### Abstract

Supergen Exemplar 5 MW wind turbine model, with results indicating that this is a potential low cost method for detecting changes in aerodynamic efficiency and drivetrain losses. The GP approach is benchmarked against standard least-squares (LS) regression, with the GP shown to be the superior method in this case.

Language | English |
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Article number | 032024 |

Number of pages | 8 |

Journal | Journal of Physics: Conference Series |

Volume | 1037 |

Early online date | 19 Jun 2018 |

DOIs | |

Publication status | E-pub ahead of print - 19 Jun 2018 |

Event | The Science of Making Torque from Wind 2018 - Politecnico di Milano, Milan, Italy Duration: 20 Jun 2018 → 22 Jun 2018 http://www.torque2018.org/ |

### Fingerprint

### Keywords

- polynomial regression problem
- maximum efficiency power coefficient
- Cp,max
- Gaussian process (GP) machine learning

### Cite this

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**Wind turbine Cpmax and drivetrain-losses estimation using Gaussian process machine learning.** / Hart, E; Leithead, W E; Feuchtwang, J.

Research output: Contribution to journal › Conference Contribution

TY - JOUR

T1 - Wind turbine Cpmax and drivetrain-losses estimation using Gaussian process machine learning

AU - Hart, E

AU - Leithead, W E

AU - Feuchtwang, J

PY - 2018/6/19

Y1 - 2018/6/19

N2 - In this paper it is shown that measured data in a wind turbine, available to the controller, can be formulated into a polynomial regression problem in order to estimate the turbine's maximum efficiency power coefficient, Cpmax, and drivetrain losses, assuming the latter can be well approximated as being linear. Gaussian process (GP) machine learning is used for the regression problem. These formulations are tested on data generated using theSupergen Exemplar 5 MW wind turbine model, with results indicating that this is a potential low cost method for detecting changes in aerodynamic efficiency and drivetrain losses. The GP approach is benchmarked against standard least-squares (LS) regression, with the GP shown to be the superior method in this case.

AB - In this paper it is shown that measured data in a wind turbine, available to the controller, can be formulated into a polynomial regression problem in order to estimate the turbine's maximum efficiency power coefficient, Cpmax, and drivetrain losses, assuming the latter can be well approximated as being linear. Gaussian process (GP) machine learning is used for the regression problem. These formulations are tested on data generated using theSupergen Exemplar 5 MW wind turbine model, with results indicating that this is a potential low cost method for detecting changes in aerodynamic efficiency and drivetrain losses. The GP approach is benchmarked against standard least-squares (LS) regression, with the GP shown to be the superior method in this case.

KW - polynomial regression problem

KW - maximum efficiency power coefficient

KW - Cp,max

KW - Gaussian process (GP) machine learning

UR - http://iopscience.iop.org/journal/1742-6596

U2 - 10.1088/1742-6596/1037/3/032024

DO - 10.1088/1742-6596/1037/3/032024

M3 - Conference Contribution

VL - 1037

JO - Journal of Physics: Conference Series

T2 - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

M1 - 032024

ER -