Fault Diagnosis of Wind Turbine Pitch System Based on Support Vector Machine

Zhenzhen ZHANG; Lidong WU; Xiaomin CHEN; Zhixuan XU; Shanqiao CAO

doi:10.16513/j.2096-2185.DE.2106512

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Distributed Energy ›› 2021, Vol. 6 ›› Issue (3) : 70-75. DOI: 10.16513/j.2096-2185.DE.2106512

Application Technology

Fault Diagnosis of Wind Turbine Pitch System Based on Support Vector Machine

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Abstract

The rotor system of wind turbine is the most fragile parts of wind turbine, and accurate diagnosis of its fault categories can improve the effectiveness of wind turbine maintenance. A multi-component fault diagnosis method based on the three-phase currents data of wind turbine was proposed for various fault categories of induction motor and planetary gearbox. The method extracted feature vectors from three-phase currents data by deep auto-encoder and used support vector machine for fault classification. The above method was verified by an example of a rotor driver of wind turbine. The experimental results show that the method can accurately identify and diagnose the fault categories of rotor driver under variable load and variable speed environment.

Key words

pitch system / three-phase current / deep auto-encoder / support vector machine

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Zhenzhen ZHANG , Lidong WU , Xiaomin CHEN , et al . Fault Diagnosis of Wind Turbine Pitch System Based on Support Vector Machine[J]. Distributed Energy Resources. 2021, 6(3): 70-75 https://doi.org/10.16513/j.2096-2185.DE.2106512

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Funding

Science and Technology Function of China Datang Corporation Renewable Energy Science and Technology Research Institute(research and development of new energy monitoring and big data center multi-source fusion remote expert diagnosis system)