Voltage Tracking of Bidirectional DC-DC Converter Using Online Neural Network for Green Energy Application

In the current era, green energy systems like solar PV, wind energy, and battery storage critically rely on DC-DC converters to manage power flow and voltage conversion efficiently, ensuring optimal performance and reliability. Nevertheless, converters face multiple challenges, including efficiency losses, thermal management concerns, and electromagnetic interference, which can impact these green energy systems' overall performance and reliability. To overcome these challenges, it is necessary to utilize advanced control mechanisms, enhance heat management approaches, and optimize component design. Implementing these improvements will improve the effectiveness and durability of DC-DC converters in renewable energy applications. This research aims to analyze the performance characteristics of a three-phase interleaved half-bridge bidirectional (TPHB-Bi) converter. The research objective involves investigating the effectiveness of the proposed controller by rigorously assessing voltage tracking. This is done through comprehensive assessments of start-up procedures and reference voltage variations using MATLAB/Simulink. The study utilizes a neural network controller with an online learning algorithm based on backpropagation to enhance the converter's operational capabilities, ensuring a stable output voltage and improved transient response. The simulation results highlight the significant advantages of the proposed controller over a conventional PID controller. It exhibits a remarkable reduction in overshoot by 5.29%, considerably shorter rise times ranging from 0.01ms to 0.1ms, and faster settling times of 0.025s. The findings have great importance in promoting sustainable energy development and environmental protection. They demonstrate that implementing advanced control strategies for DC-DC converters can result in more efficient and reliable green energy systems.