Predicting Composition of Photo Voltaic Cells Using Neural Networks

Norman, Thaddeus J ; Ranjan, Shubha S

关键词: CHEMICAL COMPOSITION;    COMPUTER NETWORKS;    ELECTRICAL PROPERTIES;    GALLIUM ARSENIDES;    MATHEMATICAL MODELS;    NEURAL NETS;    OPEN CIRCUIT VOLTAGE;    PHOTOVOLTAIC CONVERSION;    PREPROCESSING;    SAMPLED DATA SYSTEMS;    SHORT CIRCUIT CURRENTS;    SOLAR CELLS;   

RP-ID  :  ARC-E-DAA-TN75359

学科分类：空间科学

美国|英语

来源: NASA Technical Reports Server

【 摘 要 】

A better understanding of IV (current voltage) curve data collected from photo voltaic cells may lead to the construction of solar cells with improved electrical properties. With this in mind, IV curve data from different types of solar cells were acquired from the Photovoltaics and Electrochemical Systems Branch, NASA Glenn Research Center. Neural networks were created to predict the chemical composition of three classes of solar cells. The success of these predictions varied with class.