Data-driven design of robust fault detection system for wind turbines |
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Affiliation: | 1. Research Center for Supervision, Safety and Automatic Control (CS2AC). Rambla Sant Nebridi, s/n, 08022 Terrassa, Spain;2. Institut de Robòtica i Informàtica Industrial (CSIC-UPC). Carrer LLorens Artigas, 4-6, 08028 Barcelona, Spain |
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Abstract: | In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct final decision. The effectiveness of the proposed approach is finally illustrated by simulations on the wind turbine benchmark model. |
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Keywords: | Data-driven Fault detection Wind turbine Performance index Optimization criterion Robustness |
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