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You searched for +publisher:"Georgia Tech" +contributor:("He, Lijun"). Showing records 1 – 3 of 3 total matches.

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1. Zhang, Shen. Multi-objective design, optimization, and condition monitoring of high-performance electric machines for electric propulsion.

Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech

The objective of the proposed research is to develop methods for the multi-objective design, optimization, and condition monitoring of electric machines, so as to generate the optimal designs and improve machine robustness for electric propulsion. In particular, the selected high-performance electric machines are the switched reluctance machine (SRM) with a simple and robust structure, and the interior permanent magnet (IPM) machine with a high torque density and efficiency. For SRMs, an active current profiling technique integrated multi-objective analytical design and optimization is proposed to generate the optimal design in terms of multiple performance indices, which is proven to be accurate and time-saving, especially for a large search space with multiple prime design variables. The proposed scheme offers machine designers accurate, handy and convenient initial designs, which can be further verified or fine-tuned if necessary. The optimization process is further developed with advanced machine learning algorithms to accelerate the search process and facilitate the final decision-making process with the self-organizing map and t-SNE algorithm. To monitor the demagnetization property of the closed-loop direct torque controlled (DTC) IPMSM, two nonintrusive high-frequency signal injection schemes are proposed for PM temperature estimation via analyzing the PM electrical high-frequency resistance, which is a byproduct of the eddy current loss induced by the applied high-frequency magnetic field. The developed methods bring practical ways to excite a proper amount of high-frequency current into the stator winding, which leads to a simple, accurate, and nonintrusive permanent magnet thermal monitoring scheme for DTC-controlled IPM machines. The demagnetization properties of the IPM machine under the most commonly observed stator inter-turn short circuit fault is also systematically investigated via simulations and experiments, thereby offering machine designers handy information in evaluating the demagnetization fault-tolerant capability of various IPM machine design candidates. Advisors/Committee Members: Habetler, Thomas G. (advisor), Graber, Lukas (committee member), Molzahn, Daniel (committee member), He, Lijun (committee member), Wang, Yan (committee member).

Subjects/Keywords: Electric machines; Electric propulsion; Switched reluctance machines; Interior permanent magnet machines; Optimization; Multi-objective; Demagnetization; Monitoring

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APA (6th Edition):

Zhang, S. (2019). Multi-objective design, optimization, and condition monitoring of high-performance electric machines for electric propulsion. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61715

Chicago Manual of Style (16th Edition):

Zhang, Shen. “Multi-objective design, optimization, and condition monitoring of high-performance electric machines for electric propulsion.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 08, 2021. http://hdl.handle.net/1853/61715.

MLA Handbook (7th Edition):

Zhang, Shen. “Multi-objective design, optimization, and condition monitoring of high-performance electric machines for electric propulsion.” 2019. Web. 08 Mar 2021.

Vancouver:

Zhang S. Multi-objective design, optimization, and condition monitoring of high-performance electric machines for electric propulsion. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 08]. Available from: http://hdl.handle.net/1853/61715.

Council of Science Editors:

Zhang S. Multi-objective design, optimization, and condition monitoring of high-performance electric machines for electric propulsion. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61715


Georgia Tech

2. Shao, Hang. Electro-magnetic modeling and design optimization of synchronous reluctance machines and single-phase induction machines.

Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech

The objective of the proposed research is to develop the analytical electro-magnetic (EM) models for synchronous reluctance machines (SynRMs) and single-phase induction machines (IMs), so as to generate the optimal designs to improve their performances. For the SynRM, a universal analytical model is proposed based on Maxwell’s equations and conformal mapping. Saturation effect is modeled with the help of the magnetic equivalent circuit (MEC) model. For the single-phase IM, the equivalent circuit model is adopted in order to analyze the machine performance from the design parameters. Evolutionary algorithms are used to conduct the multi-objective optimization (MOO) for the SynRM and single-phase IM. The optimal designs show improved performance compared with the original designs, and the time consumed is acceptable due to the time efficiency of the analytical model. The ultimate goal of this research is to create a computationally efficient design tool that has the ability to rapidly locate an optimal design candidate which satisfies the design specifications and objectives. Once the optimal design candidate is located, a final design can be easily completed by further refinement using the commercially available finite element analysis (FEA) software. Advisors/Committee Members: Habetler, Thomas G. (advisor), Saeedifard, Maryam (committee member), Molzahn, Daniel K. (committee member), Mayor, J. Rhett (committee member), He, Lijun (committee member).

