IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 5, No. 1, March 2016, pp. 1~12 ISSN: 2252-8938
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Classification of Power Quality Events Using Wavelet analysis and Probabilistic neural Network Pampa Sinha*, Sudipta Debath**, Swapan Kumar Goswami ** * Electrical Engineering Department, Netaji Subhash Engineering College, Technocity, Garia ** Electrical Engineering Department, Jadavpur Univrsity
Article Info
ABSTRACT
Article history:
Power quality studies have become an important issue due to widespread use of sensitive electronic equipment in power system. The sources of power quality degradation must be investigated in order to improve the power quality. Switching transients in power systems is a concern in studies of equipment insulation coordination. In this paper a wavelet based neural network has been implemented to classify the transients due to capacitor switching, motor switching, faults, converter and transformer switching. The detail reactive powers for these five transients are determined and a model which uses the detail reactive power as the input to the Probabilistic neural network (PNN) is set up to classify the above mentioned transients. The simulation has been executed for an 11kv distribution system. With the help of neural network classifier, the transient signals are effectively classified.
Received Dec 4, 2015 Revised Feb 7, 2016 Accepted Feb 25, 2016 Keyword: Detail Reactive Powers, Power System, Probabilistic Neural Network. Switching-Transients, Wavelet Decomposition
Copyright © 2016 Institute of Advanced Engineering and Science. All rights reserved.
Corresponding Author: Pampa Sinha, Electrical Engineering Department, Netaji Subhash Engineering College, Technocity, Garia. Kolkata-700152,
Email: pampa.sinha.ee@gmail.com
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INTRODUCTION Detection and classification of transient signals have recently become an active research topic in the area of power quality analysis [1-5]. In [6-7] the authors have proposed a method to classify the power system transients based on dual tree complex wavelet transform (DTCWT). But in this method computational complexity is very high. Power transients occur from variety of disturbances on the power system like, capacitor bank switching, different types of faults, converters and different apparatus switching. In order to improve the power quality, the location of such disturbances must be identified. Techniques like wavelet transforms, mathematical morphology etc have been used to identify them [5-7]. Wavelet analysis, can extract the essential features of transient signal effectively for its classification. With these features as inputs to the neural network, classification of the switching transients, short circuit fault, primary arc, lightning disturbance and lightning strike fault is possible [8]. Authors of paper [9] have proposed a discrete wavelet transform (DWT) based on multiresolution analysis technique and parseval’s theorem which is employed to extract the energy distribution features of transient signal at different resolution levels. Probabilistic neural network (PNN) classifies the extracted features to identify the disturbance type. In paper [10] a discrete wavelet transform and multi fractal analysis based on a variance dimension trajectory technique have been used as tools to analyze the transients for feature extraction. A probabilistic neural network is used as a classifier for classification of transients associated with power system faults and switching. The authors of paper [11] presented DWT-FFT based integrated approach for detection and classification of various PQ disturbances with and without noisy environment. Ibrahim and Morcos [12] have given a survey of artificial intelligence technique for power quality, which includes fuzzy logic, artificial neural network (ANN) and genetic algorithm. Wavelet based on line disturbance detection for power quality applications have been discussed in [13]. Perunicic et al. [14] used wavelet coefficients of discrete wavelet transform (DWT) as Journal homepage: http://iaesjournal.com/online/index.php/IJAI