Artificial neural network based boost inverter topology for grid-connected battery applications

This study introduces a novel cascaded boost converter controlled by artificial neural networks (ANN) to enhance efficiency and performance. The cascaded structure allows for higher voltage gain while maintaining reduced current ripple, ensuring optimal operation in energy storage and distribution systems. The converter was designed to boost low-voltage battery outputs for seamless grid integration. Beyond voltage amplification, this cascaded topology improves power quality, enhances dynamic response, and minimizes component stress. This innovation is crucial for improving grid-connected battery applications, where efficiency and reliability are paramount. In this study, an artificial neural network (ANN) based boost inverter topology is developed for grid-connected battery applications. In the first stage, a boost converter is designed to increase efficiency and performance. Then, an inverter and LC filter are added to this converter. In the last stage, ANN is integrated to provide dynamic and adaptive control of the system. The boost converter converts the low voltage battery output to a higher voltage and applies it to the inverter input. The inverter converts the DC voltage to AC voltage and provides grid connection. The LC filter improves the output waveform and reduces harmonic distortion. ANN is used to optimize the performance of the system and adapt to variable load conditions. MATLAB/Simulink simulations show that the proposed system is more efficient and stable than previous techniques. ANN-based control technique increases the inverter output and system performance. In addition, ANN in the boost inverter architecture provides real-time monitoring and adaptive control to ensure optimum performance under variable grid conditions. This adaptive capacity increases power supply reliability and system resilience to dynamic load changes. Research shows that ANN can revolutionize power electronics with better and more efficient control methods. As a result, the artificial neural network-based boost inverter architecture has the potential to make a significant contribution to future renewable energy and battery management system integration research.