# A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

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## Abstract

**:**

## 1. Introduction

## 2. The Test Case

^{TM}and Opti-speed

^{TM}control mechanisms. The control mechanisms aim to maximise energy capture at wind speeds below the rated power wind speed and to fix the power output to rated power at wind speeds above the rated power wind speed. In normal turbine operation the blade pitch angle is always below 20 degrees. In a fault condition or a pause/stop state, the blade pitch angle is fixed to approximately 86 degrees. Time series data of a number of turbine parameters are logged by the wind turbine SCADA system in 10-minute mean values. These parameters include: wind speed, wind speed standard deviation, absolute wind direction, relative wind direction, rotor RPM, generator RPM, blade pitch angle and power output. A number of 10-minute mean temperature parameters are also logged and include: gearbox oil temperature, gearbox bearing temperatures, generator bearing temperatures, internal nacelle temperature and external ambient air temperature at hub height.

## 3. Methods

- Wind turbines are typically equipped with cup anemometers mounted behind the rotor and the undisturbed wind speed is ex-post reconstructed through a nacelle transfer function;
- The power curve has non-trivial seasonal and ambient conditions dependence.

#### 3.1. Support Vector Machine Regression

- Undisturbed wind speed average, estimated by the control system through the nacelle transfer function;
- Turbulence intensity, estimated as the ratio between the standard deviation and the average of the undisturbed wind speed;
- Yaw angle;
- Blade pitch angle;
- Rotor speed;
- Generator speed.

#### 3.2. The Data Sets Arrangement and the Performance Monitoring

- ${\mathrm{D}}_{2008}$;
- ${\mathrm{D}}_{2012}$;
- ${\mathrm{D}}_{2013}$;
- ${\mathrm{D}}_{2014}$;
- ${\mathrm{D}}_{2017}$;
- ${\mathrm{D}}_{2018}$.

- ${\mathrm{D}}_{2008}$ is randomly divided in two subsets: D0 (a random selection of $\frac{2}{3}$ of the data set) and D1 (the remainder $\frac{1}{3}$ of the data set). D0 is used for training the regression, D1 is used for testing the regression. The convergence of model training is obtained through the MATLAB
^{®}$fitrsvm$ routine. - A data set posterior to ${\mathrm{D}}_{2008}$ is named as D2 and is used to quantify the performance deviation with respect to 2008.

#### 3.3. Power Curve Analysis

^{®}$fitdist$ function that is based on the statistical method of moments.

## 4. Results

#### 4.1. Regression Results

#### 4.2. Analysis before and after Gearbox Replacement

#### 4.3. Power Curve Analysis and Production Assessment

## 5. Conclusions

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**The average difference R between power measurement Y and estimation $\widehat{Y}$ (Equation (14)). Data sets, respectively: 2008 vs. 2012, 2013, 2014, 2015, 2017, 2018.

**Figure 5.**The average difference R between power measurement Y and estimation $\widehat{Y}$ (Equation (14)). Data sets: 2017–2018 and 2019.

**Figure 6.**The average difference R between power measurement Y and estimation $\widehat{Y}$ (Equation (14)). Data sets: 2008 and 2019.

Specification | Data |
---|---|

Model | PLH-400V52 |

Rated Power | 935 kW |

Rated RPM (Low speed shaft) | 26 min${}^{-1}$ |

Gearing ratio | 61.799 |

Weight | 5400 kg |

Parameter | Logging Interval | Years Available |
---|---|---|

Wind Speed (m/s) | 10 min. | 2008, 2014, 2017–2018 (12 months), 2019 (6 months) |

Wind Speed Standard Deviation (m/s) | 10 min. | 2008, 2014, 2017–2018 (12 months), 2019 (6 months) |

Wind Direction (deg) | 10 min. | 2008, 2014, 2017–2018 (12 months), 2019 (6 months) |

Ambient Temperature (°C) | 10 min. | 2008, 2014, 2017–2018 (12 months), 2019 (6 months) |

Rotor Speed (RPM) | 10 min. | 2008, 2014, 2017–2018 (12 months), 2019 (6 months) |

Blade Pitch Angle (deg) | 10 min. | 2008, 2014, 2017–2018 (12 months), 2019 (6 months) |

Generator Speed (RPM) | 10 min. | 2008, 2014, 2017–2018 (12 months), 2019 (6 months) |

Power (kW) | 10 min. | 2008, 2014, 2017–2018 (12 months), 2019 (6 months) |

Gear oil Temperature (°C) | 10 min. | 2008, 2014, 2017–2018 (12 months), 2019 (6 months) |

Data Set | $\mathsf{\Delta}$ |
---|---|

2012 vs. 2008 | −0.1% |

2013 vs. 2008 | −0.7% |

2014 vs. 2008 | −0.4% |

2015 vs. 2008 | −1.1% |

2017 vs. 2008 | −6.2% |

2018 vs. 2008 | −4.0% |

**Table 4.**Estimate of the performance change before and after gearbox replacement. (below rated power).

Data Set | $\mathsf{\Delta}$ |
---|---|

2019 vs. 2017–2018 | +1.9% |

**Table 5.**Estimate of the performance change: 2008 vs. 2019 after gearbox replacement. (below rated power).

Data Set | $\mathsf{\Delta}$ |
---|---|

2008 vs. 2019 | −2.9% |

Year | Uave (m/s) | Weibull A (m/s) | Weibull k | Power Density (W/m${}^{2}$) |
---|---|---|---|---|

2008 | 6.2 | 6.95 | 1.91 | 280.5 |

2014 | 5.8 | 6.54 | 1.93 | 227.2 |

2017–2018 | 5.6 | 6.32 | 2.04 | 196.6 |

Wind Year | Power Curve | AEP (kWh) | $\mathsf{\Delta}$ AEP (kWh) | % $\mathbf{\Delta}$ AEP |
---|---|---|---|---|

2008 | 2008 | 1,837,900 | - | - |

2014 | 1,810,700 | −27,200 | −1.5% | |

2017–2018 | 1,759,600 | −78,300 | −4.3% | |

2019 | 1,782,200 | −55,700 | −3.0% | |

2014 | 2008 | 1,598,300 | - | - |

2014 | 1,571,500 | −26,800 | −1.7% | |

2017–2018 | 1,520,200 | −78,100 | −4.9% | |

2019 | 1,539,900 | −58,400 | −3.6% | |

2017–2018 (12 months) | 2008 | 1,434,500 | - | - |

2014 | 1,407,300 | −27,200 | −1.9% | |

2017–2018 | 1,354,400 | −80,100 | −5.6% | |

2019 | 1,372,000 | −62,500 | −4.4% |

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**MDPI and ACS Style**

Byrne, R.; Astolfi, D.; Castellani, F.; Hewitt, N.J. A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis. *Energies* **2020**, *13*, 2086.
https://doi.org/10.3390/en13082086

**AMA Style**

Byrne R, Astolfi D, Castellani F, Hewitt NJ. A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis. *Energies*. 2020; 13(8):2086.
https://doi.org/10.3390/en13082086

**Chicago/Turabian Style**

Byrne, Raymond, Davide Astolfi, Francesco Castellani, and Neil J. Hewitt. 2020. "A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis" *Energies* 13, no. 8: 2086.
https://doi.org/10.3390/en13082086