Electronic Converters For Renewable Energy Integration: A Findings-Based Analysis

Abstract

The global transition toward renewable energy sources has fundamentally transformed modern power systems, introducing significant challenges in power conversion, grid stability, and control adaptability. This paper presents a comprehensive findings-based analysis of artificial intelligence-enabled adaptive control strategies for power electronic converters in renewable energy integration applications. Through systematic examination of recent experimental validations and comparative studies, we establish that AI-enabled control architectures substantially outperform conventional approaches across multiple performance metrics. Key findings demonstrate that neural network-based adaptive PID controllers achieve up to 96.2% conversion efficiency with total harmonic distortion below 3%, while deep reinforcement learning-based grid-forming converters achieve approximately 35% faster frequency recovery with THD reduction from 4.8% to 3.1%. The intelligent adaptive neuro-fuzzy control framework demonstrates exceptional performance with 99.45% efficiency and stabilization within 0.02 seconds. Hardware-in-the-loop validation confirms practical viability with control latency below 65 ms. This paper contributes a structured analysis of empirical findings and provides actionable insights for researchers and practitioners navigating AI-enabled power converter control for sustainable energy systems.