Application of Prony Algorithm in Power System Low-Frequency Oscillation Analysis

In power systems, the Prony algorithm serves as a critical method for low-frequency oscillation analysis and identification. This algorithm performs signal data fitting to calculate oscillation frequency and damping characteristics based on given phase information. The implementation typically involves constructing a linear prediction model using sampled data points, where the algorithm solves for complex exponentials that best match the observed oscillations. Key computational steps include: forming a Hankel matrix from measurement data, performing singular value decomposition (SVD) for model order selection, and solving the characteristic equation to extract frequency and damping parameters. This method enables detection of potential system issues and provides essential solutions for stability enhancement. Furthermore, the Prony algorithm facilitates comprehensive stability analysis and evaluation of power systems through modal identification. For power system engineers, proficiency in implementing Prony algorithm with proper preprocessing (such as noise filtering and data windowing) constitutes a vital skill for oscillation monitoring and control strategy development.