Power quality event detection and classification using wavelet-alienation based scheme - Rathore, Bhuvnesh; Raghav, Anil Kumar; Soni, Ravi (e-Prime, 2025)
•Focus on Power Quality (PQ) Issues: Comprehensive analysis of PQ disturbances, including single-stage and complex events, to safeguard grid and end-user equipment.•Novel Algorithm Development: Introduction of a Wavelet-Alienation-Neural algorithm for swift detection and classification of PQ events.•High-Speed Detection: Algorithm achieves event detection within a quarter cycle, utilizing alienation coefficients derived from wavelet approximations.•Versatility in Detection: Successfully tested on a wide spectrum of PQ events, including sags, swells, flickers, notches, harmonics, interruptions, transients, and complex disturbances.•Accurate Event Classification: Neural network-based classification using one-cycle approximate coefficients ensures precise identification of diverse PQ disturbances. In recent times, the issue of power quality has garnered significant attention from both utility providers and consumers, aiming to pinpoint the origins of disruptions to safeguard equipment. Power quality problems (PQ) encompass a range of abnormalities in voltage, current, or frequency, leading to equipment malfunction or failure. This research introduces a novel Wavelet-Alienation-algorithm designed for the detection and classification of Power Quality (PQ) events. The algorithm is engineered to swiftly detect PQ events within a quarter cycle time-frame, leveraging alienation coefficients derived from approximate wavelet coefficients. The variation of these alienation coefficients over time indicates PQ events, as their values surpass a predefined threshold, whereas they remain significantly lower in the case of pure sine waves. This methodology has been rigorously tested across various PQ events, including single-stage disturbances such as swells, sags, notches, harmonics, flickers, interruptions, and impulsive transients, as well as complex PQ disturbances formed by combinations of these simpler events. Encouragingly, the algorithm consistently demonstrates satisfactory performance in accurately detecting these events.