Ambient Data-Driven Online Tracking of Electromechanical Modes Using Recursive Subspace Dynamic Mode Decomposition
Shuyu Zhou, Deyou Yang, Guowei Cai, Lixin Wang, Zhe Chen, Jin Ma, Bo Wang
Abstract
The extraction of electromechanical modal parameters from ambient data is a useful and practical method that can monitor the modal properties of power system oscillations online. This paper proposes a recursive subspace dynamic mode decomposition (RSub-DMD) algorithm for online monitoring power system modes using wide-area synchronized ambient data. By introducing Givens rotation to the recursion process, the computational capability of subspace dynamic mode decomposition has been significantly improved without compromising accuracy. The shorter sliding data window enables the proposed RSub-DMD to quickly track the electromechanical modal parameters and participation factors (PFs), with low estimation variance. IEEE 16 generator 68 bus test system and measurement data from real system are used to verify the performance of the proposed RSub-DMD algorithm. The tracking results in different operating environments verify the effectiveness of the proposed algorithm.