Multivariable signal processing algorithm for identification of power quality disturbances

This paper investigated a multi-variable power quality disturbance identification algorithm (MPQDIA) applying the Stockwell transform (ST), Hilbert transform (HT) and rule based decision tree (RBDT). Voltage waveform with associated power quality disturbances (PQDs) are realized by the mathematical formulation in compliance with standard IEEE-1159. These voltage signals with PQDs are processed applying the ST and HT for computing the index for power quality identification (IPI) and index for power quality location (IPL). IPI and IPL plots effectively identify and locate the PQDs of simple nature and multiple nature. Four features are extracted from data sets of IPI and IPL. These features are considered as input for the RBDT to classify the PQDs. MPQDIA is effectively tested for identification, to locate and to classify the total 21 PQDs of simple as well as multiple natures. Performance of MPQDIA is analysed in terms of rightly and wrongly categorized PQDs in noise free environment and considering noise of 20 dB SNR (signal to noise ratio) level. Efficiency of MPQDIA is compared with the ST and RBDT based algorithm, WT based fast Kurtogram and decision tree powered algorithm, and ST and RBDT powered method in terms of classification accuracy, noise level for which performance is not affected, effectiveness to detect the simple nature and multiple nature PQDs. It is established that MPQDIA effectively recognizes the PQDs on a real time power system network. Study is performed with the help of MATLAB software.