BUSCADOR RECOLECTA

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., This work has been supported by the Spanish Research Agency, and the EU/PTR Next Generation Funds under grants PID2019-107258RB-C32 and TED2021-131052B-C21, and also by the Government of Spain (Plan Estatal de Investigación Científica, Técnica y de Innovación 2021–2023) under grants PID2022-138491OB-C32 and PDC 2023-145876-C22.

There is a growing demand for computer-generated realistic high-fidelity wind speed data for various applications in the wind industry. Such data should capture the non-stationary dynamics of real-world wind time series, as well as be consistent with the statistical descriptors - the probability density function and power spectral density - of the observed wind speed. However, complying with the statistical descriptors is not a guarantee that the seasonality will be correctly reproduced in synthetic data. The seasonality, characterized by the average diurnal and seasonal variations, is driven by the periodicities embedded in diurnal and annual harmonic series respectively. Those periodicities are determined by the long-term orbital forcing components, which establish the insolation for a given latitude and longitude. We show that average diurnal and seasonal variations can be visualized as the output of comb filters, whose fundamental frequencies match the diurnal and annual fundamental frequency respectively. The aforementioned theoretical findings are readily reproduced in synthetic wind speed, generated by a non-parametric data-driven statistical model, based on the phase-randomized Fourier transform. The model, tested on both 10-min and 1-min resolution real-world datasets, yields average non-stationarities in synthetic wind speed with the accuracy close to the computing precision., Funding text 1: This work has been supported by the Spanish Research Agency, and the EU/PTR Next Generation Funds under grants TED2021-131052B-C21, and PID2022-138491OB-C32.; Funding text 2: This work has been supported by the Spanish Research Agency, and the EU/PTR Next Generation Funds under grants TED2021-131052B-C21, and PID2022-138491OB-C32.

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., This paper has been supported by the Spanish Research Agency, and
the EU/PTR Next Generation Funds under grants PID2019-107258RBC32,
TED2021-131052B-C21, and PID2022-138491OB-C32. The Public
University of Navarre has also partially supported this project under
grant: PJUPNA2024-11690.

In this letter, a novel low-voltage junction field-effect transistor (JFET) oscillator with self-starting capability to implement an ac-dc boost converter is introduced. The circuit is transformer free and can operate with very low-voltage and low-frequency signals. In order to operate with positive and negative input signals, a coupled topology of JFETs has been used. The circuit has been built using off the shelf components, and can be used with electromagnetic harvesters, thermoelectric modules, and/or wearable devices. Experimental results with a practical harvester are provided in order to demonstrate the proposed ac-dc boost converter., This work was supported by Spanish Research Agency and the EU/PTR Next Generation Funds, under Grant TED2021-131052B-C21, Grant PID2019-107258RB-C32, and Grant PID2022-138491OB-C32.

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., This paper has been supported by the Spanish Research Agency, and the EU/PTR Next Generation Funds under grants TED2021-131052B-C21, and PID2022-138491OB-C32.

Scaling up CO2 electroreduction to formate faces several challenges, including using chemicals as electrolytes and high energy demands. To address these issues, this study uses an industrial stream—specifically a caustic soda stream from the textile industry—as anolytes for the oxygen evolution reaction (OER). Using this approach, formate concentrations of 226 g L⁻¹ and Faradaic efficiencies (FE) of 53 % are achieved at 200 mA cm⁻², demonstrating the competitiveness of industrial streams compared to synthetic anolyte solutions. Various anode materials are tested to optimize OER kinetics under industrial conditions and reduce energy consumption. Ni foam exhibited promising results, achieving FEs of 78 % and 58 % at 90 and 200 mA cm⁻², with energy consumption between 236 and 385 kWh kmol⁻¹ , making it one of the most efficient options among commercially available materials. In addition, alternative materials, such as NiFeOx and NiZnFeOx particulate anodes, are synthesized to provide viable substitutes for commercial anodes that rely on scarce elements. These alternatives demonstrated similar formate concentrations, with FEs up to 74 % and reduced energy requirements compared to commercial NiO. The synthesized NiFe foam anode excelled in performance, with energy consumption below 210 and 380 kWh kmol⁻¹ and an impressive formate production of 255 g L−1 of formate achieving a 60 % FE at 200 mA cm−2. Overall, this research demonstrates the feasibility of CO₂ electroreduction to formate using textile effluents under relevant conditions, representing a significant step toward making this process a competitive option for decarbonizing hard-to-abate industries., The authors gratefully acknowledge Grant TED2021-129810B-C21 and PLEC2022-009398 funded by MICIU/AEI/10.13039/501100011033/ and by the “European Union NextGenerationEU/PRTR”, and Grants PID2022-138491OB-C31, and PID2022-138491OB-C32, funded by MICIU/AEI/10.13039/501100011033 and by “ERDF/EU”. The present work is related to CAPTUS Project. This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101118265. J. A. Abarca gratefully acknowledges the predoctoral research grant (FPI) PRE2021-097200.