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This paper presents a novel approach to estimate modal parameters of power systems for monitoring and analyzing the embedded modes of small signal oscillations. The proposed approach applies continuous wavelet transform (CWT) to identify damping and frequency of critical modes based on its time-frequency localization capability. The CWT has modified Morlet function as its mother wavelet. A procedure is also presented to fine-tune settings of the modified Morlet function of the CWT based on Heisenberg's uncertainty principle and Shannon's entropy criterion. Additionally, high computational burden of the time-frequency methods is an important obstacle in online monitoring of power systems by these methods. To remedy this problem, the convolution integral of the CWT is calculated by efficient fast Fourier transform (FFT) routine in the proposed approach leading to a low computational burden. The proposed approach is compared with several other signal processing methods for modal identification of power systems. These comparisons illustrate effectiveness of the proposed approach, regarding run time, persistency against noise and estimation accuracy for online monitoring of small signal oscillations. Index Terms-Continuous wavelet transform (CWT), fastFourier transform (FFT), Heisenberg's uncertainty principle, modal identification, Shannon's entropy, small signal oscillations.

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