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Subsynchronous oscillation detection in wind power system using multivariate empirical mode decomposition
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Peng Yu1, Guoxian Guo2, Xiaoming Yang1, Yingming Liu2
Renewable Energy Resources | 2024, 42(6) : 781 - 788
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Renewable Energy Resources | 2024, 42(6): 781-788
Subsynchronous oscillation detection in wind power system using multivariate empirical mode decomposition
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Peng Yu1, Guoxian Guo2, Xiaoming Yang1, Yingming Liu2
Affiliations
  • 1 Power Dispatching and Control Center State Grid Liaoning Electric Power Co., Ltd. Shenyang 110006 China
  • 2 School of Electrical Engineering Shenyang University of Technology Shenyang 110870 China
Published: 2024-06-20
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With the largescale integration of wind power, power system subsynchronous oscillation (SSO)events occur frequently, which seriously threatens the safe and stable operation of the power grid. The accurate detection of SSO in wind power gridconnected system is of great significance to ensure the stable operation of the power systems. Most of the existing SSO detection methods are singlechannel methods, which are difficult to take into account the global SSO characteristics of the systems. Therefore, this paper proposes a SSO detection method for wind power gridconnected system based on multivariate empirical mode decomposition (MEMD). Firstly, the multivariate empirical mode decomposition is performed on the measurements of wind power gridconnected points, and then the IMF components with SSO mode are screened out via TeagerKaiser energy operator (TKEO). Then, the HilbertHuang transform (HHT) is used to identify the SSO frequency and damping ratio. Finally, the proposed detection method is analyzed by the improved 4machine 2area system simulation data, and the results verify the effectiveness of the proposed method.

subsynchronous oscillation detection  /  measurements  /  multivariate empirical mode decomposition  /  Teager-Kaiser energy operator  /  Hilbert-Huang transform
Peng Yu, Guoxian Guo, Xiaoming Yang, Yingming Liu. Subsynchronous oscillation detection in wind power system using multivariate empirical mode decomposition[J]. Renewable Energy Resources, 2024 , 42 (6) : 781 -788 .
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Article Info
  • Receive Date：2024-01-08
  • Online Date：2025-07-22
  • Published：2024-06-20
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  • Received：2024-01-08
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Affiliations
    1 Power Dispatching and Control Center State Grid Liaoning Electric Power Co., Ltd. Shenyang 110006 China
    2 School of Electrical Engineering Shenyang University of Technology Shenyang 110870 China
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红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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