A data-driven model is proposed for the failure rate prediction of offshore wind turbines using the Supervisory Control And Data Acquisition (SCADA) data from onshore and offshore wind farms. The wind turbines are first decomposed into multiple components according to maintenance records. An adaptive weighting algorithm is then developed to assess the relative contributions of reliability-influencing factors to failure rate conversion. Subsequently, a failure rate prediction model is proposed for offshore wind turbines based on transforming onshore device failure rates. The result shows that the failure rate of offshore wind turbines is approximately 23% higher than that of onshore wind turbines. Comparative results confirm that the proposed method generates lower estimation errors than existing approaches.
Kong et al. (Fri,) studied this question.