Parameter estimation of harmonics-polluted single-phase grid voltage signal

Pay, Miao Lin; Ahmed, Hafiz

doi:10.1007/s00202-020-00956-1

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…To find an optimal window for our problem, we need to find the window parameter a 0 for which (11) is maximised. This represents a compromise between the process gain, the peak sidelobe level and the equivalent noise bandwidth.…”

Section: Windowingmentioning

confidence: 99%

“…Estimating, e.g. the directions of arrival, frequencies, Doppler shifts, phase and more is required in a wide range of applications including radar [7], sonar [8], channel sounding [9], wireless communications [10] and power electronics [11]. Therefore, different methods can be used to estimate the harmonics parameter such as the discrete Fourier transform (DFT) [12], [13], phase lock loop [14] and more recent techniques such as ESPRIT [15] and MUSIC [16].…”

Section: Introductionmentioning

confidence: 99%

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Phase Estimation of Single Tones Next to Modulated Signals in the Medium Frequency R-Mode System

et al. 2022

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Position, navigation, and timing information are critical to today's infrastructures; as a result, the possibility of estimating ranges is being explored in more and more radio systems. One way to achieve this is to extend the modulation with time-synchronised aiding carriers and to estimate their phase at the receiver side. In this paper, we present two ways to minimise the negative influence of the modulation on the phase estimation. We show that the classical maximum likelihood estimator is still an efficient estimator for our problem, using a medium-frequency R-Mode signal as an example, and is therefore used in receiver designs. We then describe two possible ways to precondition the signal to increase the accuracy for short observations. As a first approach, we describe how window functions can positively change the signal-tonoise ratio for our estimation. As a second approach, we show the use of a narrowband bandpass filter. Finally, we show that these approaches, applied to real measurements, improve the variance of the estimate by up to two orders of magnitude.

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Section: Windowingmentioning

confidence: 99%