Plaza Puértolas, Aitor
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Plaza Puértolas
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Publication Embargo Main shaft instantaneous azimuth estimation for wind turbines(Elsevier, 2025-02-20) Zivanovic, Miroslav; Vilella San Martín, Iñigo; Iriarte Goñi, Xabier; Plaza Puértolas, Aitor; Gainza González, Gorka; Carlosena García, Alfonso; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Ingeniería; Ingeniaritza; Institute of Smart Cities - ISC; Gobierno de Navarra / Nafarroako GobernuaWe present a novel approach to estimating the instantaneous main shaft angular position in the context of wind turbine structural health monitoring. We show that only two IMU channels - gyroscope axial and accelerometer tangential - contain enough information to build an acceleration state-space model that properly captures the rotational dynamics of a wind turbine. The kernel of the model is an in-phase and quadrature time-varying sinusoid whose argument is driven by the integral of the gyroscope output. This approach clearly stands in contrast to most state-of-the-art methods, where the gyroscope output is explicitly modeled. The model equation describes the states dynamics, which simultaneously assesses the instantaneous amplitude and initial phase of the angular displacement through a first-order autoregressive process. Such a state-space model features only two states per time instant; furthermore, it is linear-in-states and therefore straightforwardly estimated by the linear Kalman filter. It is shown that the instantaneous azimuth estimates obtained from the state-space model, linearly combined with the gyroscope output, effectively cancel out the long-term drift in the estimate. The benchmark results suggest that the proposed method outperforms a state-of-the-art method, in terms of robustness against noise and adaptability to changing operating regimes in a wind turbine.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.