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Paper Information

Journal:   INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING   2019 , Volume 10 , Number 2; Page(s) 181 To 188.
 
Paper: 

Comparative assessments of binned and support vector regression‑ based blade pitch curve of a wind turbine for the purpose of condition monitoring

 
 
Author(s):  Pandit Ravi Kumar*, Infield David
 
* University of Strathclyde, Glasgow, UK
 
Abstract: 
The unexpected failure of wind turbine components leads to significant downtime and loss of revenue. To prevent this, supervisory control and data acquisition (SCADA) based condition monitoring is considered as a cost-effective approach. In several studies, the wind turbine power curve has been used as a critical indicator for power performance assessment. In contrast, the application of the blade pitch angle curve has hardly been explored for wind turbine condition monitoring purposes. The blade pitch angle curve describes the nonlinear relationship between pitch angle and hub height wind speed and can be used for the detection of faults. A support vector machine (SVM) is an improved version of an artificial neural networks (ANN) and is widely used for classification-and regression-related problems. Support vector regression is a data-driven approach based on statistical learning theory and a structural risk minimization principle which provides useful nonlinear system modeling. In this paper, a support vector regression (a nonparametric machine learning approach)-based pitch curve is presented and its application to anomaly detection explored for wind turbine condition monitoring. A radial basis function (RBF) was used as the kernel function for effective SVR blade pitch curve modeling. This approach is then compared with a binned pitch curve in the identification of operational anomalies. The paper will outline the advantages and limitations of these techniques.
 
Keyword(s): Condition monitoring,Support vector regression,Performance monitoring,Performance curves,Wind turbines
 
 
References: 
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Citations: 
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+ Click to Cite.
APA: Copy

Pandit, R., & Infield, D. (2019). Comparative assessments of binned and support vector regression‑ based blade pitch curve of a wind turbine for the purpose of condition monitoring. INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING, 10(2), 181-188. https://www.sid.ir/en/journal/ViewPaper.aspx?id=668664



Vancouver: Copy

Pandit Ravi Kumar, Infield David. Comparative assessments of binned and support vector regression‑ based blade pitch curve of a wind turbine for the purpose of condition monitoring. INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING. 2019 [cited 2021July24];10(2):181-188. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=668664



IEEE: Copy

Pandit, R., Infield, D., 2019. Comparative assessments of binned and support vector regression‑ based blade pitch curve of a wind turbine for the purpose of condition monitoring. INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING, [online] 10(2), pp.181-188. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=668664.



 
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