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Zivanovic, Miroslav

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https://academica-e.unavarra.es/handle/2454/50204

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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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Author: Carlosena García, Alfonso × Subject: Operational Modal Analysis (OMA) ×

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    PublicationOpen 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.