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Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination^{ †}

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## Abstract

**:**

## 1. Introduction

## 2. Data and Method

## 3. Proposed Procedure

## 4. Case Study

## 5. Concluding Remarks

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

WTs | Wind Turbines |

DBSCAN | Density-Based Spatial Clustering of Applications with Noise |

RMSE | Root Mean Squared Error |

MAE | Mean Absolute Error |

${R}^{2}$ | Coefficient of Determination |

GOF | Goodness-of-Fit |

## References

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**Figure 2.**Observations of Power Output against Wind Speed. (

**a**) 1.5 MW Wind Turbine; (

**b**) 3.0 MW Wind Turbine.

**Figure 8.**Fitted Functions of Power Curve with Refined Data. (

**a**) 1.5 MW Wind Turbine; (

**b**) 3.0 MW Wind Turbine.

Turbine | Models | RMSE | MAE | ${\mathit{R}}^{2}$ |
---|---|---|---|---|

1.5 MW | Polynominal | 0.0855 | 0.0618 | 0.9525 |

Gompertz | 0.0889 | 0.0663 | 0.9487 | |

Logistics | 0.0871 | 0.0641 | 0.9507 | |

Weibull | 0.0878 | 0.0611 | 0.9530 | |

3.0 MW | Polynominal | 0.1644 | 0.0981 | 0.9567 |

Gompertz | 0.1643 | 0.0976 | 0.9567 | |

Logistics | 0.1654 | 0.0995 | 0.9567 | |

Weibull | 0.1660 | 0.0998 | 0.9559 |

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**MDPI and ACS Style**

Paik, C.; Chung, Y.; Kim, Y.J.
Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination. *Appl. Syst. Innov.* **2023**, *6*, 41.
https://doi.org/10.3390/asi6020041

**AMA Style**

Paik C, Chung Y, Kim YJ.
Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination. *Applied System Innovation*. 2023; 6(2):41.
https://doi.org/10.3390/asi6020041

**Chicago/Turabian Style**

Paik, Chunhyun, Yongjoo Chung, and Young Jin Kim.
2023. "Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination" *Applied System Innovation* 6, no. 2: 41.
https://doi.org/10.3390/asi6020041