@inproceedings{dbe9564e47b8484aacc89112a4f46113,
title = "Fault diagnosis of star-connected auto-transformer based 24-pulse rectifier",
abstract = "This paper proposes a fault diagnosis method for star-connected auto-transformer based 24-pulse rectifier by integrating artificial neural networks (ANN) with wavelet packet decomposition (WPD) and principal component analysis (PCA). The WPD is employed to extract the features of different fault waveforms of the output voltage of the rectifier. PCA is adopted to reduce the dimensionality of the extracted feature vectors, which leads to fast computation of the algorithm. BP neural network is adopted to classify the fault types and determine the fault location according to the extracted features. These faults are simulated in real-time simulation platform and the data are then analyzed with MATLAB. Compared with other diagnosis methods, the proposed method shows better performance and faster response.",
keywords = "ATRU, BP neural network, fault diagnosis, PCA, WPD",
author = "Wei Wu and Xiaobin Zhang and Wenli Yao and Weilin Li",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 5th IEEE International Workshop on Applied Measurements for Power Systems, AMPS 2014 ; Conference date: 24-09-2014 Through 26-09-2014",
year = "2014",
month = nov,
day = "5",
doi = "10.1109/AMPS.2014.6947721",
language = "英语",
series = "2014 IEEE International Workshop on Applied Measurements for Power Systems, AMPS 2014 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "132--137",
booktitle = "2014 IEEE International Workshop on Applied Measurements for Power Systems, AMPS 2014 - Proceedings",
}