Code and data from an ADALINE network trained with the RTRL and LMS algorithms for an MPPT controller in a photovoltaic system.

This paper presents a detailed description of the data obtained as a result of the computational simulations and experimental tests of an MPPT controller based on an ADALINE artificial neural network with FIR architecture, trained with the RTRL and LMS algorithms that were used as mechanisms of control in an off-grid photovoltaic system. In addition to the data obtained with the neural control method, the data for the MPPT controller based on the traditional Perturb and Observe (P&O) algorithm are presented. The simulations were performed in MATLAB/Simulink software without using the Neural Network Toolbox for controller training. The experimental tests were performed in an open space without shaded areas, exposing the neurocontroller to varying environmental conditions. Additionally, the scripts developed in MATLAB for the neural training algorithms used in the simulations are presented. These computational simulations were structured in five test cases to represent the behavior of each controller under varying environmental conditions. The codes developed in C are part of the implementation of the MPPT neurocontroller in the PIC18F2550, from which the experimental data were obtained. The data and codes presented in this research are available in the Mendeley Data repository, which allows evaluating the performance and optimizing the training algorithms with the purpose of improving the control methods applied to photovoltaic systems.

Data sets of predicted stable and meta-stable crystalline phase structural of NB-N system under pressure.

This document presents a dataset on various stoichiometric Niobium nitrides compounds under different pressures, which have been identified by first-principles calculations in combination with an evolutionary algorithm methodology implemented in the USPEX code in its variable-composition mode. The feature of this methodology is to find the ground state or metastable structures with only the knowledge of chemical composition at given pressure conditions and predict through all possible structures, not relying on any prior known structural information. We have successfully predicted the crystal structures and phase transitions of NbN at pressures up to 100 GPa. Because the Niobium nitrides represent a rich family of phases where the stability and microstructures are still not completely understood, it is exciting to find news structures of NbxNy under high pressure.

Reconstructed data of landings for the artisanal beach seine fishery in the marine-coastal area of Taganga, Colombian Caribbean Sea.

This paper presents a dataset on the abiotic (oceanographic, atmospheric and global climatic indices) and fishery variables of the marine-coastal area of the Magdalena Province in the area between Taganga and Bahía Concha, located north of Santa Marta in the Colombian Caribbean. The abiotic variables were downloaded from the satellites of the National Aeronautics and Space Administration (NASA) and the meteorological stations of the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM). The fishery variables were obtained through field trips in the study area. A dynamic artificial neural network was implemented to reconstruct the missing data in the fishery variables from the known abiotic variables (Precipitation, North Atlantic Oscillation and Multivariate ENSO Indices). In this way, a dataset was obtained that is important to determine the historical changes of fishery resources for the study area and to make catch forecasts incorporating the variability of the environmental conditions (atmospheric and oceanographic).

Data from multimodal functions based on an array of photovoltaic modules and an approximation with artificial neural networks as a scenario for testing optimization algorithms.

This paper presents the data of multimodal functions that emulate the performance of an array of five photovoltaic modules under partial shading conditions. These functions were obtained through mathematical modeling and represent the P-V curves of a photovoltaic module with several local maximums and a global maximum. In addition, data from a feedforward neural network are shown, which represent an approximation of the multimodal functions that were obtained with mathematical modeling. The modeling of multimodal functions, the architecture of the neural network and the use of the data were discussed in our previous work entitled "Search for Global Maxima in Multimodal Functions by Applying Numerical Optimization Algorithms: A Comparison Between Golden Section and Simulated Annealing" [1]. Data were obtained through simulations in a C code, which were exported to DAT files and subsequently organized into four Excel tables. Each table shows the voltage and power data for the five modules of the photovoltaic array, for multimodal functions and for the approximation of the multimodal functions implemented by the artificial neural network. In this way, a dataset that can be used to evaluate the performance of optimization algorithms and system identification techniques applied in multimodal functions was obtained.