Improved power curve monitoring of wind turbines

被引：21

作者：

Morshedizadeh M. ^{[1
]}

Kordestani M. ^{[2
]}

Carriveau R. ^{[1
]}

Ting D.S.K. ^{[1
]}

Saif M. ^{[2
]}

机构：

[1] Turbulence and Energy Laboratory, University of Windsor, Windsor, ON

[2] Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON

来源：

Wind Engineering | 2017年 / 41卷 / 04期

关键词：

artificial neural networks; Performance monitoring; power curve; Supervisory Control And Data Acquisition; wind turbines;

D O I：

10.1177/0309524X17709730

中图分类号：

学科分类号：

摘要：

Wind turbine power output monitoring can detect anomalies in turbine performance which have the potential to result in unexpected failure. This study examines common Supervisory Control And Data Acquisition data over a period of 20 months. It is common to have more than 150 signals acquired by Supervisory Control And Data Acquisition systems, and applying all is neither practical nor useful. Thus, to address the issue, correlation coefficients analysis has been applied in this work to reveal the most influential parameters on wind turbine active power. Then, radial basis function and multilayer perception artificial neural networks are set up, and their performance is compared in two static and dynamic states. The proposed combination of the feature selection method and the dynamic multilayer perception neural network structure has performed well with favorable prediction error levels compared to other methods. Thus, the combination may be a valuable tool for turbine power curve monitoring. © 2017, © The Author(s) 2017.

引用

页码：260 / 271

页数：11

共 40 条

[1]

Akande K.O., Owolabi T.O., Olatunji S.O., Investigating the effect of correlation-based feature selection on the performance of support vector machines in reservoir characterization, Journal of Natural Gas Science and Engineering, 22, pp. 515-522, (2015)

[2]

Caselitz P., Giebhardt J., Advanced maintenance and repair for offshore wind farms using fault prediction techniques, (2002)

[3]

Chen W.H., Chen C.Y., Huang C.Y., Et al., Power output analysis and optimization of two straight-bladed vertical-axis wind turbines, Applied Energy, 185, pp. 223-232, (2017)

[4]

Ching W.K., Li L., Tsing N.K., Et al., A weighted local least squares imputation method for missing value estimation in microarray gene expression data, International Journal of Data Mining and Bioinformatics, 4, 3, pp. 331-347, (2010)

[5]

Gatzert N., Kosub T., Risks and risk management of renewable energy projects: The case of onshore and offshore wind parks, Renewable and Sustainable Energy Reviews, 60, pp. 982-998, (2016)

[6]

Ghaemi M., Feizi-Derakhshi M.R., Feature selection using forest optimization algorithm, Pattern Recognition, 60, pp. 121-129, (2016)

[7]

Giahi M.H., Dehkordi A.J., Investigating the influence of dimensional scaling on aerodynamic characteristics of wind turbine using CFD simulation, Renewable Energy, 97, pp. 162-168, (2016)

[8]

Heier S., Grid Integration of Wind Energy Conversion Systems, (1998)

[9]

Heng A., Zhang S., Tan A.C., Et al., Rotating machinery prognostics: State of the art, challenges and opportunities, Mechanical Systems and Signal Processing, 23, 3, pp. 724-739, (2009)

[10]

Jafarnejadsani H., Pieper J., Ehlers J., Adaptive control of a variable-speed variable-pitch wind turbine using radial-basis function neural network, IEEE Transactions on Control Systems Technology, 21, 6, pp. 2264-2272, (2013)

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