Article citationsMore>>

Khokhar, S., Zin, A.A.M., Memon, A.P. and Mokhtar, A.S. (2017) A New Optimal Feature Selection Algorithm for Classification of Power Quality Disturbances Using Discrete Wavelet Transform and Probabilistic Neural Network. Measurement, 95, 246-259.
https://doi.org/10.1016/j.measurement.2016.10.013

has been cited by the following article:

• TITLE: A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

  AUTHORS: Mian Khuram Ahsan, Tianhong Pan, Zhengming Li

  KEYWORDS: Power Quality, Feature Extraction, Power Quality Disturbances, Power Quality Events Classifier

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.6 No.8, August 1, 2018

  ABSTRACT: Around the globe, the necessity of green supply with a dedicated standard quality thrust of consumers is increasing day by day. The advancement in technology urges the electrical power system to deliver a high-quality rated undistorted sinusoidal current, the voltage at a constant desired standard frequency to its consumers. The present paper reveals a complete and inclusive study of power quality events, such as automatic classification and signal processing via creative techniques and the noises effect on the detection and classification of power quality disturbances. It’s planned to make a possible list for quick reference to obtain an extensive variety on the condition & status of available research for detection and classification for young engineers, designers and researchers who enter in the power quality field. The current extensive study is supported by a critical review of more than 200 publications on detection and classification techniques of power quality disturbances.