Performance Comparison of Two Real-Time Power System Frequency Estimation Methods

2017 ◽  
pp. 421-429
Author(s):  
R. B. Sharma ◽  
G. M. Dhole ◽  
M. B. Tasare
Keyword(s):  
Power System ◽  
Real Time ◽  
Frequency Estimation ◽  
Performance Comparison ◽  
Estimation Methods ◽  
System Frequency

Optimal orthogonal subband fusion for online power system frequency estimation

2016 ◽  
Vol 52 (5) ◽  
pp. 390-392 ◽  
Author(s):  
Bo Yang ◽  
Xiao Yu Peng ◽  
Wen Feng Zheng ◽  
Shan Liu
Keyword(s):  
Power System ◽  
Frequency Estimation ◽  
System Frequency

Power system frequency estimation utilizing the Newton-Raphson method

1994 ◽  
Vol 77 (3) ◽  
pp. 221-226 ◽  
Author(s):  
M. B. Durić ◽  
V. V. Terzija ◽  
I. A. Škokljev
Keyword(s):  
Power System ◽  
Frequency Estimation ◽  
Newton Raphson ◽  
System Frequency ◽  
Raphson Method

A Comparison of Beat Frequency Estimation Methods for Large Ring Laser

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jailos Mrisho Nzumile ◽  
Keyword(s):  
Real Time ◽  
Noise Level ◽  
Ring Laser ◽  
Frequency Estimation ◽  
Low Noise ◽  
Estimation Methods ◽  
Acquisition Rate ◽  
Large Ring ◽  
True Value ◽  
Output Characteristics

Autoregressive (AR2) technique has always been used to estimate frequency of the output signal from Large ring laser. However, the acquisition rate is not at near real time which is the requirement and noise level still challenge the process resulting to errors in the final estimation. A research was done to compare the Autoregressive (AR2) with the counterparts such as Pisarenko, Quinn, Hilbert and Phase looking for a better technique that will estimate the frequency at near real time to minimize errors. Secondary data from G and C – II ring laser were used during the comparison between the techniques and Autoregressive (AR2). Results shows that, the output characteristics from the counterpart does not depict the oscillations of the Earth rotation as expected contrast to that of Autoregressive (AR2) which does. Moreover, there were much deviation from the expected true value for the techniques contrast to that of AR2 which is very minimum. On the other hand, when the C – II data were used, it was observed that both techniques resemble on their output characteristics though AR2 was still better in the acquisition rate expect for Hilbert transform which does not resemble with others. Following the scope of this paper, Autoregressive (AR2) technique still emerge as a favorite frequency estimation technique contrast to the four counterparts due to its robustness, high acquisition rate as well as low noise level.

Power system frequency estimation using a least mean squares differentiator

2017 ◽  
Vol 87 ◽  
pp. 166-175 ◽  
Author(s):  
Carlos João Ramos ◽  
António Pina Martins ◽  
Adriano da Silva Carvalho
Keyword(s):  
Power System ◽  
Frequency Estimation ◽  
Least Mean Squares ◽  
System Frequency

A new time-domain based power system frequency estimation algorithm

2011 ◽  
Vol 22 (4) ◽  
pp. 433-448 ◽  
Author(s):  
Heresh Seyedi ◽  
Majid Sanaye-Pasand
Keyword(s):  
Power System ◽  
Time Domain ◽  
Frequency Estimation ◽  
Estimation Algorithm ◽  
System Frequency ◽  
New Time

Power system frequency estimation using morphological prediction of Clarke components

2015 ◽  
Vol 122 ◽  
pp. 208-217 ◽  
Author(s):  
E.M. dos Santos ◽  
J.P. Juchem Neto ◽  
G. Marchesan ◽  
G. Cardoso
Keyword(s):  
Power System ◽  
Frequency Estimation ◽  
System Frequency
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