Title: Power quality disturbance recognition using hybrid signal processing and machine intelligence techniques

Authors: Manohar Mishra; Pravat Kumar Rout; Sangram Keshari Routray; Niranjan Nayak

Addresses: Department of Electrical Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Jagamara, Khandagiri, Bhubaneswar – 751030, Orissa, India ' Department of Electrical Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Jagamara, Khandagiri, Bhubaneswar – 751030, Orissa, India ' Department of Electrical Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Jagamara, Khandagiri, Bhubaneswar – 751030, Orissa, India ' Department of Electrical Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Jagamara, Khandagiri, Bhubaneswar – 751030, Orissa, India

Abstract: This paper presents two novel approaches for power quality (PQ) event classification. It is a two stage system in which optimal features that characterise the complete signal behaviour are extracted in the first stage and in second stage, based on these features various disturbance waveforms are classified. In the first classifier, a hybrid approach between S-transform and decision tree (DT) is presented. In the second classifier, the S-transform (ST) technique is integrated with neural network (NN) model with multilayer perceptron. Power system suffers from different PQ events such as sag, swell, momentary interruptions, impulsive transients, notch, spike, harmonics and also combination of the above with noise. The above-mentioned events comprise high-frequency and low-frequency components. Thus, it is difficult to classify these PQ events using traditional approaches. Both the classification methods derive various statistical parameters of eight types of single power disturbance and two types of complex power disturbance using generalised S-transform. After the required features are extracted, the neural network and the decision tree are used for power quality event detection. The analysis and simulation results show that the proposed classifiers can effectively classify with higher degree of accuracy to recognise the different PQ disturbances even under noise contamination.

Keywords: power quality; neural networks; multiresolution analysis; S-transform; decision trees; feature extraction; power disturbance recognition; signal processing; machine intelligence; multilayer perceptron; disturbance waveforms; power quality event detection; simulation.

DOI: 10.1504/IJIED.2014.059217

International Journal of Industrial Electronics and Drives, 2014 Vol.1 No.2, pp.91 - 104

Received: 23 Feb 2013
Accepted: 05 Jun 2013
Published online: 24 Jul 2014 *

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