Abstract
In this paper, a model based on a Gaussian Process is constructed for assessing the performance of a turbine. Here, a reference power curve using SCADA datasets from a healthy turbine is developed using a Gaussian Process and then is compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a common issue with wind turbine which causes underperformance, hence it is used as case study to test and validate the algorithm effectiveness.
| Language | English |
|---|---|
| Article number | 023 |
| Number of pages | 8 |
| Journal | International Journal of Prognostics and Health Management |
| Volume | 9 |
| Issue number | 1 |
| Publication status | Published - 20 Jun 2018 |
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Keywords
- condition monitoring
- Gaussian Process models
- wind turbine anomaly detection
- wind turbine
- SCADA data
- SCADA analysis
Cite this
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Performance assessment of a wind turbine using SCADA based Gaussian Process model. / Pandit, Ravi Kumar; Infield, David.
In: International Journal of Prognostics and Health Management, Vol. 9, No. 1, 023, 20.06.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - Performance assessment of a wind turbine using SCADA based Gaussian Process model
AU - Pandit, Ravi Kumar
AU - Infield, David
PY - 2018/6/20
Y1 - 2018/6/20
N2 - Loss of wind turbine power production identified through performance assessment is a useful tool for effective condition monitoring of a wind turbine. Power curves describe the nonlinear relationship between power generation and hub height wind speed and play a significant role in analyzing the performance of a turbine. Performance assessment using nonparametric models is gaining popularity. A Gaussian Process is a nonlinear, non-parametric probabilistic approach widely used for fitting models and forecasting applications due to its flexibility and mathematical simplicity. Its applications extended to both classification and regression related problems. Despite promising results, Gaussian Process application in wind turbine condition monitoring is limited.In this paper, a model based on a Gaussian Process is constructed for assessing the performance of a turbine. Here, a reference power curve using SCADA datasets from a healthy turbine is developed using a Gaussian Process and then is compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a common issue with wind turbine which causes underperformance, hence it is used as case study to test and validate the algorithm effectiveness.
AB - Loss of wind turbine power production identified through performance assessment is a useful tool for effective condition monitoring of a wind turbine. Power curves describe the nonlinear relationship between power generation and hub height wind speed and play a significant role in analyzing the performance of a turbine. Performance assessment using nonparametric models is gaining popularity. A Gaussian Process is a nonlinear, non-parametric probabilistic approach widely used for fitting models and forecasting applications due to its flexibility and mathematical simplicity. Its applications extended to both classification and regression related problems. Despite promising results, Gaussian Process application in wind turbine condition monitoring is limited.In this paper, a model based on a Gaussian Process is constructed for assessing the performance of a turbine. Here, a reference power curve using SCADA datasets from a healthy turbine is developed using a Gaussian Process and then is compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a common issue with wind turbine which causes underperformance, hence it is used as case study to test and validate the algorithm effectiveness.
KW - condition monitoring
KW - Gaussian Process models
KW - wind turbine anomaly detection
KW - wind turbine
KW - SCADA data
KW - SCADA analysis
UR - http://www.phmsociety.org/node/2492
M3 - Article
VL - 9
JO - International Journal of Prognostics and Health Management
T2 - International Journal of Prognostics and Health Management
JF - International Journal of Prognostics and Health Management
SN - 2153-2648
IS - 1
M1 - 023
ER -