PROJECT TITLE :

Global Identification of a Low-Order Lumped-Parameter Thermal Network for Permanent Magnet Synchronous Motors

ABSTRACT:

Monitoring vital temperatures in permanent magnet synchronous motors (PMSM) is crucial to prevent device failures or excessive motor life-time reduction thanks to thermal stress. A lumped-parameter thermal network (LPTN) consisting of four nodes is intended to model the foremost important motor components, i.e., the stator yoke, stator winding, stator teeth, and therefore the permanent magnets. An empirical approach primarily based on the excellent experimental coaching data and a particle swarm optimization are used to identify the LPTN parameters of a 60-kW automotive traction PMSM. Varying parameters and physically motivated constraints are taken under consideration to extend the model scope beyond the coaching data domain. Here, a so-known as world identification technique for linear parameter-varying systems is innovatively applied to a thermal motor model for the first time. The model accuracy is cross-validated with freelance load profiles, and a maximum estimation error (worst-case) of eight°C relating to all considered motor temperatures is achieved. Additionally, a comprehensive residual statistical analysis proves suitable estimation leads to terms of model robustness and accuracy.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE :Global Optimality in Low-Rank Matrix Optimization - 2018ABSTRACT:This Project considers the minimization of a general objective function f(X) over the set of rectangular n × m matrices that have rank at most r.
PROJECT TITLE :Global Energy Efficiency Optimization for Wireless-Powered Massive MIMO Aided Multiway AF Relay Networks - 2018ABSTRACT:This Project considers a wireless-powered massive multi-input multioutput aided multiway amplify-and-forward
PROJECT TITLE :Multi-Label Learning with Global and Local Label Correlation - 2018ABSTRACT:It is well-known that exploiting label correlations is vital to multi-label learning. Existing approaches either assume that the label
PROJECT TITLE :Extremum Seeking Control-based Global Maximum Power Point Tracking algorithm for PV array under partial shading conditions - 2017ABSTRACT:The aim of this paper is to gift a general description concerning the method
PROJECT TITLE :Adaptive Global Fast Terminal Sliding Mode Control of Grid-connected Photovoltaic System Using Fuzzy Neural Network Approach - 2017ABSTRACT:In this paper, an adaptive international quick terminal sliding mode control

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry