High-Performance Converters for Optimal Utilization of Interconnected Renewable Energy Resources: A Proposed AGORNN Controller

Abstract

In most of the industrial applications, an interleaved buck converter is used due to its limited requirements of filter and the enhanced converter dynamic. However, it suffers from an extremely small duty ratio when the high voltage ratio is required. To overcome the above drawbacks, a series capacitor buck converter was introduced. Based on the series capacitor converter model, a high-performance buck converter is proposed in this paper. In this paper, a high-performance converter is designed for the optimal utilization of interconnected renewable energy sources with the proposed control technique. The model and design of high-performance converter is revealed with the consideration of enhanced converter efficiency, effective utilization of renewable energy sources and reduced switching loss. The proposed control technique is the organized execution of both the adaptive grasshopper optimization algorithm and recurrent neural network (AGORNN). Here, the searching behavior of the grasshopper is modified by using the search functions like crossover and mutation. In the proposed technique, the adaptive grasshopper optimization algorithm (AGOA) is utilized to generate the optimal gain dataset based on the objective of minimum error function. The accomplished dataset is used to train the recurrent neural network (RNN) and it leads to predict the exact gain parameter for the PI controller. Batteries are used as an energy source, to balance out and allow the renewable power system units to continue running at a steady and stable output power. The proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques.