Model order estimation methods for low frequency oscillations in power systems

•Model order estimation methods are proposed for low frequency oscillations.•The methods detect the change in magnitude of eigenvalues of autocorrelation matrix.•A model order validation condition is proposed.•The proposed methodologies are tested on synthetic data and the field data.•The methods are compared with the existing techniques available for model order estimation. Estimation of low frequency oscillation modes is important for taking remedial actions to ensure the small signal stability of the power system. This paper proposes three approaches to estimate the model order of low frequency oscillations using ringdown data from Phasor Measurement Units. The first approach uses a sequential cumulative sum test on the eigenvalues of the autocorrelation matrix of the signal data to estimate the model order. The second approach estimates the model order based on the eigenvalue contribution in the trace of the autocorrelation matrix. The third algorithm uses sum of squares of the eigenvalues to estimate the model order. All the methods are compared to ascertain their efficacy in estimating the model. The algorithms are tested on oscillation data obtained for WSCC 9 bus system simulated on RTDS and the field data from a generating station in India.