Subjects/Keywords: Synchronous reluctance machines; Maxwell's equations; Magnetic equivalent circuit; Finite element analysis; Particle swarm optimization; Differential evolution; Pareto front; Single-phase induction machines; Multi-objective optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shao, H. (2019). Electro-magnetic modeling and design optimization of synchronous reluctance machines and single-phase induction machines. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62272

Chicago Manual of Style (16th Edition):

Shao, Hang. “Electro-magnetic modeling and design optimization of synchronous reluctance machines and single-phase induction machines.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 08, 2021. http://hdl.handle.net/1853/62272.

MLA Handbook (7th Edition):

Shao, Hang. “Electro-magnetic modeling and design optimization of synchronous reluctance machines and single-phase induction machines.” 2019. Web. 08 Mar 2021.

Vancouver:

Shao H. Electro-magnetic modeling and design optimization of synchronous reluctance machines and single-phase induction machines. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 08]. Available from: http://hdl.handle.net/1853/62272.

Council of Science Editors:

Shao H. Electro-magnetic modeling and design optimization of synchronous reluctance machines and single-phase induction machines. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62272


Georgia Tech

3. Li, Sufei. Multi-physics modeling and design of switched reluctance machines and large synchronous generators.

Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech

This dissertation focuses on developing multi-physics models for switched reluctance machines (SRMs) and the end regions of large synchronous generators (LSGs), so as to further exploit effective and efficient methods for the design optimization to improve their performances. In particular, for SRMs, a generalized and fast analytical model based on Maxwell’s equations and magnetic equivalent circuits (MECs) that predicts their electromagnetic (EM) behaviors is developed and validated by finite-element analyses (FEAs) and measured results; then, a hybrid thermal model combining 2-dimensional (2D) finite-difference (FD) formulations and thermal circuits is applied to estimate the temperature based on the loss distribution calculated by the EM model. Based on the multi-physics model, the methods of design of experiments (DoE) and evolutionary algorithms are adopted in the multi-objective optimization of SRMs. For the design of the end regions of LSGs, 3-dimensional (3D) EM and thermal models are constructed to estimate the magnetic field, loss density and temperature distributions in this region, which are verified by the agreement between the predicted and measured temperature values. To improve the computational efficiency, a harmonic quasi-3D FD formulation is developed that can provide acceptable solutions of the magnetic field and loss density distributions within a short period of time and is thus an appropriate tool for the initial design. In addition, parametric studies are performed to investigate the influences of different design parameters on the EM and thermal behaviors in the LSG end regions. Advisors/Committee Members: Habetler, Thomas G. (advisor), Saeedifard, Maryam (committee member), Mayor, James R. (committee member), Graber, Lukas (committee member), He, Lijun (committee member).

Subjects/Keywords: Multi-physics modeling; Design optimization; Switched reluctance machines; Large synchronous generators; End region

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Li, S. (2019). Multi-physics modeling and design of switched reluctance machines and large synchronous generators. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61086

Chicago Manual of Style (16th Edition):

Li, Sufei. “Multi-physics modeling and design of switched reluctance machines and large synchronous generators.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 08, 2021. http://hdl.handle.net/1853/61086.

MLA Handbook (7th Edition):

Li, Sufei. “Multi-physics modeling and design of switched reluctance machines and large synchronous generators.” 2019. Web. 08 Mar 2021.

Vancouver:

Li S. Multi-physics modeling and design of switched reluctance machines and large synchronous generators. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 08]. Available from: http://hdl.handle.net/1853/61086.

Council of Science Editors:

Li S. Multi-physics modeling and design of switched reluctance machines and large synchronous generators. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61086

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