Performance Assessment of a Wind Turbine Using SCADA based Gaussian Process Model

Ravi Pandit and David Infield
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Full Paper
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642108.
ijphm_18_023.pdf562.93 KBJune 12, 2018 - 2:04am

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.

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Submission Keywords: 
condition monitoring
Gaussian Processes
Predictive Models Evaluation
Wind Turbine
power curve
nonparametric models
performance assessments
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Model-based methods for fault detection, diagnostics, and prognosis
Verification and validation
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