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

Sun-Bin Kim, Vattanak Sok, Sang-Hee Kang, Nam-Ho Lee and Soon-Ryul Nam
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Vattanak Sok: Department of Electrical Engineering, Myongji University, Yongin 17058, Korea
Sang-Hee Kang: Department of Electrical Engineering, Myongji University, Yongin 17058, Korea
Nam-Ho Lee: Korea Electric Power Research Institute, Daejeon 34056, Korea
Soon-Ryul Nam: Department of Electrical Engineering, Myongji University, Yongin 17058, Korea

Energies, 2019, vol. 12, issue 9, 1-19

Abstract: 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 of neurons in each hidden layer. Then, the number of hidden layers is experimentally decided by comparing the performance of DNNs with different numbers of hidden layers. Once the optimal DNN size has been determined, intensive training is performed using both the supervised and unsupervised training methodologies. Through various case studies, it was verified that the DNN is immune to harmonics, noise distortion, and variation of the time constant of the DC offset. In addition, it was found that the DNN can be applied to power systems with different voltage levels.

Keywords: autoencoder; exponentially decaying DC offset; deep neural networks (DNNs); optimal size; supervised training; Tensorflow; unsupervised training (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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