AI-Driven Assistant for Optimal Design of Two-Level Three-Phase Voltage Source Inverters

The design of power electronics converters is a complex and time-consuming process, requiring the evaluation of multiple design topologies, components, and technologies to meet specific efficiency and cost targets. Traditional methods are often constrained by manual effort, limiting the number of possible solutions explored. This project proposes an artificial intelligence (AI)-driven assistant that leverages generative AI to automate and optimize the converter design process. The system will take user-defined input specifications, distinguishing between fixed and variable parameters, and generate a diverse set of potential designs. First, by integrating a structured and searchable database of converter technologies and components, the assistant intelligently explores a broad design space, including various topologies, manufacturers, and switching technologies. Second, it leverages similarity-based retrieval and constraint filtering to provide tailored component recommendations that align with the user's technical requirements. Third, it performs iterative evaluation of candidate configurations, presenting the most competitive solutions through a Pareto efficiency-cost analysis, along with detailed technical specifications for each optimal design. Two case studies are presented to validate the methodology, demonstrating the assistant's ability to rapidly identify high-quality design solutions while reducing engineering effort. These results confirm the effectiveness of the proposed AI-driven approach and open new directions for research in intelligent inverter design automation.