Thesis
Advanced structural modelling and design of wind turbine electrical generators
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- Awarding institution
- University of Strathclyde
- Date of award
- 2017
- Thesis identifier
- T14729
- Person Identifier (Local)
- 20153978
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- This thesis concentrates on direct drive electrical generators for wind energy applications. A variety of wind turbine configurations and generator topologies are reviewed.;Direct drive renewable energy converters introduce a low speed, high torque input into the electrical machine. Due to this, these generators have to be larger and more robust than their high speed counterparts. With very large airgap closing forces, a very stiff structure capable of withstanding the stress is necessary. As a result very heavy machines, with structural ('inactive') material dominating the electromagnetically 'active' material are designed.;In this thesis a stiffness approach is introduced which combines electromagnetic stiffness and structural stiffness for different modes of deflection. This is used to minimise mass of the generator by trading stiffness of rotor and stator structures.;Design tools are presented, validated and utilised to model lightweight supporting structures ('inactive material') for high torque radial flux permanent magnet synchronous generators. Different structural layouts are statically studied, compared and optimised. Making use of low density materials, such as composites, a simplified generator structure is designed and contrasted with its optimised steel counterpart.;As a rotating piece machinery forming part of a bigger and more complex machine, electrical generators are subject to dynamic and external forces coming from the wind turbine rotor. The optimised steel design is looked at from a dynamic viewpoint. Discussions and conclusions highlight the potential design solutions that can be adopted to minimise the mass and therefore the cost of these machines.
- Resource Type
- DOI
- Date Created
- 2017
- Former identifier
- 9912568091202996
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