A PSO Tuning ANN for Extracting the MPP from a DC Microgrid System under Changing Irradiance

The application of Particle Swarm Optimization (PSO) tuning for Artificial Neural Networks (ANNs) is an effective method for maximizing power output from photovoltaic (PV) panels in a DC microgrid system under changing irradiance conditions. This technique can be used to optimize the performance of a PV system, particularly in locations with variable weather conditions. This study proposes a new approach of implementing ANN that is tuned using practical swarm optimization technique. The ANN is trained using data generated by MATLAB Software on the weather conditions and the power output of the PV system and the PSO algorithm is then used to tune the parameters of the ANN model. The optimized ANN model can then be under severe proposed changes in irradiance and to adjust the power converter to achieve the maximum power output. The DC microgrid system used in this study is composed of multiple PV panels and battery along with a super capacitor for a storage system to feed a resistive load. The results of this study illustrate that the PSO tuning of ANNs is an effective method for maximizing power output from PV panels in a DC microgrid system under changing irradiance conditions reaching a precision of 99.7% from the PV panels and response time of 0.11s.