing complexity of the power system, and the gradually expanding scales, the probability of low-frequency oscillation (LFO) is increasing, and its performance is more diverse. New oscillation types appear, such as close modes oscillation and intermittent modes oscillation. Due to the frequency resolution limitation, it is difficult to identify model parameters of the new oscillation types accurately by the classical methods. Based on this, this article proposes an improved analytical modal decomposition (IAMD) method. Analytical modal decomposition (AMD) has the advantage in processing low-frequency signals, narrow-band signals, and intermittent signals. However, when we use AMD method to process the above signals, the cut-off frequency is a fixed bisection frequency. This frequency cannot adjust adaptively according to the signals. This article proposes to utilize chaotic particle swarm optimization (CPSO) algorithm and auxiliary signal to obtain the optimal cut-off frequency of AMD, quickly and accurately. The optimal decomposed signals are also obtained at the same time. Finally, the Hilbert Transform (HT) is used to identify the parameters of the LFO signals. Through the analysis of simulated and measured signals, it is verified that the IAMD method is suitable not only for online analysis and monitoring of typical LFO signals, but also for new oscillation types such as close modes oscillation and intermittent modes oscillation. |
| first_indexed | 2024-04-12T04:46:16Z |
| format | Article |
| id | doaj.art-eb00351e4e4141e38f2e55ca0914265f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T04:46:16Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eb00351e4e4141e38f2e55ca0914265f2022-12-22T03:47:30ZengIEEEIEEE Access2169-35362020-01-01821525621526610.1109/ACCESS.2020.30408489272341Online Monitoring of Low-Frequency Oscillation Based on the Improved Analytical Modal Decomposition MethodYan Zhao0Jinsong Zhang1https://orcid.org/0000-0001-6389-7976Qi Zhao2School of Power Transmission and Distribution Technology, Northeast Electric Power University, Jilin, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin, ChinaWith increasing complexity of the power system, and the gradually expanding scales, the probability of low-frequency oscillation (LFO) is increasing, and its performance is more diverse. New oscillation types appear, such as close modes oscillation and intermittent modes oscillation. Due to the frequency resolution limitation, it is difficult to identify model parameters of the new oscillation types accurately by the classical methods. Based on this, this article proposes an improved analytical modal decomposition (IAMD) method. Analytical modal decomposition (AMD) has the advantage in processing low-frequency signals, narrow-band signals, and intermittent signals. However, when we use AMD method to process the above signals, the cut-off frequency is a fixed bisection frequency. This frequency cannot adjust adaptively according to the signals. This article proposes to utilize chaotic particle swarm optimization (CPSO) algorithm and auxiliary signal to obtain the optimal cut-off frequency of AMD, quickly and accurately. The optimal decomposed signals are also obtained at the same time. Finally, the Hilbert Transform (HT) is used to identify the parameters of the LFO signals. Through the analysis of simulated and measured signals, it is verified that the IAMD method is suitable not only for online analysis and monitoring of typical LFO signals, but also for new oscillation types such as close modes oscillation and intermittent modes oscillation.https://ieeexplore.ieee.org/document/9272341/Improved analytical mode decomposition (IAMD)auxiliary signalchaotic particle swarm optimization (CPSO) algorithmlow-frequency oscillation (LFO) |
| spellingShingle | Yan Zhao Jinsong Zhang Qi Zhao Online Monitoring of Low-Frequency Oscillation Based on the Improved Analytical Modal Decomposition Method IEEE Access Improved analytical mode decomposition (IAMD) auxiliary signal chaotic particle swarm optimization (CPSO) algorithm low-frequency oscillation (LFO) |
| title | Online Monitoring of Low-Frequency Oscillation Based on the Improved Analytical Modal Decomposition Method |
| title_full | Online Monitoring of Low-Frequency Oscillation Based on the Improved Analytical Modal Decomposition Method |
| title_fullStr | Online Monitoring of Low-Frequency Oscillation Based on the Improved Analytical Modal Decomposition Method |
| title_full_unstemmed | Online Monitoring of Low-Frequency Oscillation Based on the Improved Analytical Modal Decomposition Method |
| title_short | Online Monitoring of Low-Frequency Oscillation Based on the Improved Analytical Modal Decomposition Method |
| title_sort | online monitoring of low frequency oscillation based on the improved analytical modal decomposition method |
| topic | Improved analytical mode decomposition (IAMD) auxiliary signal chaotic particle swarm optimization (CPSO) algorithm low-frequency oscillation (LFO) |
| url | https://ieeexplore.ieee.org/document/9272341/ |
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