Person: Zivanovic, Miroslav
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Zivanovic
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Miroslav
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Ingeniería Eléctrica, Electrónica y de Comunicación
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ISC. Institute of Smart Cities
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0000-0001-8729-6657
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1948
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Publication Open Access Data-driven generation of synthetic wind speeds: a comparative study(Wiley, 2022) D'Ambrosio, Daniel; Schoukens, Johan; Troyer, T. De; Zivanovic, Miroslav; Runacres, Mark; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio IngeniaritzarenThe increasing sophistication of wind turbine design and control generates a need for high-quality wind data. The relatively limited set of available measured wind data may be extended with computer generated data, for example, to make reliable statistical studies of energy production and mechanical loads. Here, a data-driven model for the generation of surrogate wind speeds is compared with two state-of-the-art time series models that can capture the probability distribution and the autocorrelation of the target wind data. The proposed model, based on the phase-randomised Fourier transform, can generate wind speed time series that possess the power spectral density of the target data and converge to their generally non-Gaussian probability distribution with an arbitrary, user-defined precision. The model performance is benchmarked in terms of probability distribution, power spectral density, autocorrelation, and nonstationarities such as the diurnal and seasonal variations of the target data. Comparisons show that the proposed model can outperform the selected models in reproducing the statistical descriptors of the input datasets and is able to capture the nonstationary diurnal and seasonal variations of the wind speed.Publication Open Access Unified analysis of glottal source spectrum(ISCA, 2003) Arroabarren Alemán, Ixone; Zivanovic, Miroslav; Carlosena García, Alfonso; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta ElektronikoaThe spectral study of the glottal excitation has traditionally been based on a single time-domain mathematical model of the signal, and the spectral dependence on its time domain parameters. Opposite to this approach, in this work the two most widely used time domain models have been studied jointly, namely the KLGLOTT88 and the LF models. Their spectra are analyzed in terms of their dependence on the general glottal source parameters: Open quotient, asymmetry coefficient and spectral tilt. As a result, it has been proved that even though the mathematical expressions for both models are quite different, they can be made to converge. The main difference found is that in the KLGLOTT88 model the asymmetry coefficient is not independent of the open quotient and the spectral tilt. Once this relationship has been identified and translated to LF model, both models are shown to be equivalent in both time and frequency domains.Publication Open Access A decomposition approach to cyclostratigraphic signal processing(Elsevier, 2022) Wouters, Sebastien; Crucifix, Michel; Sinnesael, Matthias; Silva, Anne-Christine da; Zeeden, Christian; Zivanovic, Miroslav; Boulvain, Frédéric; Devleeschouwer, Xabier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio IngeniaritzarenSedimentary rocks can record signals produced byhighly complex processes. These signals are generated by a progressive deposition of sediments which can be affected, mainly through the climate system, by regular astronomical cycles (i.e. Milankovitch cycles), and by irregular oscillations like the El Niño-Southern Oscillation. Also, usually through biological, chemical and/or physical post-depositional processes,thesedimentary records can be affected by pattern-creating heterogeneous processes. The noise in the signals further complicates the records,and the deposition rate (or sedimentation rate) can fluctuate, which greatly reduces the effectiveness of the classical stationary time-series analysis methods commonly used in cyclostratigraphy (i.e.the study of the cycles found inthe sedimentary records). Faced with this multiplicity of processes, a common approach used in cyclostratigraphy is to reduce each signal to more manageable sub-signals, either over a given range of frequencies (e.g.,by filtering), or by considering a continuum of constant frequencies (e.g.,using transforms). This makes it possible to focus on the features of interest, commonly astronomical cycles. However, working with sub-signals is not trivial. Firstly, sub-signals have a certain amount of cross-cancellation when they are summed back to reconstructthe initial signal. This means that in filters and in transforms, wiggles that are not present in the initial signal can appear in the sub-signals. Secondly, the sub-signals considered often cannot be summed to reconstruct the initial signal: this means that there are processes affecting the signal which remain unstudied. It is possible to takecross-cancellation into accountand to consider the entire content of a signal by dividing the signal into a decomposition: a set of sub-signals that can be added back together to reconstruct the original signal. We discusshere how to reframe commonly used time-series analysis techniques in the context of decomposition, how they are affected by cross-cancellation, and how adequate they are for comprehending the whole signals. We also show that decomposition can be carried out by non-stationary time-series methods, which can mini misecross-cancellation, and have now reached sufficient maturity to tackle sedimentary records signals. We present novel tools to adapt non-stationary decomposition for cyclostratigraphic purposes, based on the concepts of Empirical Mode Decomposition (EMD) and Instantaneous Frequency (IF), mainly: (1) afast Ensemble Empirical Mode Decomposition (EEMD) algorithm, (2) quality metrics for decomposition, and (3) plots to visualise instantaneous frequency, amplitude and frequency ratio. We illustrate the use of these tolos by applying them on a grey scale signal from the site of the Ocean Drilling Program, at Ceara Rise (western equatorial Atlantic), especially to identifyand characterisethe expression of astronomical cycles. The main goal is to show that by minimising cross-cancellation, we can apply in real signalswhat we call the wiggle-in-signal approach: making the sub-signals in the decomposition more representative of the expression, wiggle by wiggle, of all the processes affecting the signal(e.g., astronomical cycles). We finally argue that decomposition could be used as a practical standard output for time-series analysis interpretation of cyclostratigraphic