A Study on Deep Neural Network-Based DC Offset Removal for Phase Estimation in Power Systems

A Study on Deep Neural Network-Based DC Offset Removal for Phase Estimation in Power Systems

The purpose of this paper is to remove the exponentially decaying DC offset in fault current waveforms using a deep neural network (DNN), even under harmonics and noise distortion. The DNN is implemented using the TensorFlow library based on Python. Autoencoders are utilized to determine the number...

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Bibliographic Details
Main Authors: Sun-Bin Kim, Vattanak Sok, Sang-Hee Kang, Nam-Ho Lee, Soon-Ryul Nam
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Energies
Subjects:
autoencoder
exponentially decaying DC offset
deep neural networks (DNNs)
optimal size
supervised training
Tensorflow
unsupervised training
Online Access:https://www.mdpi.com/1996-1073/12/9/1619

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https://www.mdpi.com/1996-1073/12/9/1619

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