Model and design of an efficient controller for microgrid connected HRES system with integrated DC–DC converters: ATLA-GBDT approach

Abstract

A controller is modelled and designed to optimize the power transfer in microgrid-connected hybrid renewable energy systems using an integrated DC/DC converter. To maximize the converter's output power and minimize the switching losses of the converter, a model is developed by including a simplified high conversion ratio converter, a maximal power point tracker, and an optimal controller with an effective control strategy. The proposed control system is a combination of the Artificial Transgender Longicorn Algorithm (ATLA) and the Gradient Boosting Decision Tree (GBDT) algorithm, named the ATLA-GBDT method. In the suggested technique, the ATLA is used as an assessment method to build up accurate control signals for the system and to improve the control signals database for offline use while considering the power exchange between the source and load. In addition, for training a GBDT system online, the data set received from the sensor is used to develop a control system for faster response. In addition, the goal function is defined by the system data, which is subject to equality and inequality constraints. Various constraints considered in the problem formulation are the output of renewable energy sources, power requirements, and the state of charge of storage components. The proposed control system is simulated using the MATLAB/Simulink platform, and the implementation is compared with the existing techniques. Various performance metrics like accuracy, specificity, recall and precision, RMSE, MAPE, and MBE of the proposed method and existing methods in the literature are presented.