Synthetical Modal Parameters Identification Method of Damped Oscillation Signals in Power System

It is vital to improve the stability of the power system by accurately identifying the modal parameters of damped low-frequency oscillations (DLFO) and controlling the oscillation in time. A new method based on empirical mode decomposition (EMD), stochastic subspace identification (SSI), and Prony a...

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Bibliographic Details
Published in:Applied sciences 2022-05, Vol.12 (9), p.4668
Main Authors: Li, Huan, Bu, Siqi, Wen, Jiong-Ran, Fei, Cheng-Wei
Format: Article
Language:English
Subjects:
Aeronautics
Algorithms
Damping ratio
Electricity distribution
Identification methods
low-frequency oscillations
Methods
modal identification
Noise
Oscillations
Parameter identification
parameter matching
Prony
Resonant frequencies
stochastic subspace identification
Time signals
Troubleshooting
Wavelet transforms
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Summary:It is vital to improve the stability of the power system by accurately identifying the modal parameters of damped low-frequency oscillations (DLFO) and controlling the oscillation in time. A new method based on empirical mode decomposition (EMD), stochastic subspace identification (SSI), and Prony algorithms, called synthetical modal parameters identification (SMPI) method, is developed by efficiently matching the modal parameters of DLFO which are acquired from the SSI and Prony algorithm. In this approach, EMD is used for denoising the raw oscillation signals thereby enhancing the noise resistance, and then using the SSI and Prony algorithms to identify the precise modal parameters assisted by parameter matching. It is demonstrated that the proposed SMPI method holds great accuracy in identifying full modal parameters including natural frequencies, damping ratios, amplitudes, and phase angles with simulated signals with known modal parameters and real-time signals from some power system case studies. The strategy of SMPI has effectively overcome the weakness of a single approach, and the identification results are promising to heighten the stabilization of power systems. Besides, SMPI shows the potential to troubleshoot in different fields, such as construction, aeronautics, and marine, for its satisfactory robustness and generalization ability.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12094668