ADVANCEMENT IN CONTROL STRATEGIES OF CASCADED MULTILEVEL INVERTERS USING AI DRIVEN ALGORITHM - Ashish Biradar; Veerendra Dakulagi (International Educational Journal of Science and Engineering, 2026)
The growing demand for high-quality power conversion in renewable energy systems and industrial applications has driven advancements in control strategies for cascaded multilevel inverters (CMLIs). Conventional control methods often suffer from limitations such as high total harmonic distortion (THD), switching losses, and reduced adaptability under dynamic conditions. This paper presents an AI-driven approach for enhancing the control performance of CMLIs using techniques such as machine learning and deep learning. The proposed methods enable adaptive modulation, real-time optimization, and improved fault detection by leveraging system data including voltage levels, load variations, and switching states. Performance is evaluated using key metrics such as THD, efficiency, and response time, demonstrating superior results compared to traditional control techniques. The AI-based approach achieves reduced harmonic distortion, improved voltage quality, and enhanced system stability. Despite challenges related to computational complexity and data requirements, the findings highlight the potential of AI-driven algorithms in developing efficient, reliable, and intelligent inverter systems for modern power applications.