Design of Induction Machines using Reinforcement Learning

TL;DR

This paper presents a reinforcement learning approach to automate the design of induction machines, enabling quick and customized parameter selection without relying on human expertise.

Contribution

The paper introduces a novel reinforcement learning algorithm for designing induction motors, eliminating the need for manual engineering adjustments.

Findings

Automated design process achieved without human engineering knowledge.

Reinforcement learning effectively optimizes machine parameters.

Method accelerates and simplifies induction machine design.

Abstract

The design of induction machine is a challenging task due to different electromagnetic and thermal constraints. Quick estimation of machine's dimensions is important in the sales tool to provide quick quotations to customers based on specific requirements. The key part of this process is to select different design parameters like length, diameter, tooth tip height and winding turns to achieve certain torque, current and temperature of the machine. Electrical machine designers, with their experience know how to alter different machine design parameters to achieve a customer specific operation requirements. We propose a reinforcement learning algorithm to design a customised induction motor. The neural network model is trained off-line by simulating different instances of of electrical machine design game with a reward or penalty function when a good or bad design choice is made. The results demonstrate that the suggested method automates electrical machine design without applying any human engineering knowledge.