Detection and Classification of Power Quality Disturbances in the Supply to Induction Motor Using Wavelet Transform and Neural Networks

Sukrutha Jade, Sukrutha Jade,,K Uma Rao,Sridhar S

doi:10.17694/bajece.62699

Sukrutha Jade, Sukrutha Jade,, K Uma Rao + Show 1 more

Open Access

https://doi.org/10.17694/bajece.62699

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Abstract

This paper aims to represent a proposition of an innovative and novel methodology applicable in detecting and classifying the power quality disturbances present in the supply to the induction motor. In all practicality, considering circumstantial real world applications, induction motors are usually operated on load. If the supply voltage is varied in any way, it would adversely affect the normal operation of the motor. In the present work, a healthy induction motor is subjected to power quality disturbances like balanced voltage sag, balanced voltage swell, unbalanced voltage sag and unbalanced voltage swell. For the purpose of detecting these power quality disturbances, discrete wavelet transform is applied to the stator current of the induction motor. The stator current wavelet coefficients are fed as input to the neural network for the classification purpose. Radial basis neural network and feed forward neural network have been independently trained and tested. The observation about the feedforward network having higher performance efficiency as compared to the radial basis network, has been seen.

Highlights

DESPITE espite several precautions taken, supply in the grid is never perfectly balanced
Though Fast Fourier Transforms (FFT), Short Time Fourier Transforms (STFT) are good signal processing techniques, they suffer from the fact that FFT provides only spectral information of the signal without time localization, and STFT provides fixed window width, which is more suitable for stationary signals
The stator current of phase stator current (phase A) corresponding to balanced supply, supply with balanced sag and unbalanced sag, supply with balanced swell and unbalanced swell are as given in Fig. 9 to Fig 13

Summary

Introduction

DESPITE espite several precautions taken, supply in the grid is never perfectly balanced Due to this imbalance in the supply, a lot of harmonics are generated which will lead to increase in losses and decrease in efficiency of the machine which is directly connected to them. Wavelet transform is an excellent tool for the analysis of notstationary signal as it employs a flexible window to obtain both time and spectral information of the signal. These signal processing techniques in conjunction with certain classification techniques like a neural network, fuzzy logic, neuro- fuzzy, support vector machine and expert systems have been used in the past for the classification of the power quality disturbances [3-14]. Chuah Heng Keow et al [15] has proposed a scheme for enhancing power quality problem classification based on wavelet transform and a rule-based method

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