The wind sectors pends roughly 2200M€ in repair the wind turbines failures. These failures do not contribute to the goal of reducing greenhouse gases emissions. The 25–35% of the generation costs are operation and maintenance services. To reduce this amount, the wind turbine industry is backing on the Machine Learning techniques over SCADA data. This data can contain errors produced by missing entries, uncalibrated sensors or human errors. Each kind of error must be handled carefully because extreme values are not always produced by data reading errors or noise. This document evaluates the impact of removing extreme values (outliers) applying several widely used techniques like Quantile, Hampel and ESD with the recommended cut-off values. Experimental results on real data show that removing outliers systematically is not a good practice. The use of manually defined ranges (static and dynamic) could be a better filtering strategy.

风力涡轮机维修风力涡轮机故障的费用大约为22亿欧元。这些失败无助于减少温室气体排放的目标。发电成本的25–35％为运营和维护服务。为了减少这一数量，风力涡轮机行业正在支持基于SCADA数据的机器学习技术。此数据可能包含因缺少条目，未校准的传感器或人为错误而产生的错误。每种错误都必须谨慎处理，因为数据读取错误或噪声并不总是产生极值。本文档使用推荐的截止值，采用分位数，Hampel和ESD等几种广泛使用的技术，评估了消除极值（异常值）的影响。实际数据的实验结果表明，系统地删除异常值不是一个好习惯。