Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling

Hong Wang, Hongbin Wang, Guoqian Jiang, Jimeng Li and Yueling Wang
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Hong Wang: School of Electrical Engineering, Yanshan University, No. 438, Hebei Avenue, Qinhuangdao 066004, China
Hongbin Wang: School of Electrical Engineering, Yanshan University, No. 438, Hebei Avenue, Qinhuangdao 066004, China
Guoqian Jiang: School of Electrical Engineering, Yanshan University, No. 438, Hebei Avenue, Qinhuangdao 066004, China
Jimeng Li: School of Electrical Engineering, Yanshan University, No. 438, Hebei Avenue, Qinhuangdao 066004, China
Yueling Wang: School of Electrical Engineering, Yanshan University, No. 438, Hebei Avenue, Qinhuangdao 066004, China

Energies, 2019, vol. 12, issue 6, 1-22

Abstract: Health monitoring and early fault detection of wind turbines have attracted considerable attention due to the benefits of improving reliability and reducing the operation and maintenance costs of the turbine. However, dynamic and constantly changing operating conditions of wind turbines still pose great challenges to effective and reliable fault detection. Most existing health monitoring approaches mainly focus on one single operating condition, so these methods cannot assess the health status of turbines accurately, leading to unsatisfactory detection performance. To this end, this paper proposes a novel general health monitoring framework for wind turbines based on supervisory control and data acquisition (SCADA) data. A key feature of the proposed framework is that it first partitions the turbine operation into multiple sub-operation conditions by the clustering approach and then builds a normal turbine behavior model for each sub-operation condition. For normal behavior modeling, an optimized deep belief network is proposed. This optimized modeling method can capture the sophisticated nonlinear correlations among different monitoring variables, which is helpful to enhance the prediction performance. A case study of main bearing fault detection using real SCADA data is used to validate the proposed approach, which demonstrates its effectiveness and advantages.

Keywords: wind turbines; health monitoring; fault detection; optimized deep belief networks; supervisory control and data acquisition system; multioperation condition (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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