Work

Optimization of Stator Structure for Improved Accuracy in Variable Reluctance Resolvers Using Advanced Machine Learning Techniques

Year: 2024

Type: article

Abstract: This study presents an optimized design for a Segmented Sinusoidal Parameter Winding with Magnetic Wedge Variable Reluctance Resolver (SSPWMW-VRR), addressing challenges like winding asymmetry and harmonic distortion in conventional designs. By integrating Particle Swarm Optimization (PSO) for winding design, Magnetic Equivalent Circuit (MEC) analysis for leakage flux, and machine learning techniques (XGBoost and Multi-Layer Perceptron), the stator slot shape was fine-tuned for improved accuracy. XGBoost outperformed MLP in prediction accuracy with a Mean Absolute Error (MAE) of 0.1172. Finite Element Analysis (FEA) simulations and experimental validation demonstrated a reduction in position errors from ±30’ in conventional VRRs to ±5’ in the optimized design, along with significant harmonic reduction.

Source: Energies

DOI: 10.3390/en17215454

Authors: Wentao Li, Siyang Ye, Qiankun Liu, Surong Huang

Institution: Shanghai University

Language: English

Topic: Electric Motor Design and Analysis

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,955