Asian Journal of Advanced Research and Reports

This study presents a systematic review of intelligent power electronics converters for renewable energy systems, focusing on Digital Twin-based optimization to enhance operational efficiency, reliability, and real-time performance tracking. The operating conditions of renewable energy systems remain unpredictable because these systems create difficulties for power converters that use established control methods. The solution to these problems required a complete approach which united system modeling through integrated sensor deployment, data acquisition and processing, communication protocols, and database management and predictive algorithm development. The Digital Twin framework enables real-time interaction between physical converters and their virtual counterparts using standardized industrial communication protocols. The system used PostgreSQL and InfluxDB and SQLite databases to handle both structured data and time-series information. A Long Short-Term Memory neural network model was used to predict converter operating parameters while simultaneously identifying energy conversion and environmental condition-related anomalies which impact converter performance. The results show that the system became more responsive while users gained better awareness of their situation and the system developed better prediction abilities. The review synthesizes evidence from 52 peer-reviewed studies selected through a PRISMA-guided systematic screening process The model successfully detected all abnormal operating conditions which included voltage and current deviations and excessive thermal levels and unfavourable environmental effects. The Long Short-Term Memory model produced a Root Mean Squared Error of 0.04 which shows that it made very accurate predictions. The research establishes an expandable system which unites smart system management with predictive equipment upkeep and renewable energy power electronics converter performance optimization. The reviewed studies predominantly employ simulation-based and hardware-in-the-loop validation environments under variable renewable operating conditions. These findings highlight the potential of intelligent converter frameworks to support scalable, reliable, and grid-compliant renewable energy infrastructures.