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Energies 2017, 10(7), 898; doi:10.3390/en10070898

Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS

1
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China
2
State Grid Lishui Electric Power Supply Company, Lishui 323000, China
3
State Grid Chengdu Power Supply Company, Chengdu 610041, China
*
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Received: 30 March 2017 / Revised: 12 June 2017 / Accepted: 22 June 2017 / Published: 1 July 2017
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Abstract

The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT) faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA) systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS) is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the traditional threshold method. View Full-Text
Keywords: wind turbine; cluster analysis; improved Adaptive Neuro-fuzzy Inference System (ANFIS); fault early warning wind turbine; cluster analysis; improved Adaptive Neuro-fuzzy Inference System (ANFIS); fault early warning
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Zhou, Q.; Xiong, T.; Wang, M.; Xiang, C.; Xu, Q. Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS. Energies 2017, 10, 898.

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