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Energies 2017, 10(2), 253; doi:10.3390/en10020253

A Parameter Selection Method for Wind Turbine Health Management through SCADA Data

1
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
2
China Electric Power Research Institute, Beijing 100192, China
3
Department of Electric Power and Energy Systems (EPE), KTH Royal Institute of Technology, Stockholm 10044, Sweden
*
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Received: 14 January 2017 / Revised: 9 February 2017 / Accepted: 14 February 2017 / Published: 20 February 2017
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Abstract

Wind turbine anomaly or failure detection using machine learning techniques through supervisory control and data acquisition (SCADA) system is drawing wide attention from academic and industry While parameter selection is important for modelling a wind turbine’s condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. First, the copula is proven to be capable of simplifying the estimation of mutual information. Then an empirical copulabased mutual information estimation method (ECMI) is introduced for application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough. View Full-Text
Keywords: wind turbine; failure detection; SCADA data; feature extraction; mutual information; copula wind turbine; failure detection; SCADA data; feature extraction; mutual information; copula
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Du, M.; Yi, J.; Mazidi, P.; Cheng, L.; Guo, J. A Parameter Selection Method for Wind Turbine Health Management through SCADA Data. Energies 2017, 10, 253.

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