Efficient, reliable, and adaptable energy conversion solutions, particularly grid-connected power electronics, are essential for the modernization of electrical power networks. Traditional control techniques are inadequate for smart grids because of the increasing complexity and quantity of dispersed energy resources and the incorporation of renewable power sources. In order to improve energy conversion for power electronics devices that are connected to the grid, this study employs big data. With the use of high-volume, real-time data collected from sensors, inverters, and converters, the suggested system employs predictive modelling, machine learning, and advanced analytics to enhance energy efficiency, stability, and fault tolerance. With cloud-edge computing, adaptive grid, environmental, and load response are made possible, as is low-latency data processing. The use of digital twin simulations allows for the real-time prediction of system performance and the prevention of faults. Smart inverters, electric vehicle infrastructure, and renewable energy integration are perfect applications for the flexible and expandable technology. Several operational situations significantly improve system durability, switching losses, and energy conversion efficiency, according to both theoretical and experimental research. This study details autonomous power electronics that are data-driven and capable of optimization and decision-making in complicated grid situations.
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