DC offset removal for phase estimation based on DNN in power system using less than one cycle waveform

2020 ◽  
Author(s):  
V. Sok ◽  
S. Key ◽  
Chang-Sung Ko ◽  
Su-Hwan Kim ◽  
Young-Ik Son ◽  
...  
Keyword(s):  
Power System ◽  
Phase Estimation ◽  
Dc Offset

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

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1619
Author(s):  
Kim ◽  
Sok ◽  
Kang ◽  
Lee ◽  
Nam
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Deep Neural Network ◽  
Phase Estimation ◽  
Fault Current ◽  
Dc Offset ◽  
Intensive Training ◽  
Unsupervised Training ◽  
Training Methodologies ◽  
Hidden Layer

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.

scholarly journals Robust Power System Frequency Estimation Based on a Sliding Window Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Dun Pei ◽  
Yili Xia
Keyword(s):  
Power System ◽  
Frequency Estimation ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Sliding Windows ◽  
Dc Offset ◽  
Three Phase ◽  
Window Approach ◽  
System Frequency ◽  
The Relationship

A novel framework for online estimation of the fundamental frequency of power system in both the single-phase and three-phase cases is proposed. This is achieved based on the consideration of the relationship among the samples within every four consecutive sliding windows and the use of the Wiener filtering approach and an adaptive filter trained by the least mean square (LMS) algorithm. Compared with the original work proposed in Vizireanu, 2011, which employs the scalar samples, the proposed vector-valued methods alleviate the drawbacks, such as sensitivity to initial phase value, noise, harmonics, DC offset, and system unbalance. Simulations on both benchmark synthetic cases and for real-world scenarios support the analysis.

Two-level state estimation for large power system. Part 1: Algorithms

1988 ◽  
Vol 135 (4) ◽  
pp. 299 ◽  
Author(s):  
K.L. Lo ◽  
M.M. Salem ◽  
R.D. McColl ◽  
A.M. Moffatt
Keyword(s):  
Power System ◽  
State Estimation ◽  
Large Power ◽  
System Part

Developments in Power-System Protection

IEE Review ◽  
1989 ◽  
Vol 35 (6) ◽  
pp. 220
Author(s):  
J.H. Naylor
Keyword(s):  
Power System ◽  
Power System Protection ◽  
System Protection

The UoSAT-2 spacecraft power system

1987 ◽  
Vol 57 (5S) ◽  
pp. S116 ◽  
Author(s):  
H.S. Wong ◽  
M.J. Blewett
Keyword(s):  
Power System
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