Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

The proposed work accomplishes detection and classification of simple and complex power quality disturbances occurring in the realm of wind-grid integrated system using fast time-time transform and small residual-extreme learning machine. Taking the advantage of time-time transform, this work has further tailored time-time transform by accommodating the dyadic scaling to make it a computationally less complex and faster technique. Here, three-phase power quality signals are first segmented into single phases to be analyzed with fast time-time transform for getting the event information in time-time domain. The event information is further passed through two-stage feature extraction process to bring out twenty two characteristic curves; each of them is further used to compute five statistical features to produce 107 features after ignoring three redundant features. Thus, a high-dimensional feature set of size 321 is obtained corresponding to three phases of voltage signal. Because, high-dimensional feature set demands a robust classifier that can accurately classify the disturbances even in the presence of noise, small residual-extreme learning machine has been trained to classify the power quality signal into 12 classes (symmetrical sag, asymmetrical sag, swell, unbalance, harmonics, notch, momentary interruption, normal voltage signal, sag with harmonics, swell with harmonics, sag and notch with harmonics, and swell and notch with harmonics). This simulation-based study has been proved effective in dealing with noisy power quality signals also with reduced computational complexity and higher classification rate.