Improving Solar Energy Conversion Efficiency through Luo Converter Enhanced by Artificial Neural Network and Cloud Integration

In the field of renewable energy systems, the focus on abundance and sustainability has propelled solar power to the forefront. Despite its popularity, optimizing solar energy conversion and utilization efficiency remains a significant challenge. In order to address this challenge, this research study introduces a novel approach: The integration of an Artificial Neural Network (ANN) controller with a Luo Boost Converter to create a solar charge controlling based efficient load distributing system. The proposed system utilizes Photovoltaic (PV) panels for solar energy collection, storing the harvested energy in a battery bank. An ANN controller operates the Luo Boost Converter, ensuring optimal distribution by efficiently transforming the battery’s stored DC voltage into a steady, increased output voltage capable of powering AC loads upon inversion. Leveraging the ANN controller enhances the system’s responsiveness and adaptability to varying load demands and environmental conditions, enabling real-time adjustments for peak efficiency. Precise control facilitated by the ANN optimizes the load distribution process, ensuring efficient utilization of available solar energy without wastage. This innovative strategy aims to advance solar energy system development by enhancing both distribution and harvesting domains. Additionally, the incorporation of cloud integration technology enables remote monitoring of voltage levels within the Luo converter via smartphones or computers. Furthermore, a voltage sensor triggers an alarm or buzzer in case of overvoltage or undervoltage scenarios, empowering users to identify and address issues promptly. Through the fusion of advanced control techniques with effective power conversion technology, the proposed model not only promises improved performance and viability of solar energy systems but also promotes sustainable energy practices for a greener future.