The swift growth of the wind power industry necessitates comprehensive evaluation and efficient fault prediction of wind turbines. Given the challenges of integration and optimization of reliability evaluation and fault prediction models, a systematic method of reliability fuzzy evaluation and fault prediction based on the Supervisory Control and Data Acquisition (SCADA) data is proposed. A mid-to-long-term reliability fuzzy evaluation model is constructed using Fuzzy Comprehensive Evaluation (FCE). The mid-term evaluation results in ten failure modes reveal that the model's hazard ranking results match the situation better than the RPN method. And the long-term evaluation results of 5 years in the operating mode show that the model effectively gathers the evaluation information each year and provides a clear and accurate reflection of reliability. Meanwhile, fault prediction is studied using alarm logs because they are better at expressing the status of wind turbines than monitoring data. And the tree-based algorithms and unsupervised statistical learning methods are used to mine the mapping relationship between input variables and predefined tags. The fault prediction achieves both accuracy and recall of 0.784 and saves over 163k Euros based on local wind turbine maintenance expenditures. Overall, the reliability evaluation and fault prediction complement each other, which may either affect future wind farm management or prevent unnecessary maintenance costs.

中文翻译：

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风电机组模糊可靠性评估和基于机器学习的故障预测

风电产业的快速发展需要对风机进行全面的评估和高效的故障预测。针对可靠性评估和故障预测模型集成和优化的挑战，提出了一种基于监控和​​数据采集（SCADA）数据的可靠性模糊评估和故障预测的系统方法。利用模糊综合评价法(FCE)构建了中长期可靠性模糊评价模型。十种失效模式的中期评估结果表明，该模型的危险排序结果比RPN方法更符合实际情况。运行模式5年的长期评估结果表明，该模型有效地收集了每年的评估信息，清晰、准确地反映了可靠性。同时，利用报警日志来研究故障预测，因为它们比监测数据更能表达风力发电机的状态。并使用基于树的算法和无监督统计学习方法来挖掘输入变量和预定义标签之间的映射关系。故障预测准确率和召回率均达到0.784，根据当地风机维护支出节省超过16.3万欧元。总体而言，可靠性评估和故障预测是相辅相成的，这可能会影响未来风电场的管理或避免不必要的维护成本。