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

被引：10

作者：

Kim, Sun-Bin ^{[1
]}

Sok, Vattanak ^{[1
]}

Kang, Sang-Hee ^{[1
]}

Lee, Nam-Ho ^{[2
]}

Nam, Soon-Ryul ^{[1
]}

机构：

[1] Myongji Univ, Dept Elect Engn, Yongin 17058, South Korea

[2] Korea Elect Power Res Inst, Daejeon 34056, South Korea

来源：

ENERGIES | 2019年 / 12卷 / 09期

关键词：

autoencoder; exponentially decaying DC offset; deep neural networks (DNNs); optimal size; supervised training; Tensorflow; unsupervised training; DECAYING DC; ESTIMATION ALGORITHM;

D O I：

10.3390/en12091619

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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.

引用

页数：19