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Abstract
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.
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.
Original language | English |
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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 |
Keywords
- condition monitoring
- Gaussian Process models
- wind turbine anomaly detection
- wind turbine
- SCADA data
- SCADA analysis
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Projects
- 1 Finished
Research Output
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Comparative analysis of Gaussian Process power curve models based on different stationary covariance functions for the purpose of improving model accuracy
Pandit, R. K. & Infield, D., 30 Sep 2019, In: Renewable Energy. 140, p. 190-202 13 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile10 Citations (Scopus)6 Downloads (Pure) -
Comparative analysis of binning and Gaussian Process based blade pitch angle curve of a wind turbine for the purpose of condition monitoring
Pandit, R. K. & Infield, D., 10 Oct 2018, In: Journal of Physics: Conference Series. 1102, 1, 10 p., 012037 .Research output: Contribution to journal › Article › peer-review
Open AccessFile5 Citations (Scopus)16 Downloads (Pure) -
Comparative analysis of binning and support vector regression for wind turbine rotor speed based power curve use in condition monitoring
Pandit, R. & Infield, D., 13 Dec 2018, 2018 53rd International Universities Power Engineering Conference (UPEC). Piscataway, NJ: IEEE, 6 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
Open AccessFile5 Citations (Scopus)16 Downloads (Pure)