| Title | Low Frequency Oscillation Mode Identification Algorithm Based on VMD Noise Reduction and Stochastic Subspace Method |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Zhang, Yanjun, Zhao, Peng, Han, Ziyang, Yang, Luyu, Chen, Junrui |
| Conference Name | 2022 Power System and Green Energy Conference (PSGEC) |
| Keywords | low-frequency oscillation, Noise measurement, noise reduction, Power measurement, power system stability, pubcrawl, Resiliency, Scalability, simulation, Stability analysis, Stochastic Computing Security, stochastic subspace identification algorithm, variational mode decomposition, White noise |
| Abstract | Low-frequency oscillation (LFO) is a security and stability issue that the power system focuses on, measurement data play an important role in online monitoring and analysis of low-frequency oscillation parameters. Aiming at the problem that the measurement data containing noise affects the accuracy of modal parameter identification, a VMD-SSI modal identification algorithm is proposed, which uses the variational modal decomposition algorithm (VMD) for noise reduction combined with the stochastic subspace algorithm for identification. The VMD algorithm decomposes and reconstructs the initial signal with certain noise, and filters out the noise signal. Then, the optimized signal is input into stochastic subspace identification algorithm(SSI), the modal parameters is obtained. Simulation of a three-machine ninenode system verifies that the VMD-SSI mode identification algorithm has good anti-noise performance. |
| DOI | 10.1109/PSGEC54663.2022.9881194 |
| Citation Key | zhang_low_2022 |
Low Frequency Oscillation Mode Identification Algorithm Based on VMD Noise Reduction and Stochastic Subspace Method