A Parameter Selection Method for Wind Turbine Health Management through SCADA Data
Mian Du,
Jun Yi,
Peyman Mazidi,
Lin Cheng and
Jianbo Guo
Additional contact information
Mian Du: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Jun Yi: China Electric Power Research Institute, Beijing 100192, China
Peyman Mazidi: Department of Electric Power and Energy Systems (EPE), KTH Royal Institute of Technology, Stockholm 10044, Sweden
Lin Cheng: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Jianbo Guo: China Electric Power Research Institute, Beijing 100192, China
Energies, 2017, vol. 10, issue 2, 1-14
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.
Keywords: wind turbine; failure detection; SCADA data; feature extraction; mutual information; copula (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
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