Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

Abstract

A multiple power quality (MPQ) disturbance has two or more power quality (PQ) disturbances superimposed on a voltage signal. A compact and robust technique is required to identify and classify the MPQ disturbances. This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality (MPQ) disturbance using stockwell transform (ST) and hilbert transform (HT). This will reduce the computational time to identify the MPQ disturbances, which makes the algorithm fast. A MPQ identification index (IPI) is computed using statistical features extracted from the voltage signal using the ST and HT. IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances. A MPQ time location index (IPL) is computed using the features extracted from the voltage signal using ST and HT. IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time. Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio (SNR). The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree (RBDT) is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances. MATLAB software is used to perform the study.