Robust harmonic estimation using Forgetting Factor RLS

Sahoo, Harish Kumar; Sharma, Pradeep; Rath, N. P.

doi:10.1109/indcon.2011.6139563

Cited by 7 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However various adaptive filtering approaches has been applied to characterize harmonic signal parameters such as Least Mean Square (LMS) [3][4], Extended Least Mean Square (ELMS) [5][6], Recursive Least Square (RLS) [7][8], Forgetting Factor Recursive Least Square (FFRLS) [9], Nonnalized Least Mean Square (NLMS) [10][11] etc but each of them has several limitations in tenns of inaccuracies due to the presence of noise, hannonics and other changing parameters such as change in fault inception angle, change in fault resistance etc in the signal.…”

Section: Santosh K Singh Is a Phd Research Scholar In Electrical Engmentioning

confidence: 99%

“…Keeping this in mind, two iterative adaptive filtering algorithms such as Least Mean Square (LMS) [9] and a novel Variable Constrained based Least Mean Square (VCLMS) along with their comparisons are presented in this paper for fast and accurate estimation of nominal and off-nominal power system frequency of a harmonic signal. During comparison we observe that the proposed VCLMS algorithm outperforms on LMS algorithm.…”

Section: Santosh K Singh Is a Phd Research Scholar In Electrical Engmentioning

confidence: 99%

See 1 more Smart Citation

Variable constrained based LMS algorithm for power system harmonic estimation

Singh

Nath

Chakraborty

et al. 2014

2014 International Conference on Communication and Signal Processing

View full text Add to dashboard Cite

Many algorithms have been proposed for harmonic estimation to improve the power quality performance but till today it is still a challenge for accurate estimation. In this paper an adaptive filtering algorithm called Variable Constrained based Least Mean Square (VCLMS) is applied for the first time for estimating harmonic parameters. The algorithm has been considered to estimate the amplitudes and phases in case of time varying power signals containing harmonics in the presence of White Gaussian Noise in MATLAB simulating environment. Also a comparison between a recently proposed algorithm i.e. Least Mean Square (LMS) are presented to show the effectiveness of the proposed VCLMS algorithm.

show abstract

Section: Santosh K Singh Is a Phd Research Scholar In Electrical Engmentioning

confidence: 99%

Section: Santosh K Singh Is a Phd Research Scholar In Electrical Engmentioning

confidence: 99%

Variable constrained based LMS algorithm for power system harmonic estimation

Singh

Nath

Chakraborty

et al. 2014

2014 International Conference on Communication and Signal Processing

View full text Add to dashboard Cite

show abstract

“…However, because harmonic source parameters and system operating conditions are not constant in real power systems, this assumption can lead large errors in the assessment. Several harmonic assessment methods based on recursive least square (RLS) algorithm were presented [21]- [23]. However, these methods have a potential 'wind-up' problem because the RLS algorithm uses constant forgetting factor.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Assessing the Contribution of Harmonic Sources to Voltage Distortion in Power Systems

et al. 2020

View full text Add to dashboard Cite

This paper presents a method for evaluating the harmonic contributions of multiple harmonic sources to voltage distortion at the point of common coupling (PCC). The proposed method quantitatively represents harmonic contributions based on the equivalent voltage models of harmonic sources. The equivalent voltage models can be estimated using the recursive least square (RLS) algorithm and measured voltage and current at the PCC. The assessment of harmonic contribution heavily depends on the accuracy of equivalent model estimation. Many existing studies have assumed that the equivalent model parameters of harmonic sources are not changed. However, because harmonic source parameters and system operating conditions are not constant in real power systems, this assumption can lead large errors in harmonic contribution assessment. In this paper, we propose a new assessment method based on variable forgetting factor RLS and parameter change detection to solve the problem. The proposed method is also effective for evaluating comprehensive harmonic contributions as well as individual harmonic contributions. The case studies show that the proposed method is superior to the previous one even under parameter changing conditions.

show abstract

“…In [23], estimation of harmonics that are integer multiplication of the principal frequency is performed using least square method and genetic algorithm. In [24], robust harmonic estimation of a time variable signal is investigated using RLSFF method. In [25,26], RLS algorithm with variable forgetting factor is explained.…”

Section: Introductionmentioning

confidence: 99%

Harmonic Components Estimation in Power System Using Bacterial Foraging Optimization Algorithm and Stochastic Gradient Algorithm With Variable Forgetting Factor

Mohammadzadeh

Sadati

Rezaie

2016

IIUMEJ

View full text Add to dashboard Cite

In this paper, a hybrid configuration algorithm called stochastic gradient method with variable forgetting factor (SGVFF) is proposed to better estimate unknown parameters in a power system such as amplitude and phase of harmonics using variable forgetting factor following the bacterial foraging optimization algorithm (BFO). It must be mentioned that harmonic estimation is a nonlinear problem and using linear optimization algorithms for solving this problem reduces the convergence speed. Thus, BFO algorithm is used for initial estimation. In this paper, first, using little information and by applying BFO algorithm in an off-line procedure initial value for SGVFF algorithm is achieved and then SGVFF algorithm is gained in an on-line procedure. In the hybrid algorithm applied in this paper, amplitudes and phases are estimated simultaneously. Simulation results indicate that the proposed method has faster convergence speed, better performance and higher accuracy in a noisy system in comparison with recursive least squares variable forgetting factors algorithm (RLSVFF). This proves the superiority of the proposed method.KEYWORDS:Â Power system harmonic; BFO algorithm; SGVFF method; RLSVFF method

show abstract

Robust harmonic estimation using Forgetting Factor RLS

Cited by 7 publications

References 7 publications

Variable constrained based LMS algorithm for power system harmonic estimation

Variable constrained based LMS algorithm for power system harmonic estimation

A Novel Method for Assessing the Contribution of Harmonic Sources to Voltage Distortion in Power Systems

Harmonic Components Estimation in Power System Using Bacterial Foraging Optimization Algorithm and Stochastic Gradient Algorithm With Variable Forgetting Factor

Contact Info

Product

Resources

About