An artificial neural network-based performance model of triple-junction InGaP/InGaAs/Ge cells for the production estimation of concentrated photovoltaic systems - Shoaib, Aisha; Burhan, Muhammad; Chen, Qian; Oh, Seung Jin (Frontiers in Energy Research, 2023)
Analytical and empirical models analyze complex and non-linear interactions between input-output parameters of the system. This is very important in the case of photovoltaic systems to understand their real performance potential. On the other hand, the manufacturers of the photovoltaic panels rate the maximum performance of the system under fixed lab conditions as per standard testing conditions, STC or nominal operating cell temperature, and NOCT standards of IEC. These ratings do not provide the actual production potential of the system in the field with fluctuating conditions of irradiance and temperature. For the case of concentrated photovoltaic (CPV), utilizing multi-junction solar cell (MJC), there is no commercial tool available to analyze the performance and production despite some recent empirical models which also require post-processing of experimental data to be used in the conventional models. In this study, an artificial neural network (ANN) based performance model is presented for a multi-junction solar cell (MJC) which is not only convenient to apply but can be easily expanded to predict the real field performance of CPV system of any designed size. In addition, the ANN base model showed high accuracy of 99.9% to predict the performance output of MJC as compared to diode-based empirical models available in the literature. Irradiance concentration at the cell area and the cell temperature are taken as inputs for the neural network. If both of these parameters are known, the cell efficiency as output can accurately predict the CPV performance for the field operation.