Considerations for Thrust Coefficient Constraints on Axial Induction-Based Optimization
Pitching turbine blades into the wind increases the thrust coefficient, <inline-formula> <tex-math notation="LaTeX">$C_{T}$ </tex-math></inline-formula>, which increases the power generated by the wind turbine. However, excessive <inline-formula> <tex-math...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10359510/ |
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| author | Mfon O. Charles David T. O. Oyedokun Mqhele E. Dlodlo |
| author_facet | Mfon O. Charles David T. O. Oyedokun Mqhele E. Dlodlo |
| author_sort | Mfon O. Charles |
| collection | DOAJ |
| description | Pitching turbine blades into the wind increases the thrust coefficient, <inline-formula> <tex-math notation="LaTeX">$C_{T}$ </tex-math></inline-formula>, which increases the power generated by the wind turbine. However, excessive <inline-formula> <tex-math notation="LaTeX">$C_{T}$ </tex-math></inline-formula> increments beyond rotor mean wind speed <inline-formula> <tex-math notation="LaTeX">$C_{T}$ </tex-math></inline-formula>-equivalent, tend to cause overexertion and increased loads. Consequently, the rated operational lifetime of the turbine is reduced. This study uses a high-fidelity 2-D Gaussian wake model and an augmented version of Frandsen’s turbulence intensity (TI) model to simulate a hexagonally deployed wind plant (WP) operation. Turbines’ axial-induction factor <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> is optimised using Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA), to maximise WP power and annual energy production (AEP), with constrains on individual turbine <inline-formula> <tex-math notation="LaTeX">$C_{T}$ </tex-math></inline-formula> values to remain within rotor wind speed equivalent based on turbine’s thrust curve. At a <inline-formula> <tex-math notation="LaTeX">$5D$ </tex-math></inline-formula> minimum turbine-to-turbine (T-2-T) separation distance, results show that <inline-formula> <tex-math notation="LaTeX">$C_{T}$ </tex-math></inline-formula> constraints on individual turbines increased the wind speed range of healthy operations by up to 66.67% considering extreme loads. AEP gains reduced from 11.91% and 13.25% (optimised without constraints), to approximately 7.59% and 5.74% (with constraints), when compared to the corresponding <inline-formula> <tex-math notation="LaTeX">$5D$ </tex-math></inline-formula> Base case (non-optimised and unconstrained), using PSO and GA, respectively. The study also shows that WP power maximisation can increase turbulence intensity levels within the WP especially if turbines are tightly deployed. The outcome of this study has implications for new wind farm layouts and wind plant power optimization. |
| first_indexed | 2024-03-08T16:33:48Z |
| format | Article |
| id | doaj.art-fac3da1b495541a58d59c739cb42574a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T16:33:48Z |
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| spelling | doaj.art-fac3da1b495541a58d59c739cb42574a2024-01-06T00:01:51ZengIEEEIEEE Access2169-35362024-01-011282483910.1109/ACCESS.2023.334291910359510Considerations for Thrust Coefficient Constraints on Axial Induction-Based OptimizationMfon O. Charles0https://orcid.org/0000-0003-1603-0733David T. O. Oyedokun1https://orcid.org/0000-0002-8025-0143Mqhele E. Dlodlo2https://orcid.org/0000-0003-2364-2957Department of Electrical Engineering, University of Cape Town, Cape Town, South AfricaDepartment of Electrical Engineering, University of Cape Town, Cape Town, South AfricaNational University of Science and Technology (NUST), Bulawayo, ZimbabwePitching turbine blades into the wind increases the thrust coefficient, <inline-formula> <tex-math notation="LaTeX">$C_{T}$ </tex-math></inline-formula>, which increases the power generated by the wind turbine. However, excessive <inline-formula> <tex-math notation="LaTeX">$C_{T}$ </tex-math></inline-formula> increments beyond rotor mean wind speed <inline-formula> <tex-math notation="LaTeX">$C_{T}$ </tex-math></inline-formula>-equivalent, tend to cause overexertion and increased loads. Consequently, the rated operational lifetime of the turbine is reduced. This study uses a high-fidelity 2-D Gaussian wake model and an augmented version of Frandsen’s turbulence intensity (TI) model to simulate a hexagonally deployed wind plant (WP) operation. Turbines’ axial-induction factor <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> is optimised using Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA), to maximise WP power and annual energy production (AEP), with constrains on individual turbine <inline-formula> <tex-math notation="LaTeX">$C_{T}$ </tex-math></inline-formula> values to remain within rotor wind speed equivalent based on turbine’s thrust curve. At a <inline-formula> <tex-math notation="LaTeX">$5D$ </tex-math></inline-formula> minimum turbine-to-turbine (T-2-T) separation distance, results show that <inline-formula> <tex-math notation="LaTeX">$C_{T}$ </tex-math></inline-formula> constraints on individual turbines increased the wind speed range of healthy operations by up to 66.67% considering extreme loads. AEP gains reduced from 11.91% and 13.25% (optimised without constraints), to approximately 7.59% and 5.74% (with constraints), when compared to the corresponding <inline-formula> <tex-math notation="LaTeX">$5D$ </tex-math></inline-formula> Base case (non-optimised and unconstrained), using PSO and GA, respectively. The study also shows that WP power maximisation can increase turbulence intensity levels within the WP especially if turbines are tightly deployed. The outcome of this study has implications for new wind farm layouts and wind plant power optimization.https://ieeexplore.ieee.org/document/10359510/Annual energy productionwind plantaxial induction factorpower maximisationthrust coefficient constrainturbulence intensity |
| spellingShingle | Mfon O. Charles David T. O. Oyedokun Mqhele E. Dlodlo Considerations for Thrust Coefficient Constraints on Axial Induction-Based Optimization IEEE Access Annual energy production wind plant axial induction factor power maximisation thrust coefficient constrain turbulence intensity |
| title | Considerations for Thrust Coefficient Constraints on Axial Induction-Based Optimization |
| title_full | Considerations for Thrust Coefficient Constraints on Axial Induction-Based Optimization |
| title_fullStr | Considerations for Thrust Coefficient Constraints on Axial Induction-Based Optimization |
| title_full_unstemmed | Considerations for Thrust Coefficient Constraints on Axial Induction-Based Optimization |
| title_short | Considerations for Thrust Coefficient Constraints on Axial Induction-Based Optimization |
| title_sort | considerations for thrust coefficient constraints on axial induction based optimization |
| topic | Annual energy production wind plant axial induction factor power maximisation thrust coefficient constrain turbulence intensity |
| url | https://ieeexplore.ieee.org/document/10359510/ |
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