Recursive Bit Assignment With Neural Reference Adaptive Step (RNA) MPPT Algorithm for Photoviolatic System

Abstract

BackgroundNowadays, most of the recent research is directed towards photovoltaic harvesting systems due to their great characteristics. To increase the efficiency of a photoviolatic harvesting system, Maximum Power Point Tracking algorithms are utilized to achieve the maximum output power of the PV. This is done by optimizing the duty ratio of the DC-DC boost converter. ResultsIn this paper, a proposed RNA algorithm is introduced as an efficient MPPT algorithm for the PV system. This proposed RNA algorithm depends on two main parts. The First part is an artificial neural network to produce a reference power. The Second one is a proposed Recursive Bit Assignment network to present the variable step size of the duty ratio of the DC-DC boost converter. The RBA network consists of N-bit memory. The instantaneous PV power value sets the contents of the memory to generate the variable step size of the duty ratio. Moreover, the design of the neural network to give its best performance is explained. ConclusionsThe performance of the chosen PV module is simulated for a variable solar radiation and a constant temperature. Simulation studies are performed using MATLAB to evaluate the system performance. From simulation results, the proposed RNA can achieve a fast tracking time, a high power efficiency, an actual maximum power point and an acceptable ripple. Additionally, comparisons between the RNA algorithm and other related algorithms such as Perturbe and Observe, Neural Network, and Adaptive Neural Interference System algorithms are executed. The proposed RNA achieves the best performance in all terms.