Evaluation of a Maximum Likelihood Estimator for the Identification of Power Systems Oscillation Modes

In this paper, a maximum likelihood (ML) estimator is applied for the estimation of power system oscillation modes. A regularization term is used in order to improve the estimation. An index is proposed to rank the modes and separate spurious from real modes. The ML estimator is compared with Prony method, and its advantages and limitations are discussed. Both methods are applied to synthetic systems and to real Phasor Measurement Unit (PMU) data acquired from the Brazilian Interconnected Power System (BIPS). The results show that the proposed maximum likelihood estimator is useful to complement and validate the results obtained by Prony analysis.