Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

Yingying Zhao, Dongsheng Li, Ao Dong, Dahai Kang, Qin Lv and Li Shang
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Yingying Zhao: Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
Dongsheng Li: IBM Research–China, Shanghai 201203, China
Ao Dong: Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
Dahai Kang: Concord New Energy Group Limited–China, Beijing 100048, China
Qin Lv: Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA
Li Shang: Department of Computer Science and Technology, Tongji University, Shanghai 201804, China

Energies, 2017, vol. 10, issue 8, 1-17

Abstract: The fast-growing wind power industry faces the challenge of reducing operation and maintenance (O&M) costs for wind power plants. Predictive maintenance is essential to improve wind turbine reliability and prolong operation time, thereby reducing the O&M cost for wind power plants. This study presents a solution for predictive maintenance of wind turbine generators. The proposed solution can: (1) predict the remaining useful life (RUL) of wind turbine generators before a fault occurs and (2) diagnose the state of the wind turbine generator when the fault occurs. Moreover, the proposed solution implies low-deployment costs because it relies solely on the information collected from the widely available supervisory control and data acquisition (SCADA) system. Extra sensing hardware is needless. The proposed solution has been deployed and evaluated in two real-world wind power plants located in China. The experimental study demonstrates that the RUL of the generators can be predicted 18 days ahead with about an 80% prediction accuracy. When faults occur, the specific type of generator fault can be diagnosed with an accuracy of 94%.

Keywords: wind turbine; generator; prediction; diagnosis; 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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30)

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