Robust harmonic estimation using forgetting factor

Performance of multi-innovation identification for attenuating excitation of stochastic systems. In the hybrid algorithm applied in this paper, amplitudes and phases are estimated simultaneously. This approach is the robustification of Kalman filter which exhibits robust characteristics and fast convergence properties.

Journal of Global Optimization, 11 4: Previous article in issue. International Conference on pp. It is considered as a serious concern now a day.

Abstract The impact of nonlinear loads produces harmonic pollution in electrical power system. Electric Power System Research, 79 1: The technique is applied and tested for both stationary as well as dynamic signals containing harmonics.

Acta Automatica Sinica, 22 1: Whereas, many algorithms have been proposed for harmonic estimation to improve the power quality performance but till date the accurate estimation of power quality parameters remains a challenge.

Thus, BFO algorithm is used for initial estimation. Robust harmonic estimation using Forgetting Factor RLS The prime reasons for power quality degradation include voltage sag, swell and momentary interrup Fast tracking RLS algorithm using novel variable forgetting factor with unity zone.

It must be mentioned that harmonic estimation is a nonlinear problem and using linear optimization algorithms for solving this problem reduces the convergence speed. Applied Soft Computing, 12 8 First time BRLS algorithm is proposed for harmonic estimation in power system.

He is assistant professor of control engineering in the department of electrical and computer engineering of Babol University of Technology from Annals of Statistics, 10 1: In this paper the Forgetting Factor RLS FFRLS approach has been considered to estimate not only voltage sag, swell, momentary interruption but also the amplitudes and phases of harmonics in case of time varying power signals in presence of White Gaussian Noise.

Comparison results for different nonlinear dynamic plants with forgetting factor recursive least square FFRLS and extended Kalman filter E His research interests are fractional-order control, time delay systems, nonlinear control, adaptive systems, and model predictive control.

Many algorithms have been proposed for harmonic estimation to improve the power quality. A synergy of differential evolution and bacterial foraging optimization for faster global search.

This proves the superiority of the proposed method. Thus accurate computation of harmonics is really a challenging problem in power system. Electronics Letters, 27 23 Practical validation of the proposed algorithm is also made along with the real time data obtained from a Variable Frequency Drive VFD panel used for controlling the speed and torque of the induction motor used at a large paper industry.

His research interests are hybrid and complex nonlinear systems, and control theory and applications especially intelligent, adaptive and predictive control methods. He is currently is M. His research interests is system identification, heuristic optimization algorithms, and harmonic estimation in power system.

A hybrid least squares-GA-based algorithm for harmonic estimation. 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.

Chebyshev polynomials, Legendre polynomials, trigonometric expansions using sine and cosine functions as well as wavelet basis functions are used for the functional expansion of input patterns.

References [1] Tang, Y. In this paper a non-linear adaptive algorithm, called Bilinear Recursive Least Square BRLShas been applied for the first time for estimating the amplitudes, phases and frequency in case of time varying power signals containing harmonics, sub harmonics, inter harmonics in presence of White Gaussian Noise.The impact of nonlinear loads produces harmonic pollution in electrical power system.

It is considered as a serious concern now a day. Whereas, many algorithms have been proposed for harmonic estimation to improve the power quality performance but till date the accurate estimation of power quality parameters remains a challenge.

forgetting factor following the bacterial foraging optimization algorithm (BFO). It must be mentioned that harmonic estimation is a nonlinear problem and using linear. forgetting factor is chosen to estimate swell, sag, momentary interruptions as well as harmonic amplitudes and mi-centre.com forgetting factor can be tuned using.

Abstract: The prime reasons for power quality degradation include voltage sag, swell and momentary interruptions and also the presence of harmonics. Thus accurate computation of harmonics is really a challenging problem in power system. Many algorithms have been proposed for harmonic estimation to.

The prime reasons for power quality degradation include voltage sag, swell and momentary interruptions and also the presence of harmonics.

Thus accurate computation of harmonics is really a. Robust estimation of power quality disturbances using unscented H values of measurement and process noise co-variances are taken as and and for RLS algorithm the value of forgetting factor is chosen to be M.

Joorabian, S.S. Mortazavi, A.A. KhayyamiHarmonic Estimation in a Power System using a novel .

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Robust harmonic estimation using forgetting factor
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