  In this paper a technique for detection of multiple power quality (PQ) events
is illustrated. An algorithm based on wavelet transform and Random Forest based
classifier is proposed in this paper. The developed technique is implemented on
11 different power quality events consisting of single stage power quality
events such as sag, swell, flicker, interruption and multi stage power quality
events such as harmonics combined with sag, swell, flicker, interruption. PQ
events are simulated in MATLAB using standard IEEE-1159 standard. Significant
features of PQ events are extracted using wavelet transform and used to train
random forest based classifier. The efficiency of Random Forest Based
classifier is compared with other widely used machine learning algorithms such
as K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). From confusion
matrix of different algorithms it is concluded that Random Forest shows
superior classification accuracy as compared to SVM and KNN.
