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In this paper a technique for detection of multiple power quality (PQ) eventsis illustrated. An algorithm based on wavelet transform and Random Forest basedclassifier is proposed in this paper. The developed technique is implemented on11 different power quality events consisting of single stage power qualityevents such as sag, swell, flicker, interruption and multi stage power qualityevents such as harmonics combined with sag, swell, flicker, interruption. PQevents are simulated in MATLAB using standard IEEE-1159 standard. Significantfeatures of PQ events are extracted using wavelet transform and used to trainrandom forest based classifier. The efficiency of Random Forest Basedclassifier is compared with other widely used machine learning algorithms suchas K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). From confusionmatrix of different algorithms it is concluded that Random Forest showssuperior classification accuracy as compared to SVM and KNN.

Multiple Power Quality Event Detection and Classification using Wavelet Transform and Random Forest Classifier