Abstract For over a decade, most wind turbines have worked by adapting their rotation speed to that of the wind. This operating method, now widely used, allows optimal tip speed ratio to be achieved whatever the weather conditions, and in fact produces much better output than stall controlled turbines, particularly in calm weather conditions. However, this improvement means that monitoring systems are required to adapt to constant macroscopic variations in load and speed. In addition, these non-stationary operating conditions make it difficult to undertake machine diagnostics over the long term, due to the fact that the operating conditions in which successive indicators are obtained will almost never be the same. The scientific community has, in many respects, proved the usefulness of regression analysis of these indicators in relation to properly selected variables. The focus of this paper is on regression methods based on machine learning tools, which are becoming more and more popular. The difficulty lies in designing a robust self-adaptive method for estimating the statistical behaviour of an indicator in relation to operating conditions. Indeed, the concern is that indicators may obey disparate and unpredictable multivariate laws: there are many complications which make it difficult to use linear regression tools. Kernel machines, used in this paper as a robust and efficient way of normalising indicators, have proved to be capable of greatly improving a monitoring system’s diagnostic capabilities. The demonstration is based on a practical example: monitoring a bearing defect by analysing the instantaneous angular speed of the wind turbine shaft line. As this defect can only be detected under certain operating conditions – a priori unknown – the chosen example will be particularly effective in highlighting the usefulness of such an approach.

中文翻译：

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使用支持向量回归改进变速风力发电机组的监测指标

摘要 十多年来，大多数风力涡轮机的工作原理是使它们的转速适应风的转速。这种现已广泛使用的操作方法可以在任何天气条件下实现最佳叶尖速比，并且实际上比失速控制涡轮机产生更好的输出，尤其是在平静的天气条件下。然而，这种改进意味着监控系统需要适应负载和速度的持续宏观变化。此外，由于获得连续指标的操作条件几乎永远不会相同，因此这些不稳定的操作条件使得难以进行长期的机器诊断。科学界在许多方面已经 证明了对这些指标进行回归分析与正确选择的变量相关的有用性。本文的重点是基于机器学习工具的回归方法，这些方法越来越流行。难点在于设计一种稳健的自适应方法来估计与操作条件相关的指标的统计行为。事实上，令人担忧的是指标可能遵循不同且不可预测的多元法则：有许多复杂因素使得线性回归工具难以使用。本文中使用的内核机器作为一种稳健有效的指标标准化方法，已被证明能够极大地提高监控系统的诊断能力。该演示基于一个实际示例：通过分析风力涡轮机轴线的瞬时角速度来监测轴承缺陷。由于这种缺陷只能在某些操作条件下被检测到——先验未知——所选择的示例将特别有效地突出这种方法的有用性。