Robust harmonic estimation using forgetting factor

The lateral tire force and force where and are the generalized cornering stiffness of the front and rear tires, respectively. A related topic is that of resistant statisticswhich are resistant to the effect of extreme scores.

Thus accurate computation of harmonics is really a challenging problem in power system. In fact, the mean, median and trimmed mean are all special cases of M-estimators. Also many of power system loads, especially industrial loads are dynamic in nature, which implies time varying amplitude of the current waveform.

This is essential for designing filter for interruptions and also the presence of harmonics. These outliers have a large effect on the mean, dragging it towards them, and away from the center of the bulk of the data.

Robust harmonic estimation using Forgetting Factor RLS The prime reasons for power quality degradation include voltage sag, swell and momentary interrup The pitch, roll, and vertical motions and the suspension system of the vehicle are ignored.

A frequency domain optimal linear estimator is proposed which incorporates the masking properties of the human auditory system to make the residual noise distortion inaudible.

Another proposed solution was S-estimation. Manual screening for outliers[ edit ] Traditionally, statisticians would manually screen data for outliersand remove them, usually checking the source of the data to see whether the outliers were erroneously recorded.

In the last two decades, postfilters have been developed that can be used in conjunction with a single microphone acoustic echo canceller to enhance the near-end speech. Acta Automatica Sinica, 22 1: His research interests are fractional-order control, time delay systems, nonlinear control, adaptive systems, and model predictive control.

These interferences degrade the fidelity and intelligibility of near-end speech. Annals of Statistics, 10 1: Estimation of scale[ edit ] Main article: His research interests is system identification, heuristic optimization algorithms, and harmonic estimation in power system.

Show Context Citation Context The second signal is considered to show the tracking capability of the filter when the signal is distorted with higher order harmonics. One of the most important cases is distributional robustness. In most prior studies, the longitudinal force observer is designed for traditional internal combustion engine vehicle, and the longitudinal force estimation for EVs especially for 4WID-EVs is still relatively hard to see.

This approach is the robustification of Kalman filter which exhibits robust characteristics and fast convergence properties.

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References [1] Tang, Y. Seldom studies have introduced this concept into longitudinal force estimation. International Conference on pp. Bayesian robust regression, being fully parametric, relies heavily on such distributions.

Journal of Global Optimization, 11 4: In this paper the Forgetting Factor RLS FFRLS In this paper, Fogetting Factor RLS has been approach has been considered to estimate not only voltage proposed for estimating sag, swell, momentary interruption as sag,swell,momentary interruption but also the amplitudes and well as amplitudes and phases of different harmonics [7] of phases of harmonics in case of time varying power signals in distorted power signals in presence of white noise.

Implementation of Shunt Active Power Filter(SAPF) algorithms

However, dereverberation of the near-end speech was not addressed in this context. · system equations (1)-(5), the optimal forgetting factor can be obtained through iterative computation of the equation Oft(it; k) ¥l = 0, 1, 2  · estimation, the so-called M robust estimation, to the estimation of both filter parameters and noise variance simultaneously.

The application of variable forgetting factor, calculated adaptively with respect to the  · Linear Kalman Filter Algorithm with Clarke Transformation for Power System Frequency Estimation sequential tuning of the forgetting factor.

By this, the is used for the power system frequency and spectra estimation. The includes up to 31st-order harmonic components. Using the central numerical differentiation  · On the other hand, the great majority of harmonic state estimation algorithms pro- system voltage harmonics using artificial neural networks.

Haili Ma and Girgis [11] pre- Therefore, a robust dynamic harmonic state estimator using the extended  · To avoid weighting the distant past as much as the present, a forgetting factor is also introduced. We show that, under appropriate observability assumptions, the optimal estimate converges globally asymptotically to the true value of the state in the absence of noise and  · A GMM-Based Robust Incremental Adaptation with a Forgetting Factor for Speaker Verification Eunyoung Kim1,*, Minkyung Kim2, A GMM-Based RIA with a Forgetting Factor for SV we propose a GMM-based robust incremental adaptation (RIA) method with a forgetting factor

Robust harmonic estimation using forgetting factor
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