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Energies 2017, 10(8), 1210;

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
IBM Research–China, Shanghai 201203, China
Concord New Energy Group Limited–China, Beijing 100048, China
Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA
Author to whom correspondence should be addressed.
Academic Editor: Marco Mussetta
Received: 13 April 2017 / Revised: 31 July 2017 / Accepted: 10 August 2017 / Published: 15 August 2017
(This article belongs to the Special Issue Wind Generators Modelling and Control)
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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%. View Full-Text
Keywords: wind turbine; generator; prediction; diagnosis; SCADA wind turbine; generator; prediction; diagnosis; SCADA

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Zhao, Y.; Li, D.; Dong, A.; Kang, D.; Lv, Q.; Shang, L. Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data. Energies 2017, 10, 1210.

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