Novel Optimisation Algorithm of Electrical Machines

Tan, Z; Baker, NJ; Cao, W

doi:10.1049/cp.2016.0323

Novel Optimisation Algorithm of Electrical Machines

Lookup NU author(s): Zheng Tan, Professor Nick Baker ORCiD, Dr Wenping Cao

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Institute of Engineering and Technology (IET), 2016.

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Abstract

This paper presents a case study for multi-variable and multimodal design optimisation of a doubly fed induction generator (DFIG) based on surrogate-model optimisation algorithm. The DFIG's winding of stator and rotor are optimised to obtain higher efficiency for rewinding purposes. First, a Latin hypercube design is selected as the design of experiments to obtain sampling points. Then, the surrogate model is constructed using Kriging Model (KRG) method based on the Latin hypercube design. Finally, the particle swarm optimisation algorithm is applied in conjunction with the finite element method to achieve the machine design optimisation.