A Combined Approach of Optimizing High-Efficiency DC-DC Converter with Grid-Connected PV Systems
Abstract
The configuration of DC-DC converter with double loop controllers for dual phases grid connected PV inverter improves photovoltaic systems through increasing the solar output voltage for grid integration. It uses dual loop control to achieve stable regulation, which improves efficiency and power quality when transforming DC to AC power. Due to its complexity, a Step-Up DC–DC Converter with Double Loop Controllers for Dual-Stage Grid Connected PV Inverter may be unstable and inefficient. The dual-loop control system can be difficult to tune, leading to oscillations and performance losses. Its applicability may also be limited by its larger size and higher expenses. To overcome this problem, in this paper proposed a combined approach of optimizing high-efficacy DC-DC converter with grid-connected PV systems. The proposed combined technique is joint execution of both Red Billed Blue Magpie Optimizer (RBBMO) and Loss-Attention Physics-Informed Neural Network (LA-PINN). Hence, it is named as RBBMO-LA-PINN approach. The main aim of proposed method to attain high voltage gain at low DC with decreased module amount, minimize power loss and enhance efficiency. The RBBMO method is utilized to optimize duty cycle of DC-DC boost converter. The LA-PINN method is used to predict gain parameter of STSM (Super Twisting Sliding Mode Controller). The proposed technique performed in MATLAB platform and related with other existing approaches like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Bee Colony Optimization (ABC). The proposed technique shows well results than existing approaches. The proposed method increase the efficiency by 99%, which is higher compared with existing techniques. The proposed technique based error 0.03%, which is less compared with existing methods. Keywords: DC-DC Boost Converter, Grid Connected PV System, Inverter Control, Pulse Width Modulation, PR Controller and Single Switch.