NOVEL SOLAR PHOTOVOLTAIC EMULATION FOR VALIDATING THE MAXIMUM POWER POINT ALGORITHM AND POWER CONVERTER

Abstract

The photovoltaic (PV) source emulator plays an essential role in evaluating the performance of solar PV arrays, maximum power point (MPPT algorithms), power converters, and control algorithms in the rapidly growing field of solar power generation. This paper presents a novel neural network (NN)--based solar array emulator (SAE) for emulating PV array dynamic characteristics. The proposed SAE reference model, developed using NN, replicates PV array characteristics with a programmable dc power source's support under varying environmental conditions. A 640 W stand-alone PV system is designed and tested using the proposed SAE to validate its performance under various environmental conditions. The performance of the NN-based SAE with the MPPT algorithm is evaluated and compared to the conventional diode-based SAE. The results showed that the proposed NN-based SAE had good accuracy in emulating the dynamic characteristics of the PV array and was faster in execution than the conventional diode-based SAE. The output results of the developed NN-based SAE demonstrate its potential for evaluating MPPT algorithms and power converters.