This study presents a prediction model for comparing the performance of six different photovoltaic (PV) modules using artificial neural networks (ANNs), with simple inputs for the model. Cell temperature (T_c), irradiance, fill factor (FF), short circuit current (I_sc), open-circuit voltage (V_oc), maximum power (P_m), and the product of V_oc and I_sc are the inputs of the neural networks’ processes. A Prova 1011 solar system analyzer was used to extract the datasets of IV curves for six different PV modules under test conditions. As for the result, the highest FF was the mono-crystalline with an average of 0.737, while the lowest was the CIGS module with an average of 0.66. As for efficiency, the most efficient was the mono-crystalline module with an average of 10.32%, while the least was the thin-film module with an average of 7.65%. It is noted that the thin-film and flexible mono-modules have similar performances. The results from the proposed model give a clear idea about the best and worst performances of the PV modules under test conditions. Comparing the prediction process with the real dataset for the PV modules, the prediction accuracy for the model has a mean absolute percentage error (MAPE) of 0.874%, with an average root mean square error (RMSE) and mean absolute deviation (MAD) of, respectively, 0.0638 A and 0.237 A. The accuracy of the proposed model proved its efficiency for predicting the performance of the six PV modules.

This paper presents a new I-V curve prediction method using Artificial Neural Networks (ANNs), based on two ANNs, Generalized Regression Neural Network (GRNN) and cascaded forward neural network (CFNN).Solar radiation, ambient temperature, and the specification of PV module (open circuit voltage and short circuit current at STC) are inputs for this method. This method has a high accuracy in predicting I-V curves with average Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE) and Root Mean Square Error (RMSE) are 1.09%, 0.0229(A) and 0.0336(A) respectively for the validation data.

The inherent nature of power-electronic circuits inside on-grid PV inverters of Distributed Photovoltaic installations has brought new challenges of maintaining the harmonic voltage levels throughout the network within acceptable limits. Having the harmonic currents spectrum of each inverter already certified ‘individually’ does not ensure the compliance of accumulated spectrums from multiple of these inverters connected in parallel. As such, the accumulated harmonic current emissions from parallel PV inverters of certain Distributed Photovoltaic installations should be assessed in terms of their impact on the harmonic voltage levels throughout the network it's connected to. This work presents a detailed investigation and analysis to simulate and assess the levels of accumulated harmonic currents from a photovoltaic system with parallel PV inverters. The work contains derivations to generate emission limits based on the installed capacity of Distributed Photovoltaic installation and background harmonic voltage levels of the network. Furthermore, additional parameters have been considered, including the line impedance, topology of the point of connection, vector sum of harmonic orders, and others. The work presents an actual case study to assess the harmonic currents and voltages of a certain installation, based on which its integration approval with the grid has been decided by the network planners/operators.