Data Driven Automatic Electrical Machine Preliminary Design with Artificial Intelligence Expert Guidance
Yiwei Wang, Tao Yang, Hailin Huang, Tianjie Zou, Jincai Li, Nuo Chen,, Zhuoran Zhang

TL;DR
This paper introduces a data-driven AI framework for electrical machine preliminary design, enabling rapid, scalable, and optimized designs using surrogate modeling and expert databases, validated on a wound-rotor synchronous generator case.
Contribution
It develops an AI-based expert database and surrogate model to automate and accelerate electrical machine design, reducing design time from days to seconds.
Findings
Achieved higher power density in seconds versus days.
Validated surrogate model with FE simulations across power ranges.
Demonstrated scalable design generation using metaheuristic algorithms.
Abstract
This paper presents a data-driven electrical machine design (EMD) framework using wound-rotor synchronous generator (WRSG) as a design example. Unlike traditional preliminary EMD processes that heavily rely on expertise, this framework leverages an artificial-intelligence based expert database, to provide preliminary designs directly from user specifications. Initial data is generated using 2D finite element (FE) machine models by sweeping fundamental design variables including machine length and diameter, enabling scalable machine geometry with machine performance for each design is recorded. This data trains a Metamodel of Optimal Prognosis (MOP)-based surrogate model, which maps design variables to key performance indicators (KPIs). Once trained, guided by metaheuristic algorithms, the surrogate model can generate thousands of geometric scalable designs, covering a wide power range,…
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Taxonomy
TopicsSensorless Control of Electric Motors
