Estimation of mode shape in power systems under ambient conditions using advanced signal processing approach

This paper presents a dynamic approach for the monitoring and estimation of electromechanical oscillatory modes in the power system in real time with less computational burden. Extensive implementation of phasor measurement units (PMU) and the utilization of advanced signal processing techniques help in identifying the dynamic behaviors of oscillatory modes. Conventional nonstationary analysis techniques are computationally weak to handle a larger quantity of data in real-time. This research utilizes the variational mode decomposition (VMD) for signal decomposition, which is highly tolerant to noise and computationally more robust. The predefined parameters of the VMD process are assigned using FFT analysis of the signal. The significant decomposed mode resembling the original signal is determined using the correlation coefficient method and used for low-frequency mode estimation. The spectral analysis techniques are used to determine the instantaneous mode shapes, which help to identify the source of oscillation in the power system network. The proposed methodology has been tested using signals obtained from two area Kundur system and actual PMU data recorded from Power System Operation Corporation (POSOCO) Limited of the Indian Power grid. The results confirm the superior viability and adaptability of the proposed approach. The performance comparison with other existing signal processing techniques used to estimate low-frequency modes is also presented to illustrate the effectiveness of the proposed method.

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