This work considers the classification of power quality disturbances based on VMD (Variational Mode Decomposition) and EWT (Empirical Wavelet Transform) using SVM (Support Vector Machine). Performance comparison of VMD over EWT is done for producing feature vectors that can extract salient and unique nature of these disturbances. In this paper, these two adaptive signal processing methods are used to produce three Intrinsic Mode Function (IMF) components of power quality signals. Feature vectors produced by finding sines and cosines of statistical parameter vector of three different IMF candidates are used for training SVM. Validation for six different classes of power qualities including normal sinusoidal signal, sag, swell, harmonics, sag with harmonics, swell with harmonics is performed using synthetic data in MATLAB. Classification results using SVM shows that VMD outperforms over EWT for feature extraction process and the classification accuracy is tabled.

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We propose and demonstrate theoretically that Vertical External Cavity Surface Emitting Lasers (VECSELs) with external flat mirrors can be stabilized by applying a periodic spatio-temporal modulation of the pump current. Such pump modulation is shown to suppress the pattern-forming instabilities (modulation instabilities), which eventually results in stable beam emission. A modified Floquet linear stability analysis is used to characterize the dynamics of the modulated system and to evaluate its stabilization performance. Stability maps identify the regions in parameter space for complete and partial stabilization of VECSELs operating in different regimes depending on the external cavity length. In particular, the stabilization method is shown to operate most efficiently in Class-A laser limit (for relatively long VECSEL resonators) while it becomes ineffective in Class-B laser limit (for relatively short resonators). The stabilization effect is further confirmed through direct integration of the dynamical equations.