Performance Enhancement of Multi-level Inverters with Different Computation Techniques

Multi-Level Inverters (MLIs) have garnered significant attention in recent years due to their ability to address the limitations of traditional two-level inverters, such as high voltage stress on power devices and increased Total Harmonic Distortion (THD) in output voltage waveforms. This review study explores various computation techniques employed for enhancing the performance of MLIs. MLIs and their significance in modern power electronic systems is it discusses the limitations of conventional control techniques and motivates the need for advanced computation methods to optimize the operation of MLIs. It outlines the objectives and scope of the review study, including the identification of different computation techniques and their impact on MLI performance metrics such as efficiency, Total Harmonic Distortion (THD), and switching losses. A comprehensive analysis of different computation techniques used to enhance MLI performance. It categorizes these techniques into distinct groups such as artificial intelligence-based methods, machine learning algorithms, and fuzzy logic control. Each category is explored in detail, discussing the underlying principles, advantages, and limitations of the respective techniques. Moreover, the review highlights recent advancements and research trends in the field, providing insights into the potential applications and future directions for improving MLI performance through advanced computation techniques. Major Findings: Various computation techniques employed for enhancing the performance of MLIs. Conventional control techniques and motivates the need for advanced computation methods to optimize the operation of MLIs. MLI performance metrics such as efficiency, Total Harmonic Distortion (THD), and switching losses.