AI-Driven Smart Control Techniques for Multilevel Inverters in Modern Power Systems—A Comprehensive Review

Modern power systems are evolving rapidly with growing distributed generation and high penetration of renewables and electric vehicles. Renewable sources, being inherently intermittent and weather dependent, may lead to rapid power fluctuations that challenge grid stability and power quality. Multilevel inverters (MLIs) have become a key enabling technology in modern power grids due to the escalating need for high-power, high-voltage, and high-quality energy conversion. Research on the control of MLIs in modern grid scenarios has significant scope due to the rising penetration of renewables, distributed generation and smart grid technologies. Hence sophisticated control strategies and topology selection of MLIs are required to enhance efficiency and to ensure optimal performance. Emerging areas like AI-based adaptive control find relevance in enabling stable, flexible and sustainable future power systems. In this comprehensive review, the state-of-the-art MLI classification, AI-driven control techniques, and their emerging technological applications are investigated. There is limited exploration of AI-based adaptive control for self-tuning operation and coordinated control of multiple MLIs in microgrids, active filtering and harmonic compensation in MLI-based modern power systems, and vehicle-to-grid (V2G) and bidirectional battery inverter control optimization integrated with MLI control.