Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment
| dc.contributor.author | Charles, Mfon | |
| dc.contributor.author | Oyedokun, David T O | |
| dc.contributor.author | Dlodlo, Mqhele | |
| dc.date.accessioned | 2021-10-07T13:46:31Z | |
| dc.date.available | 2021-10-07T13:46:31Z | |
| dc.date.issued | 2021-08-12 | |
| dc.date.updated | 2021-08-26T13:27:49Z | |
| dc.description.abstract | Layout optimization is capable of increasing turbine density and reducing wake effects in wind plants. However, such optimized layouts do not guarantee fixed T-2-T distances in any direction and would be disadvantageous if reduction in computational costs due to turbine set-point updates is also a priority. Regular turbine layouts are considered basic because turbine coordinates can be determined intuitively without the application of any optimization algorithms. However, such layouts can be used to intentionally create directions of large T-2-T distances, hence, achieve the gains of standard/non-optimized operations in these directions, while also having close T-2-T distances in other directions from which the gains of optimized operations can be enjoyed. In this study, a regular hexagonal turbine layout is used to deploy turbines within a fixed area dimension, and a turbulence intensity-constrained axial induction-based plant-wide optimization is carried out using particle swarm, artificial bee colony, and differential evolution optimization techniques. Optimized plant power for three close turbine deployments (4<i>D</i>, 5<i>D</i>, and 6<i>D</i>) are compared to a non-optimized 7<i>D</i> deployment using three mean wind inflows. Results suggest that a plant power increase of up to 37% is possible with a 4<i>D</i> deployment, with this increment decreasing as deployment distance increases and as mean wind inflow increases. | en_US |
| dc.identifier | 10.3390/en14164943 | |
| dc.identifier.apacitation | Charles, M., Oyedokun, D. T. O., & Dlodlo, M. (2021). Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment. <i>Energies</i>, 14(16), 4943. http://hdl.handle.net/11427/34197 | en_ZA |
| dc.identifier.chicagocitation | Charles, Mfon, David T O Oyedokun, and Mqhele Dlodlo "Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment." <i>Energies</i> 14, 16. (2021): 4943. http://hdl.handle.net/11427/34197 | en_ZA |
| dc.identifier.citation | Charles, M., Oyedokun, D.T.O. & Dlodlo, M. 2021. Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment. <i>Energies.</i> 14(16):4943. http://hdl.handle.net/11427/34197 | en_ZA |
| dc.identifier.ris | TY - Journal Article AU - Charles, Mfon AU - Oyedokun, David T O AU - Dlodlo, Mqhele AB - Layout optimization is capable of increasing turbine density and reducing wake effects in wind plants. However, such optimized layouts do not guarantee fixed T-2-T distances in any direction and would be disadvantageous if reduction in computational costs due to turbine set-point updates is also a priority. Regular turbine layouts are considered basic because turbine coordinates can be determined intuitively without the application of any optimization algorithms. However, such layouts can be used to intentionally create directions of large T-2-T distances, hence, achieve the gains of standard/non-optimized operations in these directions, while also having close T-2-T distances in other directions from which the gains of optimized operations can be enjoyed. In this study, a regular hexagonal turbine layout is used to deploy turbines within a fixed area dimension, and a turbulence intensity-constrained axial induction-based plant-wide optimization is carried out using particle swarm, artificial bee colony, and differential evolution optimization techniques. Optimized plant power for three close turbine deployments (4<i>D</i>, 5<i>D</i>, and 6<i>D</i>) are compared to a non-optimized 7<i>D</i> deployment using three mean wind inflows. Results suggest that a plant power increase of up to 37% is possible with a 4<i>D</i> deployment, with this increment decreasing as deployment distance increases and as mean wind inflow increases. DA - 2021-08-12 DB - OpenUCT DP - University of Cape Town IS - 16 J1 - Energies LK - https://open.uct.ac.za PY - 2021 T1 - Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment TI - Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment UR - http://hdl.handle.net/11427/34197 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/34197 | |
| dc.identifier.vancouvercitation | Charles M, Oyedokun DTO, Dlodlo M. Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment. Energies. 2021;14(16):4943. http://hdl.handle.net/11427/34197. | en_ZA |
| dc.language.iso | en | en_US |
| dc.publisher.department | Department of Electrical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.source | Energies | en_US |
| dc.source.journalissue | 16 | en_US |
| dc.source.journalvolume | 14 | en_US |
| dc.source.pagination | 4943 | en_US |
| dc.source.uri | https://www.mdpi.com/journal/energies | |
| dc.title | Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment | en_US |
| dc.type | Journal Article | en_US |