Performance analysis of Electrical Machines based on Electromagnetic System Characterization using Deep Learning

The numerical optimization of an electrical machine entails computationally intensive and time-consuming magneto-static finite element (FE) simulation. Generally, this FE-simulation involves varying input geometry, electrical, and material parameters of an electrical machine. The result of the FE simulation characterizes the electromagnetic behavior of the electrical machine. It usually includes nonlinear iron losses and electromagnetic torque and flux at different time-steps for an electrical cycle at each operating point (varying electrical input phase current and control angle). In this paper, we present a novel data-driven deep learning (DL) approach to approximate the electromagnetic behavior of an electrical machine by predicting intermediate measures that include non-linear iron losses, a non-negligible fraction ($\frac{1}{6}$ of a whole electrical period) of the electromagnetic torque and flux at different time-steps for each operating point. The remaining time-steps of the electromagnetic flux and torque for an electrical cycle are estimated by exploiting the magnetic state symmetry of the electrical machine. Then these calculations, along with the system parameters, are fed as input to the physics-based analytical models to estimate characteristic maps and key performance indicators (KPIs) such as material cost, maximum torque, power, torque ripple, etc. The key idea is to train the proposed multi-branch deep neural network (DNN) step by step on a large volume of stored FE data in a supervised manner. Preliminary results exhibit that the predictions of intermediate measures and the subsequent computations of KPIs are close to the ground truth for a new machine design in the input design space. In the end, the quantitative analysis validates that the hybrid approach is more accurate than the existing DNN-based direct prediction of KPIs, which avoids electromagnetic calculations.