Open Access
Ultra-low frequency multidirectional harvester for wind turbines
(Elsevier, 2023) Castellano Aldave, Jesús Carlos; Carlosena García, Alfonso; Iriarte Goñi, Xabier; Plaza Puértolas, Aitor; Ingeniería; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
In this paper we propose, and demonstrate through a prototype, a completely novel device able to harvest mechanical energy from the multidirectional vibrations in a wind turbine, and convert it into electrical, to power autonomous sensors. The application is very challenging since vibrations are of ultra-low frequency, well below 1 Hz, with accelerations of tenths of cm/s2 (0.01 g), and the device must capture energy from the movement in any direction. According to our experiments, the device is capable to generate average powers around the milliwatt in the operation conditions of a wind turbine, which are enough for some very-low power sensor nodes, or at least to considerably extend the life-time of batteries. The device is based on the principle of moving (inertial) masses comprised of magnets in Hallbach arrays interacting with coils, and can work for movements on any direction of a plane. To the best of our knowledge, this is the first device specifically proposed for wind turbines and one of the few that work in such low frequencies, and capture energy from movements on any direction on a plane. Only three harvesters proposed in the literature, intended for distinct applications, can work at such low frequencies, and our device exhibits a better efficiency. Though comparisons with harvesters working in different contexts and, even using different conversion principles, is not completely fair, we make in this paper a comparison to the closest ones, resorting to two different figures of merit.
Open Access
Dataset for the identification of a ultra-low frequency multidirectional energy harvester for wind turbines
(Elsevier, 2024-11-20) Bacaicoa Díaz, Julen; Hualde Otamendi, Mikel; Merino Olagüe, Mikel; Plaza Puértolas, Aitor; Iriarte Goñi, Xabier; Castellano Aldave, Jesús Carlos; Carlosena García, Alfonso; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Ingeniería; Ingeniaritza; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
This paper presents a publicly available dataset designed to support the identification (characterization) and performance optimization of an ultra-low-frequency multidirectional vibration energy harvester. The dataset includes detailed measurements from experiments performed to fully characterize its dynamic behaviour. The experimental data encompasses both input (acceleration)-output (energy) relationships, as well as internal system dynamics, measured using a synchronized image processing and signal acquisition system. In addition to the raw input-output data, the dataset also provides post-processed information, such as the angular positions of the moving masses, their velocities and accelerations, derived from recorded high-speed videos at 240 Hz. The dataset also includes the measured power output generated in the coils. This dataset is intended to enable further research on vibration energy harvesters by providing experimental data for identification, model validation, and performance optimization, particularly in the context of energy harvesting in low-frequency and multidirectional environments, such as those encountered in wind turbines.
Open Access
Low-frequency electromagnetic harvester for wind turbine vibrations
(Elsevier, 2024) Castellano Aldave, Jesús Carlos; Plaza Puértolas, Aitor; Iriarte Goñi, Xabier; Carlosena García, Alfonso; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC; Ingeniería; Ingeniaritza; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
In this paper we describe and fully characterize a novel vibration harvester intended to harness energy from the vibration of a wind turbine (WT), to potentially supply power to sensing nodes oriented to structural health monitoring (SHM). The harvester is based on electromagnetic conversion (EM) and can work with vibrations of ultra-low frequencies in any direction of a plane. The harvester bases on a first prototype already disclosed by the authors, but in this paper, we develop an accurate model parameterized by a combination of physical parameters and others related to the geometry of the device. The model allows predicting not only the power generation capabilities, but also the kinematic behaviour of the harvester. Model parameters are estimated by an identification procedure and validated experimentally. Last, the harvester is tested in real conditions on a wind turbine.
Embargo
Modal frequency and damping estimation of wind turbines: analysis of a wind farm
(Springer, 2024-06-22) Legaz Catena, Asier; Zivanovic, Miroslav; Iriarte Goñi, Xabier; Plaza Puértolas, Aitor; Carlosena García, Alfonso; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC; Ingeniería; Ingeniaritza
In this paper, we present an in-depth analysis carried out on several units of the same Wind Turbine (WT) model installed in a wind farm. We have collected simultaneous data under several different operating conditions ranging from the idling state to nominal power close to cut-out. Both frequency and damping parameters have been estimated for the first and second Fore-Aft (FA) and Side-Side (SS) tower modes. As far as we know, there are no previous publications combining data from so many turbines, operating conditions, and for a time period spanning several months. We have made use of a novel strategy to isolate the modes and minimize the influence of harmonics, using an algorithm previously proposed by the authors. The main conclusion is that estimated modal frequencies allow for a clear discrimination between turbines, whereas damping ratios, subjected to much wider deviations, do not seem to be very discriminant. We show here results for only one operating mode (nominal power), for which the method has been tuned. The analysis of other operating modes and longer periods, now under consideration, will allow for more conclusive results.
Open Access
Comprehensive characterisation of a low-frequency-vibration energy harvester
(MDPI, 2024) Plaza Puértolas, Aitor; Iriarte Goñi, Xabier; Castellano Aldave, Jesús Carlos; Carlosena García, Alfonso; Ingeniería; Ingeniaritza; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
In this paper, we describe a measurement procedure to fully characterise a novel vibration energy harvester operating in the ultra-low-frequency range. The procedure, which is more thorough than those usually found in the literature, comprises three main stages: modelling, experimental characterisation and parameter identification. Modelling is accomplished in two alternative ways, a physical model (white box) and a mixed one (black box), which model the magnetic interaction via Fourier series. The experimental measurements include not only the input (acceleration)–output (energy) response but also the (internal) dynamic behaviour of the system, making use of a synchronised image processing and signal acquisition system. The identification procedure, based on maximum likelihood, estimates all the relevant parameters to characterise the system to simulate its behaviour and helps to optimise its performance. While the method is custom-designed for a particular harvester, the comprehensive approach and most of its procedures can be applied to similar harvesters.
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.
Open Access
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 Gobernua
We 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.