Enhanced Recognition of Power Quality Disturbances through an Augmented S-transform and XGBOOST Algorithm

Author:

Yu Jin1,Yu Zhun2,Ye Wenzhen1

Affiliation:

1. Guangzhou Xinhua University,School of Information and Intelligent Engineering,Guangzhou,China

2. Hunan Institute of Metrology and Testing,Changsha,China

Publisher

IEEE

Reference10 articles.

1. S-transform based on modified energy concentration and identification of power quality disturbance in random forest[J];Jian;Electrical Measurement & Instrumentation,2019

2. Power-quality issues and the need for reactive-power compensation in the grid integration of wind power

3. Voltage deviation orecasting based on improved ensemble clustering and BP neural network[J];Zhifang;Advanced Technology of Electrical Engineering and Energy,2018

4. Classification for hybrid power quality disturbance based on STFT and its spectral kurtosis[J];Jianming;Power System Technology,2014

5. Voltage sag detection method based on complex wavelet transform and RMS algorithm[J];Yan;Electrical Measurement & Instrumentation,2017

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