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This paper presents an isolated DC-DC converter with high voltage gain that features an advanced inter-built nanocrystalline-core medium-frequency transformer (NC-MFT). The isolated DC-DC converter with an NC-MFT is specifically designed for applications such as interconnect photovoltaic (PV) systems, DC microgrids, DC loads, and DC buses, where voltage gain is one of the essential issues to consider. The NC-MFT inside the DC-DC converter is designed with a new approach that not only provides isolation but also contributes to achieving high efficiency and a higher step-up ratio. The high efficiency of the converters contributes to the integration of PV systems into DC microgrids. The converter yields a high voltage conversion ratio of 16.17. The experimental results obtained at 41.8 V/676 V and 275 W for the prototype revealed high efficiency (95.63% at full load). The experimental results validate the theoretical analysis and simulation, confirming that the converter achieves the main objective of high voltage conversion and high efficiency. These results will contribute to the interest in the use of this type of energy and its impact on the reduction in CO2 emissions.

This paper presents a novel methodology for detecting faults in wind turbine blades using computational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying fault-related patterns. The second model leverages clustering and achieves superior performance in terms of precision and data segmentation. The results indicate that clustering may better capture the underlying data characteristics compared to supervised methods. The proposed methodology offers a new approach to early fault detection in wind turbine blades, highlighting the potential of integrating different computational learning techniques to enhance system reliability. The use of accessible tools like Orange Data Mining underscores the practical application of these advanced solutions within the wind energy sector. Future work will focus on combining these methods to improve detection accuracy further and extend the application of these techniques to other critical components in energy infrastructure.

Fires in photovoltaic (PV) electrical systems are a real and serious problem because this phenomenon can have severe consequences for the safety of people and the environment. In some cases, fires result from a lack of maintenance or improper installation of PV modules. It is essential to consider prevention and continuous monitoring of the electrical parameters to minimize these risks, as these factors increase the temperature of the photovoltaic modules. The use of thermal analysis techniques can prevent hotspots and fires in photovoltaic systems; these techniques allow detecting and correcting problems in the installation, such as shadows, dirt, and poor-quality connections in PVs. This paper presents a case study of the implementation of thermal analysis in an installation of photovoltaic modules connected to a solar pumping system to identify the formation of hotspots through thermal images using an unmanned aerial vehicle (UAV). Here, a novel methodology is proposed based on the comparison of temperature increases concerning the values of short circuit current, open circuit voltage, and real efficiency of each PV module. In addition, an electrical safety methodology is proposed to design a photovoltaic system that prevents fires caused by hotspots, contemplating critical parameters such as photovoltaic power, number of photovoltaic modules, DC:AC conversion ratio, electrical conductor selection, control devices, and electrical protection; the performance power expected was obtained using standard power test conditions, including irradiance factor, photovoltaic module (PVM) temperature factor, and power reduction factor.

In Mexico, the economic and industrial development is in the center and north; this represents more than 50% of the country’s total consumption. Data on population and energy consumption will be obtained from the following sources: the National Institute of Geography and Statistics (INEGI), and the Energy Information System. Regarding meteorological data, two databases are used: the Automatic Weather Stations (AWS) (for solar irradiance data) and the MERRA-2 reanalysis data (for wind data). These data will be analyzed for use in a geographic information system (GIS) using kriging interpolation to create maps of solar and wind energy. The area studied includes the following states: Mexico City, Puebla, State of Mexico, Hidalgo, Morelos, Zacatecas, Queretaro, San Luis Potosi, Guanajuato, Aguascalientes and Tlaxcala. The results showed that the areas with the highest solar potential are Hidalgo, Estado de México, Morelos, northern Puebla, southern Queretaro, northwestern Guanajuato, and northern Zacatecas, with 5.89 kWh/m2/day, and the months with the highest solar potential are March, April, May, and June. Regarding wind potential, the maximum wind power density is in Puebla, with 517 W/m2, and the windy season in central Mexico spans June, July, August, September, October, and November.

Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

This article presents the analysis of the performance of a flexible wind turbine blade. The simulation analysis is based on a 3 m span blade prototype. The blade has a flexible surface and a cam mechanism that modifies the aerodynamic profile and adapts the surface to different configurations. The blade surface was built with a flexible fiberglass composite, and the internal mechanism consists of a flexible structure actuated with an eccentric cam. The cam mechanism deforms five sections of the blade, and the airfoil geometry for each section was measured from zero cam displacement to full cam displacement. The measured data were interpolated to obtain the aerodynamic profiles of the five sections to model the flexible blade in the simulation process. The simulation analysis consisted of determining the different aerodynamic coefficients for different deformed surfaces and a range of wind speeds. The aerodynamic coefficients were calculated with the BEM method (QBlade®); as a result, the data performance of the flexible blade was compared for the different deformation configurations. Finally, a decrease of up to approximately 6% in the mean bending moment suggests that the flexible turbine rotor presented in this article can be used to reduce extreme and fatigue loads on wind turbines.

This paper presents the protection of the rotor side converter in a grid-connected doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) during a symmetrical voltage dip. In order to manage rotor current and regulate DC link voltage, an efficient active crowbar protection circuit is implemented in the rotor side converter (RSC). To improve the low-voltage ride-through capability for grid-connected wind turbine systems, an integrated DFIG-based WECS role is crucial because wind turbines must remain connected to the utility grid during faults to ensure continuity and reliability of power supply. This paper aims to design and implement an efficient active crowbar protection technique to protect the RSC and avoid excessive rotor current during a symmetrical voltage dip. Therefore, the efficient active crowbar protection circuit is designed using MATLAB-Simulink software, and its performance is validated using a real-time (RT) simulator. Finally, the existing methods are compared with the proposed work outcomes, and a conclusion is made. [ABSTRACT FROM AUTHOR]

The forecast of wind speed and the power produced from wind farms has been a challenge for a long time and continues to be so. This work introduces a method that we label as Wavelet Decomposition-Neural Networks (WDNN) that combines wavelet decomposition principles and deep learning. By merging the strengths of signal processing and machine learning, this approach aims to address the aforementioned challenge. Treating wind speed and power as signals, the wavelet decomposition part of the model transforms these inputs, as appropriate, into a set of features that the neural network part of the model can ingest to output accurate forecasts. WDNN is unconstrained by the shape, layout, or number of turbines in a wind farm. We test our WDNN methods using three large datasets, with multiple years of data and hundreds of turbines, and compare it against other state-of-the-art methods. It's very short-term forecast, like 1-h ahead, can outperform some deep learning models by as much as 30%. This shows that wavelet decomposition and neural network are a potent combination for advancing the quality of short-term wind forecasting. [ABSTRACT FROM AUTHOR]