Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part II)
This work addresses the need for high-performance control in electric motor drives, but it is incremental as it focuses on data-driven model extraction using existing methods on a new dataset.
The paper tackles the problem of extracting accurate mathematical models for a three-phase permanent magnet synchronous motor and inverter from measurement data, using a dataset of approximately 40 million samples to evaluate methods like ordinary differential equations, least squares, and machine learning for model predictive control, with results showing that state-of-the-art white-box models often underperform due to limited validity and unmodeled parasitic effects.
A data set was recorded to evaluate different methods for extracting mathematical models for a three-phase permanent magnet synchronous motor (PMSM) and a two-level IGBT inverter from measurement data. It consists of approximately 40 million multidimensional samples from a defined operating range of the drive. This document describes how to use the published data set \cite{Dataset} and how to extract models using introductory examples. The examples are based on known ordinary differential equations, the least squares method or on (deep) machine learning methods. The extracted models are used for the prediction of system states in a model predictive control (MPC) environment of the drive. In case of model deviations, the performance utilizing MPC remains below its potential. This is the case for state-of-the-art white-box models that are based only on nominal drive parameters and are valid in only limited operation regions. Moreover, many parasitic effects (e.g. from the feeding inverter) are normally not covered in white-box models. In order to achieve a high control performance, it is necessary to use models that cover the motor behavior in all operating points sufficiently well.