signals.Publication Open Access Two methods for nonparametric spectrum peak discrimination(2002) Zivanovic, Miroslav; Carlosena García, Alfonso; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta ElektronikoaThe conventional DFT-oriented nonparametric interpolation methods, based on time windowing and the DTFT envelope curve resampling (zero padding, Chirp-z [1], frequency scale distortion [2], etc.), can improve the spectrum computational resolution. Two methods proposed herein involve some modification of the frequency domain representation and apparently improve the spectrum physical resolution.Publication Open Access Synthetic wind speed generation for the simulation of realistic diurnal cycles(IOP Publishing, 2020) D’Ambrosio, D.; Schoukens, Johan; Troyer, T. De; Zivanovic, Miroslav; Runacres, Mark; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio IngeniaritzarenSynthetic wind-speed generators can provide a detailed characterisation of the wind variability at different time scales. A keen interest in the availability of synthetic wind speeds has recently risen in wind power modelling applications. In particular, a proper simulation of the diurnal and annual variability of the wind speed is sought that can lead to a more efficient grid integration of this renewable source. This paper proposes a statistical model for generating synthetic wind speeds consistent with both the probability density function and the spectral density function of a measured wind-speed dataset and that simulates accurately its average diurnal variation. To test the proposed methodology, multiple synthetic time series are generated using three long-term wind-speed time series recorded at a meteorological site in the Netherlands. The accuracy in terms of the statistical descriptors of the generated time series and their average diurnal variation is assessed with respect to the target data. We show that the average diurnal cycles present in all the three measured time series are always reproduced accurately, and that the statistical descriptors of the target dataset are constantly matched with high accuracy. Possible advantages of the present approach in terms of power system modelling are discussed.Publication Open Access Astronomical component estimation (ACE v.1) by time-variant sinusoidal modeling(Copernicus Publications, 2016) Sinnesael, Matthias; Zivanovic, Miroslav; Vleeschouwer, David De; Claeys, Philippe; Schoukens, Johan; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta ElektronikoaAccurately deciphering periodic variations in paleoclimate proxy signals is essential for cyclostratigraphy. Classical spectral analysis often relies on methods based on (fast) Fourier transformation. This technique has no unique solution separating variations in amplitude and frequency. This characteristic can make it difficult to correctly interpret a proxy's power spectrum or to accurately evaluate simultaneous changes in amplitude and frequency in evolutionary analyses. This drawback is circumvented by using a polynomial approach to estimate instantaneous amplitude and frequency in orbital components. This approach was proven useful to characterize audio signals (music and speech), which are non-stationary in nature. Paleoclimate proxy signals and audio signals share similar dynamics; the only difference is the frequency relationship between the different components. A harmonic-frequency relationship exists in audio signals, whereas this relation is non-harmonic in paleoclimate signals. However, this difference is irrelevant for the problem of separating simultaneous changes in amplitude and frequency. Using an approach with overlapping analysis frames, the model (Astronomical Component Estimation, version 1: ACE v.1) captures time variations of an orbital component by modulating a stationary sinusoid centered at its mean frequency, with a single polynomial. Hence, the parameters that determine the model are the mean frequency of the orbital component and the polynomial coefficients. The first parameter depends on geologic interpretations, whereas the latter are estimated by means of linear least-squares. As output, the model provides the orbital component waveform, either in the depth or time domain. Uncertainty analyses of the model estimates are performed using Monte Carlo simulations. Furthermore, it allows for a unique decomposition of the signal into its instantaneous amplitude and frequency. Frequency modulation patterns reconstruct changes in accumulation rate, whereas amplitude modulation identifies eccentricity-modulated precession. The functioning of the time-variant sinusoidal model is illustrated and validated using a synthetic insolation signal. The new modeling approach is tested on two case studies: (1) a Pliocene–Pleistocene benthic δ18O record from Ocean Drilling Program (ODP) Site 846 and (2) a Danian magnetic susceptibility record from the Contessa Highway section, Gubbio, Italy.Publication Open Access Instantaneous amplitude and phase signal modeling for harmonic removal in wind turbines(Elsevier, 2023) Zivanovic, Miroslav; Plaza Puértolas, Aitor; Iriarte Goñi, Xabier; Carlosena García, Alfonso; Ingeniería; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Gobierno de Navarra / Nafarroako Gobernua, 0011-1365-2021-000159.We present a novel approach to harmonic disturbance removal in single-channel wind turbine acceleration data by means of time-variant signal modeling. Harmonics are conceived as a set of quasi-stationary sinusoids whose instantaneous amplitude and phase vary slowly and continuously in a short-time analysis frame. These non-stationarities in the harmonics are modeled by low-degree time polynomials whose coefficients capture the instantaneous dynamics of the corresponding waveforms. The model is linear-in-parameters and is straightforwardly estimated by the linear least-squares algorithm. Estimates from contiguous analysis frames are further combined in the overlap-add fashion in order to yield overall harmonic disturbance waveform and its removal from the data. The algorithm performance analysis, in terms of input parameter sensitivity and comparison against three state-of-the-art methods, has been carried out with synthetic signals. Further model validation has been accomplished through real-world signals and stabilization diagrams, which are a standard tool for determining modal parameters in many time-domain modal identification algorithms. The results show that the proposed method exhibits a robust performance particularly when only the average rotational speed is known, as is often the case for stand-alone sensors which typically carry out data pre-processing for structural health monitoring. Moreover, for real-world analysis scenarios, we show that the proposed method delivers consistent vibration mode parameter estimates, which can straightforwardly be used for structural health monitoring.