A Hybrid Signal Processing Technique for Recognition of Complex Power Quality Disturbances

Abstract

This paper proposes an algorithm based on the Stockwell transform (ST) and Hilbert transform (HT) to classify complex power quality disturbances (CPQDs). Two indices are used for detection and locating the CPQDs. The first index is the hybrid power quality index (HPQI), which is used to classify CPQDs. The Stockwell maximum values factor (SMVF), Stockwell summation values factor (SSVF), and Stockwell variance values factor (SVVF) are computed by utilizing the ST to process a voltage signal having a superimposed PQ disturbance. To compute the Hilbert index (HI), the voltage signal is also processed using the HT. The HPQI index is calculated by element-to-element multiplication of the SMVF, SSVF, SVVF, HI, and a weight factor (WF). The second index is the hybrid power quality disturbance time localization index (HPLI), which is used to locate the complex PQ events in a given time frame. For classification of investigated CPQDs, seven different features extracted from various indices defined using ST and HT are taken as input to the rule-based decision tree (RBDT). RBDT classifies the disturbances using simple decision rules, which has the merit of low computational time. The algorithm is able to correctly detect CPQDs with a 98% accuracy rate. The proposed algorithm outperforms the literature's algorithm, which is based on HT and RBDT. Furthermore, it is demonstrated that the algorithm effectively recognizes CPQDs that occur in real-time on a practical distribution network in Rajasthan State, India.