American Journal of Engineering and Applied Sciences

Review of Signal Processing Techniques for Detection of Power Quality Events

Abhijith Augustine, Ruban Deva Prakash, Rajy Xavier and Mareeta Cheriyan Parassery

DOI : 10.3844/ajeassp.2016.364.370

American Journal of Engineering and Applied Sciences

Volume 9, Issue 2

Pages 364-370

Abstract

The challenging process industry requires true power for its smooth functioning and here comes the importance of good power or power. The term Power Quality (PQ) aims at supplying true power to the process. The scope of the power quality increased with the introduction of newly designed sophisticated devices like computers and microcontrollers. The performances of these devices are extremely sensitive to the various power quality problems. The mainly occurring PQ problems are voltage sag, voltage swell, voltage flickers, harmonics distortions etc. The concept of power quality became increasingly complex and vital with the introduction of recently designed sophisticated and sensitive devices, whose real time performance is extremely subjective to sensitiveness of the supply. Power Quality (PQ) has turned to be a serious issue to electricity consumers at all levels. Power quality is a major concern to electricity consumers today. The sensitivity factor of the power electronic equipment and non-linear loads to the input excitations voltages are widely used in process control as well as individual consumers which lead to the PQ problem. The paper gives a brief review in accordance with relevant literature surveys classifies the various electric power quality disturbances using wavelet transform analysis. The survey includes detection voltage disturbances and categorization of the type of event. The power quality analyzer is designed and used to measure the occurrence and classification of PQ events. Malfunction of the equipment will happens when the power failure occurs. Several signal processing techniques for the detection and classification of these disturbances are studied and discussed here. The detection techniques are mainly based on signal averaging, RMS method, Kalman Filter method, Fourier Transforms, Wavelet Transforms etc. Wavelets and fast Fourier transforms are of major importance in the classification.

Copyright

© 2016 Abhijith Augustine, Ruban Deva Prakash, Rajy Xavier and Mareeta Cheriyan Parassery. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.