Remote Cloud-Based Inverter Monitroing and Troubleshooting

Abstract

As nations progressively go to solar based energy as valuable option in contrast to conventional power sources, guaranteeing the proficient activity of solar power inverters becomes primitive. This project introduces the improvement in the traditional solar-inverter systems by introducing advance technological features including Maximum Power Point Tracking (MPPT), power prediction based on weather using machine learning model and inverter output parameters visualization on our custom mobile app with enhanced UI via cloud-based data management. The project involves a design of kit to introduce smart features into traditional invertors by incorporating Maximum Power Point Tracking (MPPT) to enhance efficiency by optimizing the power output from solar panels under varying environmental conditions., propelling their abilities. Also, it integrates machine learning models that are trained on historical data of weather and power generated by solar panels. This model is used to get the anticipated power according to weather forecast. Thereby, improving the management and utilization of energy. To provide users with comprehensive control and insights, a mobile application is developed, offering real-time monitoring of energy production and forecasted power production. This app enables users to track performance, receive alerts, and make informed decisions to optimize their energy use