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The phase, frequency, and amplitude of singlephase voltages are the most important and basic information required for single-phase grid connected applications. This paper proposes a new "D-estimation method" for instantly estimati...
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The phase, frequency, and amplitude of singlephase voltages are the most important and basic information required for single-phase grid connected applications. This paper proposes a new "D-estimation method" for instantly estimating the phase, frequency, and amplitude of frequency-varying single-phase signals in such applications. The D-estimation method has the following attractive features: 1) it is a new filtering method using the Dfilters; 2) it involves the use of only filters and can be easily designed; 3) even in hostile envelopments caused by a frequency variation, a phase jump, amplitude sag/swell, harmonic distortion, and/or contaminated noise, good instant estimates of the phase, frequency, and amplitude can be obtained; 4) it is simple, but can exhibit high performance similar to the "robust PLL method." This paper presents the D-estimation method together with the design rules in detail; the usefulness of the method is v e r i f i e d b y p e r f o r m i n g e x t e n s i v e n u m e r i c a l experiments.
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We show that the Lyapunov spectrum of chaotic vector time series corrupted by noises with a noise-to-signal ratio of up to 100% in one of the coordinates can be estimated using the output of a noise reduction algorithm designed to...
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We show that the Lyapunov spectrum of chaotic vector time series corrupted by noises with a noise-to-signal ratio of up to 100% in one of the coordinates can be estimated using the output of a noise reduction algorithm designed to deal with noises of large amplitude.
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In this paper obtained the estimations of amplitude and harmonic signal phases of Gaussian noise of unknown power and linear-varying background with a priori indefinite parameters. Dispersion of amplitude and harmonic signal phase...
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In this paper obtained the estimations of amplitude and harmonic signal phases of Gaussian noise of unknown power and linear-varying background with a priori indefinite parameters. Dispersion of amplitude and harmonic signal phases estimations have been obtained.
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In this paper obtained the estimations of amplitude and harmonic signal phases of Gaussian noise of unknown power and linear-varying background with a priori indefinite parameters. Dispersion of amplitude and harmonic signal phase...
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In this paper obtained the estimations of amplitude and harmonic signal phases of Gaussian noise of unknown power and linear-varying background with a priori indefinite parameters. Dispersion of amplitude and harmonic signal phases estimations have been obtained.
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摘要 :
In this paper obtained the estimations of amplitude and harmonic signal phases of Gaussian noise of unknown power and linear-varying background with a priori indefinite parameters. Dispersion of amplitude and harmonic signal phase...
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In this paper obtained the estimations of amplitude and harmonic signal phases of Gaussian noise of unknown power and linear-varying background with a priori indefinite parameters. Dispersion of amplitude and harmonic signal phases estimations have been obtained.
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Summations of cumulants (projections) along parallel lines in the cumulant domain are formed. The projection procedure first relies on a change of variables through rotation of the cumulant axes. The cumulants are effectively deco...
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Summations of cumulants (projections) along parallel lines in the cumulant domain are formed. The projection procedure first relies on a change of variables through rotation of the cumulant axes. The cumulants are effectively decoupled. Summing the cumulants reduced the dimensionality of the system from six cumulant terms to one term. An eigenstructure-based method for frequency and biphase estimation is offered as an alternative to Fourier schemes on the slice after biphases have been converted to estimable amplitudes by forming symmetric and skew-symmetric projections.
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Wind turbine noise is known to be easily perceived even when the sound level is low. This may be concerned with amplitude modulation characteristics of wind turbine noise. Several studies have attempted to examine a relationship b...
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Wind turbine noise is known to be easily perceived even when the sound level is low. This may be concerned with amplitude modulation characteristics of wind turbine noise. Several studies have attempted to examine a relationship between amplitude modulation and human perception (or annoyance). However, there are only few studies on quantifying amplitude modulation of sound from wind turbine. In this paper, a practical method to measure amplitude modulation from recorded sound is proposed. This method is based on the assumptions as follows: (1) recorded sound signal has sinusoidal amplitude modulation, (2) blade passing frequency of a wind turbine is known, and (3) fluctuation of background noise is negligible. A double fast Fourier transform was employed to find the modulation depth at each frequency band. Laboratory tests were performed to identify a relationship between noise annoyance and modulation depth of wind turbine noise, which is calculated by the proposed method. The result clearly shows that there is a correlation between noise annoyance and amplitude modulation.
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Estimation of harmonic distortion from voltage source inverter is an important task for quality control of power grid. The distortion caused by load variation, noises, and switches are unavoidable. Most current results are more ha...
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Estimation of harmonic distortion from voltage source inverter is an important task for quality control of power grid. The distortion caused by load variation, noises, and switches are unavoidable. Most current results are more hardware-oriented methods and imposing difficulty and inflexibility for controlling the voltage source converter. We proposed an adaptive algorithm to estimate the amplitudes and phases of sinusoidal harmonics given the fundamental sampling frequency of the system. When the switching frequency was also unknown, we determined a time varying updating parameter by algebraically solving a minimal of performance index based on the estimates. The algorithm was verified with signals from a real voltage converter. Once such estimations are obtained, we can reconstruct a working model of voltage inverters by combining the average model and estimated harmonics computationally and thus significantly simplify the modeling and simulation of inverters, which will further facilitate the control design for voltage converter.
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Amplitude demodulation is an ill-posed problem and so it is natural to treat it from a Bayesian viewpoint, inferring the most likely carrier and envelope under probabilistic constraints. One such treatment is Probabilistic Amplitu...
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Amplitude demodulation is an ill-posed problem and so it is natural to treat it from a Bayesian viewpoint, inferring the most likely carrier and envelope under probabilistic constraints. One such treatment is Probabilistic Amplitude Demodulation (PAD), which, whilst computationally more intensive than traditional approaches, offers several advantages. Here we provide methods for estimating the uncertainty in the PAD-derived envelopes and carriers, and for learning free parameters like the time-scale of the envelope. We show how the probabilistic approach can naturally handle noisy and missing data. Finally, we indicate how to extend the model to signals which contain multiple modulators and carriers.
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摘要 :
Amplitude demodulation is an ill-posed problem and so it is natural to treat it from a Bayesian viewpoint, inferring the most likely carrier and envelope under probabilistic constraints. One such treatment is Probabilistic Amplitu...
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Amplitude demodulation is an ill-posed problem and so it is natural to treat it from a Bayesian viewpoint, inferring the most likely carrier and envelope under probabilistic constraints. One such treatment is Probabilistic Amplitude Demodulation (PAD), which, whilst computationally more intensive than traditional approaches, offers several advantages. Here we provide methods for estimating the uncertainty in the PAD-derived envelopes and carriers, and for learning free parameters like the time-scale of the envelope. We show how the probabilistic approach can naturally handle noisy and missing data. Finally, we indicate how to extend the model to signals which contain multiple modulators and carriers.
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