Analytical modelling and efficiency optimisation of permanent magnet synchronous machine using particle swarm optimisation
| dc.contributor.author | Ling Poh Ping | |
| dc.date.accessioned | 2021-05-10T05:13:14Z | |
| dc.date.available | 2021-05-10T05:13:14Z | |
| dc.date.issued | 2018-04-01 | |
| dc.description.abstract | Machine design has always been a comprehensive process with inter-dependant variables that are subjected to many factors such as non-linear relationship between parameters, material properties, design limitations and application-dependant requirements. While analytical modelling has been continuously developed to predict as closely as possible to resemble the finite element analysis (FEA) and real-time machine operation, but analytical modelling as well as FEA are unable to pin-point specific machine variables required to be optimised for a particular design. Furthermore, stochastically choosing machine variables is not efficient in machine optimisation as there are complicated non-linear relationships between machine parameters in PMSM. Therefore, this research focuses on the usage of subdomain modelling for analytical prediction of magnetic field and other attributes to optimise a three-phase, 12slot/8pole surface mounted PMSM with external rotor topology, by varying selected machine variables - magnet pole arc, magnet thickness, air-gap width and slot opening individually. Subsequently, an intelligent computational algorithm - Particle Swarm Optimization (PSO) was later applied to all the machine variables simultaneously to find the optimal solution for a compromised optimal machine performance. The improved machine performace are based on the chosen performance indexes – efficiency and THDv. The results obtained from the analytical prediction and particle swarm PSO were compared with FEA for verification and was found to be in good agreement. From PSO study, the four machine design variables has been simultaneously optimised and successfully produced parameters for a performance-optimised machine. The research results has also demonstrated that by simplifying traditional stochastic methods in the targeted machine variables, a combination of analytical modelling and PSO allows a more efficient machine design and optimisation process. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/13378 | |
| dc.language.iso | en | en_US |
| dc.title | Analytical modelling and efficiency optimisation of permanent magnet synchronous machine using particle swarm optimisation | en_US |
| dc.type | Thesis | en_US |
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