Sparsity Promoting Dynamic Mode Decomposition Based Dominant Modes and Mode Shapes Estimation in Bulk Power Grid
Li Xue1, Yu Yang1, Jiang Tao1, Li Guoqing1, Liu Chunxiao2
1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China; 2. Power Dispatching and Control Center China Southern Power Grid Guangzhou 510623 China
Abstract This paper proposes a sparsity promoting dynamic mode decomposition (SPDMD) approach for dominant modes and mode shapes assessment in bulk power grid by using the wide area measurement. The SPDMD is first employed to estimate the low-order state matrix containing the critical dynamic oscillation features from the multichannel wide area measurements. Then, the alternating direction multiplier method (ADMM) and Lagrangian multiplier (LM) are used to estimate the optimized amplitude coefficients of the oscillation modes embedded in the low-order state matrix. Further, using the optimized amplitude coefficients, the dominant modes and mode shapes are separated. Finally, the proposed approach was evaluated by the 16-machine 68-bus test system as well as China Southern Power Grid, the results confirm the accuracy and effectively of the proposed SPDMD in dominant modes and mode shapes.
Jiang Tao,Li Xue,Li Guoqing等. Sparsity Promoting Dynamic Mode Decomposition Based Dominant Modes and Mode Shapes Estimation in Bulk Power Grid[J]. Transactions of China Electrotechnical Society, 2021, 36(13): 2832-2843.
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2021, Vol. 36 
