A Data-driven Approach for Estimating Power System Frequency and Amplitude Using Dynamic Mode Decomposition

To ensure power system stability, control and quality supply of power, it is essential to monitor power system parameters such as frequency and amplitude. This paper proposes a data-driven approach based on dynamic mode decomposition (DMD) algorithm for the accurate estimation of frequency and amplitude in smart grid. In the proposed approach, to extract the multiple frequency components, including harmonics, inter-harmonics and subharmonics, a stacked measurement matrix is created by appending multiple time-shifted versions of power signals. An optimal hard-thresholding is performed on the singular values of the measurement matrix to deal with the uncertainties and high-level noises. Further, the frequency and amplitude are computed based on the extracted dynamic modes. The performance of the proposed approach is confirmed through various experiments conducted on different power system scenarios under noisy and noiseless conditions. The effectiveness of the DMD based method is verified by comparing the results with several state-of-the-art methods. The promising results suggest that the proposed approach can be used as an efficient candidate for estimating the power system frequency and amplitude.