Abstract
The conventional means of assessing performance of a wind turbine is through consideration of its power curve. However, this representation fails to capture plausibility of measurement and cannot provide anomaly detection capabilities, which may assist in the detection of plant degradation. Although the probabilistic form of the power curve is complex, Copula models are presented here as a means of expressing the operational power curve as a joint distribution of wind speed and power output. This probabilistic model is demonstrated as an efficient way to remove outliers from operational SCADA data, simplifying and accelerating the process of identifying plant maloperation.
| Original language | English |
|---|---|
| Number of pages | 1 |
| Publication status | Published - 19 Jun 2018 |
| Event | 2018 Global Offshore Wind - Manchester Central , Manchester, United Kingdom Duration: 19 Jun 2018 → 20 Jun 2018 https://events.renewableuk.com/gow18 |
Conference
| Conference | 2018 Global Offshore Wind |
|---|---|
| Abbreviated title | gow18 |
| Country | United Kingdom |
| City | Manchester |
| Period | 19/06/18 → 20/06/18 |
| Internet address |
Keywords
- wind turbine
- performance
- power curve
- copula model
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Zorzi, G., Stephen, B., & McMillan, D. (2018). Wind turbine performance assessment & power curve outlier rejection using copula modelling. Poster session presented at 2018 Global Offshore Wind, Manchester, United Kingdom.