Detection of Power Quality Disturbances Using Wavelet Transform Technique

PQ (Power Quality) has become a major concern owing to increased use of sensitive electronic equipment. In order to improve PQ proble ms, the detection of PQ disturbances must be carried out first. It is the fact that PQ d isturbances vary in a wide range of time and frequency, which make automatic detection of PQ problems often difficult and elusive to diagnose. Hence one of the most important issues in power quality problems nowadays is how to detect these disturbance waveforms automatically in an efficient manner. PQ disturbances have been defined into several cat egories and software based novel approach techniques for detection of PQ disturbances by time and frequency analysis with wavelet transform are proposed. These techniques detect PQ problems of waveform distortion and provide a promising tool in the field of electrical power quality problems.

[1]  A.V. Mamishev,et al.  Classification of power quality events using optimal time-frequency representations-Part 1: theory , 2004, IEEE Transactions on Power Delivery.

[2]  A. Y. Chikhani,et al.  Power quality detection and classification using wavelet-multiresolution signal decomposition , 1999 .

[3]  R. A. Flores State of the art in the classification of power quality events, an overview , 2002, 10th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.02EX630).

[4]  Steven Kunsman,et al.  Improving power quality , 2000 .

[5]  Ganapati Panda,et al.  Frequency estimation of distorted power system signals using extended complex Kalman filter , 1999 .

[6]  S. Santoso,et al.  Power quality assessment via wavelet transform analysis , 1996 .

[7]  John R. Williams,et al.  Introduction to wavelets in engineering , 1994 .

[8]  Edward J. Powers,et al.  Power quality disturbance waveform recognition using wavelet-based neural classifier. I. Theoretical foundation , 2000 .

[9]  A. Elmitwally,et al.  Proposed wavelet-neurofuzzy combined system for power quality violations detection and diagnosis , 2001 .

[10]  Adly A. Girgis,et al.  Power system transient and harmonic studies using wavelet transform , 1999 .

[11]  Adly A. Girgis,et al.  Identification and tracking of harmonic sources in a power system using a Kalman filter , 1996 .

[12]  Peter Minns,et al.  Electric power quality disturbance classification using self-adapting artificial neural networks , 2002 .

[13]  A. K. Ghosh,et al.  The classification of power system disturbance waveforms using a neural network approach , 1994 .