Harmonic Fault Diagnosis in Power Quality System Using Harmonic Wavelet

Abstract The increasing use of non-linear loads such as power electronics, converters, arc furnaces, transformers, fluorescent and high intensity discharge lights have caused harmonics distortion in power quality (PQ) systems. On the other hand, harmonics have numerous effects on electrical systems. For examples, they can be troublesome to communication systems, they increase heating in the transformers and motors, and consequently decrease their life cycle. The first step to address these issues is to diagnose harmonic faults in power distribution systems. This paper introduces a new method for detecting harmonic faults using harmonic wavelets. For this purpose, harmonic wavelet transform (HWT) is used to decompose the faulty signal at different levels. Then, the energies of the decomposition levels based on parseval’s theorem are computed. Finally, the faulty signal is reconstructed with harmonics wavelets. Simulation results show that the suggested fault detection and diagnosis (FDD) system can successfully identify the maximum harmonic in the faulty signal and the amount of harmonics in the faulty signal compared to fundamental signal.

[1]  Thanatchai Kulworawanichpong,et al.  Recognition of power quality events by using multiwavelet-based neural networks , 2008 .

[2]  Saroj K. Meher,et al.  Fuzzy classifiers for power quality events analysis , 2010 .

[3]  Thai Nguyen,et al.  Power quality disturbance classification utilizing S-transform and binary feature matrix method , 2009 .

[4]  Mojtaba Kordestani,et al.  New estimation methodologies for well logging problems via a combination of fuzzy Kalman filter and different smoothers , 2016 .

[5]  José A. Aguado,et al.  Rule-based classification of power quality disturbances using S-transform , 2012 .

[6]  F. Choong,et al.  Expert System for Power Quality Disturbance Classifier , 2007, IEEE Transactions on Power Delivery.

[7]  Seyed Hossein Hosseinian,et al.  Power quality disturbance classification using a statistical and wavelet-based Hidden Markov Model with Dempster–Shafer algorithm , 2013 .

[8]  N. Ertugrul,et al.  Investigation of Effective Automatic Recognition Systems of Power-Quality Events , 2007, IEEE Transactions on Power Delivery.

[9]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Azah Mohamed,et al.  Power quality diagnosis using time frequency analysis and rule based techniques , 2011, Expert Syst. Appl..

[11]  Vassilios G. Agelidis,et al.  Evaluation of windowed ESPRIT virtual instrument for estimating Power Quality Indices , 2011 .

[12]  Bijaya Ketan Panigrahi,et al.  Detection and classification of power quality disturbances using S-transform and modular neural network , 2008 .

[13]  S.N. Singh,et al.  Denoising Techniques With Change-Point Approach for Wavelet-Based Power-Quality Monitoring , 2009, IEEE Transactions on Power Delivery.

[14]  J.M.T. Romano,et al.  An improved method for signal processing and compression in power quality evaluation , 2004, IEEE Transactions on Power Delivery.

[15]  Huseyin Eristi,et al.  Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines , 2013 .

[16]  Rajiv Kapoor,et al.  Classification of power quality events – A review , 2012 .