IDENTIFICATION OF SUB-SYNCHRONOUS OSCILLATION PARAMETERS OF POWER SYSTEM BASED ON TTAO-VMD AND CNN-SE-ATTENTION-ITCN

Ma Yanfeng; Wang Shuyan; Wang Zijian; Zhao Shuqiang; Xu Weikuo; Han Shanshan

doi:10.19912/j.0254-0096.tynxb.2025-0408

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 726-735. DOI: 10.19912/j.0254-0096.tynxb.2025-0408

IDENTIFICATION OF SUB-SYNCHRONOUS OSCILLATION PARAMETERS OF POWER SYSTEM BASED ON TTAO-VMD AND CNN-SE-ATTENTION-ITCN

Ma Yanfeng, Wang Shuyan, Wang Zijian, Zhao Shuqiang, Xu Weikuo, Han Shanshan

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Abstract

Aiming at the problems of poor generalization, and insufficient temporal feature extraction capability existing in the identification of noisy sub-synchronous oscillations （SSO） by traditional neural networks, a CNN-SE-Attention-ITCN hybrid identification model combining convolutional neural network（CNN）with self-attention mechanism and improved temporal convolutional network（ITCN）is proposed. Firstly, the mode number K and penalty factor α of variational mode decomposition（VMD）were optimized using TTAO algorithm, and several modes of SSO were decomposed. Then, effective modal components were selected according to Pearson correlation coefficient（PCC）, and time domain features were extracted. Finally, the trained CNN-SE-Attention-ITCN model was used to identify SSO parameters. The model is proved to have good identification accuracy and robustness through the testing of noisy ideal signal, simulated signal and real signal.

Key words

power system / sub-synchronous oscillation / variational mode decomposition / modal identification / CNN-SE-Attention-ITCN / triangulation topology aggregation optimizer

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Ma Yanfeng, Wang Shuyan, Wang Zijian, Zhao Shuqiang, Xu Weikuo, Han Shanshan. IDENTIFICATION OF SUB-SYNCHRONOUS OSCILLATION PARAMETERS OF POWER SYSTEM BASED ON TTAO-VMD AND CNN-SE-ATTENTION-ITCN[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 726-735 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0408

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