CPV module electric characterisation by artificial neural networks

Concentrating photovoltaic (CPV) is a relatively new technology with promising future expectations. However, it is at an early stage of development and it has much room for improvement. In order to gain knowledge about CPV technology, outdoor measurements are necessary to adjust models and to study the influence of the atmospheric conditions on the modules performance. In this work, multilayer perceptron models are applied to generate I–V characteristic curves of one of the most extended commercial module of concentrating photovoltaic technology, using the influential atmospheric variables as inputs to the networks. To train these networks an experiment with real measurements was carried out in Jaén, Spain, from July 2011 to June 2012. In addition to a model based on I–V curves expressed as a list of points in Cartesian coordinates, we present an alternative model trained with curves represented in polar coordinates. A previous selection of the most representative samples from the initial dataset was performed using a Kohonen self-organising map. This procedure allows the simulation of the curves even under non-frequent atmospheric conditions. Using the proposed models, it is possible to obtain the characteristic curve of other CPV modules under different meteorological conditions, with high accuracy and fidelity.