Data Driven Variational Mode Decomposition Method for Microgrid Frequency Monitoring - Aarthi, N.; Sindhu Thampatty, K. C.; Nambiar, T. N. P. (International Review on Modeling and Simulations, 2025)
This paper presents a Variational Mode Decomposition (VMD) technique for detecting frequency deviations from the Phasor Measurement Unit signal of a microgrid with a solar PV system. The intermittent nature and large penetration of renewable energy sources create frequency deviations in microgrids. This causes low-frequency oscillations and may impede grid stability. Conventional signal processing methods are inefficient for detecting relevant information from ambient signals amidst noisy measurements. In this study, the performance of VMD is analyzed for raw data to detect deviations in frequency. The simulation results show that the intrinsic modes of the VMD correspond to frequency variations in the microgrid. The instantaneous damping ratio of the modes was established using the Teager Kaiser energy operator. A lower damping ratio was obtained with high solar penetration. This is due to the solar PV system in the microgrid, which reduces the damping of frequency oscillations. The efficacy of the proposed method was compared with a hardware experiment in a microgrid with a solar PV system and found to be effective. Real-time data were used to validate this technique. The findings of the simulation demonstrate that it is possible to determine the dominant low-frequency oscillatory mode with good noise tolerance in an efficient and less complex manner.