A comparative study on effective signal processing tools for power quality monitoring

Due to the growing number of sources of disturbances in AC power systems; there is an ever increasing need for power quality monitoring systems. However, for an accurate analysis, a large amount of data is required in such systems, and there is a need for improvements to analyse the captured data automatically. Therefore, the signals obtained from such monitoring systems require further processing to be able to distinguish the type of disturbances. This paper investigates the efficiency of various signal-processing techniques in detecting and extracting the features of power quality signals. The paper utilizes real time signals in a power system and offers a new technique to monitor all three-phase signals simultaneously, which eliminates the limitations of the existing single-phase based analysis techniques

[1]  Hilmi M. Turanli.  Preparing for the Next Generation at Manitoba Hydro , 2002, IEEE Power Engineering Review.

[2]  Weon-Ki Yoon,et al.  Reactive power measurement using the wavelet transform , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[3]  N. Ertugrul,et al.  COMPARATIVE STUDY OF USING DIFFERENT MOTHER WAVELETS ON POWER QUALITY MONITORING , 2004 .

[4]  P.K. Dash,et al.  Multiresolution S-transform-based fuzzy recognition system for power quality events , 2004, IEEE Transactions on Power Delivery.

[5]  Mohamed Benbouzid,et al.  Monitoring and diagnosis of induction motors electrical faults using a current Park's vector pattern learning approach , 2000 .

[6]  Mohamed Benbouzid,et al.  Monitoring and diagnosis of induction motors electrical faults using a current Park's vector pattern learning approach , 1999, IEEE International Electric Machines and Drives Conference. IEMDC'99. Proceedings (Cat. No.99EX272).

[7]  Irene Yu-Hua Gu,et al.  Bridge the gap: signal processing for power quality applications , 2003 .

[8]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

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

[10]  Math Bollen,et al.  Time-frequency and time-scale domain analysis of voltage disturbances , 2000 .

[11]  B. Ogayar,et al.  Application of signal processing tools for power quality analysis , 2002, IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373).