Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects

Zhengnan Hou, Xiaoxiao Lv and Shengxian Zhuang
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Zhengnan Hou: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Xiaoxiao Lv: School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China
Shengxian Zhuang: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Energies, 2021, vol. 14, issue 22, 1-15

Abstract: Wind Turbines (WTs) are exposed to harsh conditions and can experience extreme weather, such as blizzards and cold waves, which can directly affect temperature monitoring. This paper analyzes the effects of ambient conditions on WT monitoring. To reduce these effects, a novel WT monitoring method is also proposed in this paper. Compared with existing methods, the proposed method has two advantages: (1) the changes in ambient conditions are added to the input of the WT model; (2) an Extreme Learning Machine (ELM) optimized by Genetic Algorithm (GA) is applied to construct the WT model. Using Supervisory Control and Data Acquisition (SCADA), compared with the method that does not consider the changes in ambient conditions, the proposed method can reduce the number of false alarms and provide an earlier alarm when a failure does occur.

Keywords: Wind Turbine; temperature monitoring; ambient condition; Extreme Learning Machine; genetic algorithm; SCADA (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: 2021
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