AI-based hybrid optimisation of multi-megawatt scale permanent magnet synchronous generators for offshore wind energy capture

Lilla, Abdurahman Daleel

AI-based hybrid optimisation of multi-megawatt scale permanent magnet synchronous generators for offshore wind energy capture

Master Thesis

2019

Abstract

The finite nature of earth’s natural resources has become a post-industrial reality. Despite their alarming depletion, fossil fuels still dominated the global final energy landscape. Technological advances and rapid deployment of various renewable energy technologies have demonstrated their potential at reducing the worlds dependency on fossil fuels and their negative impacts. Presently, wind energy is the most cost-effective means of renewable energy conversion in the developed world and has currently has a price point that is in direct competition with fossil fuel. Coupled with the low price, the adoption of wind power has seen capacity increases in excess of 650% over the last ten years. Permanent Magnet Synchronous Generators (PMSGs) have become prominent in large wind energy system applications. The Radial Flux machine topology has become particularly attractive. In order to improve the competitiveness of large wind energy systems, the main focal point of current research is toward reducing the Levelised Cost of Energy (LCOE) of the systems. A proven method of reducing the LCOE of wind power generation is by upscaling RF-PMSGs to the multi mega-watt (MW) range. For the much wider adoption of wind power generation, the cost of energy (price/MWh) needs to be driven down further, by the development of more efficient and cost-effective ways to harvest the vast amounts of energy. The main objective of this dissertation is the drive-train selection, detailed design, sizing and optimisation of a 10.8 MW permanent magnet radial flux synchronous generator (RF-PMSG) to be used in the next generation of offshore wind farms. From an analytical viewpoint, the results suggested the use of a medium speed RF-PMSG utilizing a single-stage geared drivetrain, together with a MV voltage rating (3.3kV) for the 10.8 MW RF-PMSG designed in the thesis. Finally, this dissertation proposes a promising hybrid, analytical-numerical optimisation of a 10.8 MW RF-PMSG to be used for offshore Wind Energy Conversion. The hybrid optimisation utilises a two-stage optimisation strategy that incorporates both an analytical and a numerical (FEA) optimisation; using the DE algorithm and the Taguchi method respectively. Although the permanent magnet losses are neglected in the analytical loss calculations, they are included in the numerical FE portion of the hybrid optimisation. The initial stage (STAGE I) of the hybrid optimisation utilised the DE algorithm. The objective function was set to reduce the initial cost (!"#"$%&) of the RF-PMSG, by reducing the active material mass ('()$"*+) in the generator, i.e. NdFeB PM mass (',-), copper mass (').), and active steel in the stator lamination and rotor core ('/0$%&1$++&), while maintaining a pmsg efficiency (23456 ≥ 97%). The initial stage saw a reduction in initial cost by 25.5%, while maintaining an efficiency of 23456 = 97.8%. The final stage (STAGE II) of the hybrid optimisation utilising the Taguchi method, to make improvements on the performance of the machine, by optimising the Torque and back EMF characteristics while further reducing the NdFeB PM mass. The Magnet Fill Factor (APM), the Slot opening (bs0), the thickness of the permanent magnet poles (ℎ34) and the equivalent length of the air gap (?6) were used as optimisation variables. The final stage saw a decrease in cogging torque (@)06) by 53.4%, an increase in average torque (@%*) by 1.1%, a reduction in the total harmonic distortion of the back EMF (@AB) by 8.0%, a reduction in the required mass of the NdFeB permanent magnet material by 12.43%, while maintaining a torque ripple (@C"3) < 10%. The RF-PMSG characteristics optimised using the hybrid analytical-numerical optimisation were hypothesised to contribute in a reduction of the LCOE of offshore wind energy both in terms of Operational expenditure (OPEX) and Capital expenditure (CAPEX).

Keywords

Electrical Engineering

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