Machine Learning Based Correction Model in PMSM Power Loss Estimation for More-Electric Aircraft Applications

Abstract

This study utilizes the machine learning (ML) technique to estimate the power loss of surface-mounted Permanent Magnet Synchronous Motor (PMSM) for More-Electric Aircraft (MEA). Existing approaches do not consider ML methods in power loss calculation and only depend on empirical correction factors. The proposed ML aided model is proved to be more precise. Matching the analytical loss estimation with finite-element analysis (FEA) is the main research goal which includes two aspects: iron loss and permanent magnet (PM) loss. They are both based on conventional formulae but this study analyzes the limitation of these equations and the ML correction model can provide dedicated factors for the analytical motor model to make sure that the loss estimation is accurate in the whole motor design space. Average correction factor (ACF) approach is regarded as the comparison method to verify the excellent performance of the proposed ML model.

Inverse Application of Artificial Intelligence for the Control of Power Converters

“…Unlike the functional fitting (e.g., polynomial, exponential), users of feedforward ANN do not need to specify any function for this relationship mapping. It has been applied to various electrical engineering problems, from the optimal design for reliability of power electronic converters [12,15], to the permanent-magnet motor performance correction [22,23].…”

Droop Coefficient Design in Droop Control of Power Converters for Improved Load Sharing: An Artificial Neural Network Approach

“…As a rule of thumb, it is recommended that one starts with a relatively small number of neurons and then increase it gradually based on the observed training error [7]. This is a trial-and-error process and can be carried out very fast since the training can be completed within a few seconds [14,15]. In this paper and for the three sources MEA EPS used as a case study, the 11 neurons selected in the hidden layer of the FFNN structure used for training provides a very good match between the droop coefficient combinations used as input to the detailed simulation model and the NN model prediction as shown in Fig.…”