Reconfiguration of PV arrays is one of the most suitable options to face issues affecting the power produced by panels, such as partial shading. This paper presents a reconfiguration procedure based on a genetic algorithm. The execution times obtained with the proposed approach validate its good performance compared with a traditional brute force algorithm. Finally, different shading patterns are considered in the simulations.

This article presents a method for the Maximum Power Point Tracking (MPPT) of a Photovoltaic (PV) panels array with partial shading, applying an Improved Pattern Search Method (IPSM). The method is simulated in PSIM ${}^{@}$ and then implemented in hardware in the loop system, emulating the PV array on an industrial computer (Speedgoat) that allows real-time emulations and the IPSM is applied in an Arduino DUE. The experiments were carried out with TP245S-20/WD, KYOCERA KC200GT, YINGLY SOLAR JS65, and MSX60 photovoltaic panels. The results are the proper MPPT with changes in partial shading over time, inducing the increase and decrease of the maximum power point. The results obtained are the search for the global maximum power point in a matrix of panels in which, due to partial shading, it might have several local maximum power points, and thanks to the IPSM algorithm, it always manages to find the global maximum power point. Finally, the results are compared with other methods where it was found that IPSM had faster answers.

Maize crops occupy an important place in world food security. However, different conditions, such as abiotic stress factors, can affect the productivity of these crops, requiring technologies that facilitate their monitoring. One such technology is spectroscopy, which measures the energy reflected and emitted by a surface along the electromagnetic spectrum. Spectral data can help to identify abiotic factors in plants, since the spectral signature of vegetation has discriminating features associated with the plant’s health condition. This paper introduces a spectral library captured on maize crops under different nitrogen-deficiency stress levels. The datasets will be of potential interest to researchers, ecologists, and agronomists seeking to understand the spectral features of maize under nitrogen-deficiency stress. The library includes three datasets captured at different growth stages of 10 tropical maize genotypes. The spectral signatures collected were in the visible to near-infrared range (450–950 nm). The data were pre-processed to reduce noise and anomalous signatures. This study presents a spectral library of the effects of nitrogen deficiency on ten maize genotypes, highlighting that some genotypes show tolerance to this type of stress at different phenological stages. Most of the evaluated genotypes showed discriminate spectral features 4–6 weeks after sowing. Higher reflectance was obtained at approximately 550 nm for the lowest nitrogen fertilization treatments. Finally, we describe some potential applications of the spectral library of maize leaves under nitrogen-deficiency stress.

This paper compares the performance of three electrical models (the single diode model, the Bishop model, and the Direct–Reverse model) in representing photovoltaic cells. Such comparison is performed in both the first quadrant (positive cell voltage and current—$Q_1$) and the second quadrant (negative cell voltage and positive cell current—$Q_2$). The analysis conducted here is based on the I–-V curves of a PV cell obtained experimentally. The parameters of each model are estimated using a Genetic Algorithm. The root mean square error and the mean absolute percentage error are computed to validate the estimation stage. Likewise, the behavior of each parameter of the models is analyzed by calculating their mean and standard deviation. Some places of interest on the I–V curve, such as the short–circuit point, the open–circuit point, and the maximum power point, are also estimated and compared.

The development and optimization of renewable energy systems are some of the most necessary topics to advance towards secure and sustainable energy models. Photovoltaic energy is one of those sustainable options that could contribute to the reduction of greenhouse gas emissions. The optimal angle of solar incidence producing the highest absorption in a day is an important parameter to install photovoltaic systems. This value is often estimated using simulation models based on geographic location; however, those models ignore the influence of nearby obstruction objects, albedo, and local weather conditions. Such a problem is addressed in this work by designing a system to estimate the optimum angle of solar incidence for the photovoltaic panels. The system is based on an arrangement of 33 measurement points spaced in arcs every 45 degrees in azimuth and every 22.5 degrees in elevation, which provides a wide range for analysis. The light captured by each optical fiber is transmitted to a flat array where the power is measured using a single RGB camera.