Journal: Data in Brief

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

doi: 10.1016/j.dib.2020.106296

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Article Snippet: How data were acquired , The simulation data were obtained using MATLAB/Simulink R2017a. The voltage and current values used as input data for the Adaptive Linear Neuron (ADALINE) network were obtained by computer simulations of a 65 W photovoltaic (PV) module. For the Real-Time Recurrent Learning Gradient (RTRL) and iterative Least Mean Square (LMS) algorithms, only six (6) of those data were needed for the neural network training. In the case of the standard LMS algorithm, only three (3) data were used for training the neural network. The experimental data were obtained with the following devices: PIC18F2550 Microcontroller, UNI T UT33C+ Digital Multimeter, FLUKE 116 Multimeter, Solar radiation pyrometer (Dr. Meter SM-206). During the experimental tests, the voltage and current values were acquired at the connection between the PV module and the neural controller. These elements, together with the dc-dc converter and a 12 V battery, were exposed to varying environmental conditions (rainy and sunny days)..

Techniques: Blocking Assay, Generated