T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case
Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/6/1306 |
| _version_ | 1798006134438625280 |
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| author | Christos Galinos Jonas Kazda Wai Hou Lio Gregor Giebel |
| author_facet | Christos Galinos Jonas Kazda Wai Hou Lio Gregor Giebel |
| author_sort | Christos Galinos |
| collection | DOAJ |
| description | Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but sufficiently detailed model for computing the turbine fatigue load. In addition, the effect of turbine curtailment is particularly important in the calculation of the turbine loads. Therefore, this paper develops a fast and computationally efficient method for wind turbine load assessment in a wind farm, including the wake effects. In particular, the turbine fatigue loads are computed using a surrogate model that is based on the turbine operating condition, for example, power set-point and turbine location, and the ambient wind inflow information. The Turbine to Farm Loads (T2FL) surrogate model is constructed based on a set of high fidelity aeroelastic simulations, including the Dynamic Wake Meandering model and an artificial neural network that uses the Bayesian Regularisation (BR) and Levenberg−Marquardt (LM) algorithms. An ensemble model is used that outperforms model predictions of the BR and LM algorithms independently. Furthermore, a case study of a two turbine wind farm is demonstrated, where the turbine power set-point and fatigue loads can be optimised based on the proposed surrogate model. The results show that the downstream turbine producing more power than the upstream turbine is favourable for minimising the load. In addition, simulation results further demonstrate that the accumulated fatigue damage of turbines can be effectively distributed amongst the turbines in a wind farm using the power curtailment and the proposed surrogate model. |
| first_indexed | 2024-04-11T12:51:12Z |
| format | Article |
| id | doaj.art-e8e7859aaef442258d3b9492e37fbd93 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T12:51:12Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e8e7859aaef442258d3b9492e37fbd932022-12-22T04:23:13ZengMDPI AGEnergies1996-10732020-03-01136130610.3390/en13061306en13061306T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine CaseChristos Galinos0Jonas Kazda1Wai Hou Lio2Gregor Giebel3Technical University of Denmark, Department of Wind Energy, Frederiksborgvej 399, 4000 Roskilde, DenmarkTechnical University of Denmark, Department of Wind Energy, Frederiksborgvej 399, 4000 Roskilde, DenmarkTechnical University of Denmark, Department of Wind Energy, Frederiksborgvej 399, 4000 Roskilde, DenmarkTechnical University of Denmark, Department of Wind Energy, Frederiksborgvej 399, 4000 Roskilde, DenmarkWind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but sufficiently detailed model for computing the turbine fatigue load. In addition, the effect of turbine curtailment is particularly important in the calculation of the turbine loads. Therefore, this paper develops a fast and computationally efficient method for wind turbine load assessment in a wind farm, including the wake effects. In particular, the turbine fatigue loads are computed using a surrogate model that is based on the turbine operating condition, for example, power set-point and turbine location, and the ambient wind inflow information. The Turbine to Farm Loads (T2FL) surrogate model is constructed based on a set of high fidelity aeroelastic simulations, including the Dynamic Wake Meandering model and an artificial neural network that uses the Bayesian Regularisation (BR) and Levenberg−Marquardt (LM) algorithms. An ensemble model is used that outperforms model predictions of the BR and LM algorithms independently. Furthermore, a case study of a two turbine wind farm is demonstrated, where the turbine power set-point and fatigue loads can be optimised based on the proposed surrogate model. The results show that the downstream turbine producing more power than the upstream turbine is favourable for minimising the load. In addition, simulation results further demonstrate that the accumulated fatigue damage of turbines can be effectively distributed amongst the turbines in a wind farm using the power curtailment and the proposed surrogate model.https://www.mdpi.com/1996-1073/13/6/1306wind turbinefatigue loadwind farmsurrogate model |
| spellingShingle | Christos Galinos Jonas Kazda Wai Hou Lio Gregor Giebel T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case Energies wind turbine fatigue load wind farm surrogate model |
| title | T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case |
| title_full | T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case |
| title_fullStr | T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case |
| title_full_unstemmed | T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case |
| title_short | T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case |
| title_sort | t2fl an efficient model for wind turbine fatigue damage prediction for the two turbine case |
| topic | wind turbine fatigue load wind farm surrogate model |
| url | https://www.mdpi.com/1996-1073/13/6/1306 |
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