A Modified S-Transform and Random Forests-Based Power Quality Assessment Framework

The proposed work aims at the accurate detection and classification of various single and multiple power quality (PQ) disturbances. To this end, a modified optimal fast discrete Stockwell transform (ST) with random forests (RF) classifier-based PQ detection framework has been proposed in this paper. In modified ST, a single signal-dependent window is introduced, with optimally selected window parameters via energy concentration maximization based constraint optimization. As a result of which accurate time-frequency localization of various PQ events is achieved, with sharper energy concentration. In classification stage, the proposed PQ framework utilizes the RF-based classifier, which follows the bagging approach by random selection of features and data points, at each node, to train the classifier. Decision stumps are used as weak classifiers, and using a simple majority voting of these decision stumps, RF builds a strong classifier. The RF gives less variance and less bias estimation due to injection of randomness into the training phase, and its performance is found to be reasonably immune to input parameter selection. As a result of this, the classification results of the proposed PQ framework are found to be very accurate and quite insensitive to the presence of noise in the data. Various test cases are presented in this paper to clearly demonstrate the superiority of the proposed scheme. The proposed approach has also been tested on real field data and very promising results have been obtained.