Data-driven Analysis for Low Frequency Oscillation Identification in Smart Grid using FB-DMD and T-DMD Methods

The major challenges faced by the modern interconnected grids are the disturbances caused due to the presence of the low frequency oscillations (LFO). These inter-area modes can cause a huge disturbances and may also lead to the failure of the entire electric grid system. Hence, monitoring of these low frequency oscillations and mitigating its effects are very much important to maintain stability of the power system. To employ a better control over the grid and to bring up the stability of the system, the damped modes of the power signal generated must be known even under a noisy condition. Based on the past literature, dynamic mode decomposition (DMD) method has proven to be the best to identify the damped modes of a signal. However, it fails to give satisfying results in the presence of noises. Hence, two variants of DMD namely forward-backward DMD (FB-DMD) and total-DMD (T-DMD) are adapted in this paper to find the dynamic modes of the system which helps to identify the low frequency modes even under a noisy condition. Based on the experimental results and analysis, T-DMD is found to be suitable to predict the low frequency modes with higher accuracy and lower complexity.