Design of Induction Machines using Reinforcement Learning
This provides a solution for electrical machine designers and sales tools to quickly generate custom designs for customers, though it appears incremental as it applies existing RL methods to a domain-specific problem.
The paper tackles the challenge of designing induction machines by automating the selection of design parameters to meet specific torque, current, and temperature requirements, using a reinforcement learning algorithm trained offline with simulations, resulting in a method that eliminates the need for human engineering knowledge.
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.