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Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning

Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning
Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning
Wind turbine flow field prediction is difficult as it requires computationally expensive computational fluid dynamics (CFD) models. The contribution of this paper is to propose and develop a method for stochastic analysis of an offshore wind farm using CFD and a non-intrusive stochastic expansion. The approach is developed through testing a range of machine-learning methods, evaluating dataset requirements and comparing the accuracy against site measurement data. The approach used is detailed and the results are compared with real measurements obtained from the existing wind farm to quantify the accuracy of the predictions. An existing offshore wind farm is modelled using a steady-state CFD solver at several deterministic input ranges and an approximation model is trained on the CFD results. The approximation models compared are Artificial Neural Networks, Gaussian Process, Radial Basis Function, Random Forest and Support Vector Regression. RBF achieves a mean absolute error relative to the CFD model of only 0.54% and the error of the SVR predictions relative to the real data, with scatter, was 12%. A Jensen model is used for comparison and achieves an error of 16%. This approach has the potential to be used in more complex situations where an existing analytical method is either insufficient or unable to make a good prediction.
Artificial Intelligence, Computational Fluid Dynamics, Wind Turbines; Power Prediction
0960-1481
Richmond, M.
22a978f6-8b05-4d75-97e6-c1cb9c034c50
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Pandit, R
64e80cdb-cf6a-4135-b3b5-525fc8f2572c
Kolios, A
62ddacfa-38ce-40d5-8a55-fa4d539806cb
Richmond, M.
22a978f6-8b05-4d75-97e6-c1cb9c034c50
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Pandit, R
64e80cdb-cf6a-4135-b3b5-525fc8f2572c
Kolios, A
62ddacfa-38ce-40d5-8a55-fa4d539806cb

Richmond, M., Sobey, Adam, Pandit, R and Kolios, A (2020) Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning. Renewable Energy, 161. (doi:10.1016/j.renene.2020.07.083).

Record type: Article

Abstract

Wind turbine flow field prediction is difficult as it requires computationally expensive computational fluid dynamics (CFD) models. The contribution of this paper is to propose and develop a method for stochastic analysis of an offshore wind farm using CFD and a non-intrusive stochastic expansion. The approach is developed through testing a range of machine-learning methods, evaluating dataset requirements and comparing the accuracy against site measurement data. The approach used is detailed and the results are compared with real measurements obtained from the existing wind farm to quantify the accuracy of the predictions. An existing offshore wind farm is modelled using a steady-state CFD solver at several deterministic input ranges and an approximation model is trained on the CFD results. The approximation models compared are Artificial Neural Networks, Gaussian Process, Radial Basis Function, Random Forest and Support Vector Regression. RBF achieves a mean absolute error relative to the CFD model of only 0.54% and the error of the SVR predictions relative to the real data, with scatter, was 12%. A Jensen model is used for comparison and achieves an error of 16%. This approach has the potential to be used in more complex situations where an existing analytical method is either insufficient or unable to make a good prediction.

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Stochastic_assessment_based_on_machine-learning_revision - Accepted Manuscript
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Accepted/In Press date: 17 July 2020
e-pub ahead of print date: 1 August 2020
Keywords: Artificial Intelligence, Computational Fluid Dynamics, Wind Turbines; Power Prediction

Identifiers

Local EPrints ID: 446329
URI: http://eprints.soton.ac.uk/id/eprint/446329
ISSN: 0960-1481
PURE UUID: a03f28c4-bbd9-425c-a132-eee422d123c3
ORCID for Adam Sobey: ORCID iD orcid.org/0000-0001-6880-8338

Catalogue record

Date deposited: 04 Feb 2021 17:33
Last modified: 18 Feb 2021 17:09

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Contributors

Author: M. Richmond
Author: Adam Sobey ORCID iD
Author: R Pandit
Author: A Kolios

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