Hybrid signal processing technique to design smart meter for detection of voltage amplitude disturbances

The increasing trend of deploying power electronic-based devices and the large-scale integration of renewable energy (RE) plants into the networks of distribution utility grids is causing problems of voltage-related power quality (PQ) disturbances. This has forced researchers to design smart PQ monitoring meters for the identification of voltage-related disturbances such as sags, swells, and momentary interruptions (MI). This research is focused on designing a hybrid signal processing approach for a smart PQ monitoring meter to detect the voltage sags, swells, and MIs. A power quality detection index (PQI) and hybrid power quality detection and location index (HPQDLI) are designed by the hybrid use of Gabor transform (GT), Stockwell transform (ST), and Hilbert transform for processing voltage signals with a PQ disturbance. PQI and HPQDLI are used to detect and locate the PQ disturbances. Features computed from PQI and HPQDLI are used for categorization of the disturbances. An algorithm is tested on simulated disturbances using mathematical formulations. The algorithm performance is also tested in the noisy environment. It is proved that the proposed hybrid algorithm is more efficient compared with the discrete wavelet transform (DWT) technique. The study is performed using MATLAB.