This paper proposes a new generalized two dimensional learning approach for particle swarm based feature selection. The core idea of the proposed approach is to include the information about the subset cardinality into the learning framework by extending the dimension of the velocity. The 2D-learning framework retains all the key features of the original PSO, despite the extra learning dimension. Most of the popular variants of PSO can easily be adapted into this 2D learning framework for feature selection problems. The efficacy of the proposed learning approach has been evaluated considering several benchmark data and two induction algorithms: Naive-Bayes and k-Nearest Neighbor. The results of the comparative investigation including the time-complexity analysis with GA, ACO and five other PSO variants illustrate that the proposed 2D learning approach gives feature subset with relatively smaller cardinality and better classification performance with shorter run times.

SUMMARY A new technique employing S‐transform (ST)‐based features, a three‐module artificial neural network and a final rule‐based classifier is proposed in this paper for the identification and classification of power quality disturbances. To validate the proposed method, various short duration disturbances such as voltage sag, voltage swell, momentary interruption, harmonics, oscillatory transient and impulsive transient, defined in terms of their spectral content, duration and magnitude by IEEE 1159–2009 standards, are used. In addition, simultaneous disturbances such as voltage sag with harmonics, voltage swell with harmonics and voltage sag with impulsive transients are also used. Further, the performance of the proposed system is evaluated in the noisy environment with the addition of Gaussian white noise in the aforementioned signals. The proposed three‐module ANN structure has less training requirements, higher accuracy and enhanced ability to classify simultaneous disturbances. The distinct capabilities of extracted features from ST, the three‐module ANN and final rule base classifier enable the proposed system to identify and classify both single and simultaneous disturbances effectively. Copyright © 2013 John Wiley & Sons, Ltd.

A novel method of classifying Power quality (PQ) events using Wavelet Packet Transform (WPT) and Extreme Learning Machines (ELM) has been proposed. In recent times, the power quality has been a major research concern due to changing regulations, liberalized distribution market and increased use of power electronic based equipment. The first step of any remedial action requires proper identification of PQ events. One of the major challenge of this event identification is to extract significant features from the limited measurements, which can subsequently be used for the classification. Therefore, in the present study Wavelet Packet Transform (WPT) has been used to obtain several mathematical features. These features can segregate both single and simultaneous PQ event occurrences. Further to improve the classification performance, the ELM based classifier has been used. This classifier significantly reduces the training time by many-fold. The performance of the proposed approach has been compared with ANN based classifier considering over 1000 PQ signals from various PQ events. The results of the simulation demonstrate that the proposed approach can achieve over 99% classification accuracy.

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