Hybrid Methods for Fast Detection and Characterization of Power Quality Disturbances

In this paper, recently developed variants of wavelet transform, namely the maximum overlapping discrete wavelet transform and the second-generation wavelet transform, are used for detection of ten types of the power quality (PQ) disturbance signals. Further, the features of PQ signal disturbances are extracted using these wavelet transforms. Those extracted features are then used to classify various PQ disturbances. Random forest (RF) classifier is presented in this paper. The RF is constructed with multiple trees for classification of large number of classes simultaneously. In order to represent realistic situation, the proposed technique is tested with noisy data.

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