Artificial Neural Network and Data Dimensionality Reduction Based on Machine Learning Methods for PMSM Model Order Reduction

• Published in: IEEE access Vol. 9; pp. 102345 - 102354
• Main Authors: Raia, Maria Raluca, Ruba, Mircea, Nemes, Raul Octavian, Martis, Claudia
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adaptive data generation; Artificial neural networks; Computational efficiency; Computational modeling; Computing costs; Data models; Data reduction; Dimensionality reduction; Dynamic models; dynamic simulation; Finishes; Finite element method; Machine learning; Mathematical model; Model accuracy; Model reduction; Neural networks; Neurons; permanent magnet synchronous machine; Post-processing; Prototyping; real-time; Rotors; Series (mathematics); Synchronous machines; Torque; Workflow
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3095668

The present paper targets a solution for permanent motor synchronous machine (PMSM) model order reduction (MOR) using artificial neural networks and machine learning techniques for data dimensionality reduction. The neural networks are trained using data obtained from a series of electromagnetic Finite Element Analysis (FEA), conducted in conditions imposed by the data dimensionality reduction method. The workflow proposed to build the PMSM MOR, starts with data generation, goes further to its post-processing, and finishes with the model training and experimental validation. In the study, data dimensionality reduction procedure (adaptive data generation) is performed to increase the computational efficiency, also maintaining the model accuracy. Different data reduction approaches are compared from the computational cost's point of view and their ease of use. The obtained results are compared to those obtained from FEA seeking the best solution for building the dynamic model. The resulting ROM is included in a real-time control prototyping platform to characterize machine's performances. The model accuracy and its usability are proved in a comparative analysis with simulated versus experimental measurements.