This paper presents a simplified automated fault detection scheme for wind turbine induction generators with rotor electrical asymmetries. Fault indicators developed in previous works have made use of the presence of significant spectral peaks in the upper sidebands of the supply frequency harmonics; however, the specific location of these peaks may shift depending on the wind turbine speed. As wind turbines tend to operate under variable speed conditions, it may be difficult to predict where these fault‐related peaks will occur. To accommodate for variable speeds and resulting shifting frequency peak locations, previous works have introduced methods to identify or track the relevant frequencies, which necessitates an additional set of processing algorithms to locate these fault‐related peaks prior to any fault analysis. In this work, a simplified method is proposed to instead bypass the issue of variable speed (and shifting frequency peaks) by introducing a set of bandpass filters that encompass the ranges in which the peaks are expected to occur. These filters are designed to capture the fault‐related spectral information to train a classifier for automatic fault detection, regardless of the specific location of the peaks. Initial experimental results show that this approach is robust against variable speeds and further shows good generalizability in being able to detect faults at speeds and conditions that were not presented during training. After training and tuning the proposed fault detection system, the system was tested on “unseen” data and yielded a high classification accuracy of 97.4%, demonstrating the efficacy of the proposed approach.

中文翻译：

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风力发电机感应发电机的简化自动故障检测

本文提出了一种具有转子电不对称性的风力发电机感应发电机的简化自动故障检测方案。先前工作中开发的故障指示器已经利用了电源频率谐波的上边带中存在的明显频谱峰值。但是，这些峰值的特定位置可能会根据风力涡轮机的速度而变化。由于风力涡轮机倾向于在变速条件下运行，因此可能很难预测这些与故障相关的峰值将在何处发生。为了适应变速和由此产生的频移峰值位置，以前的工作引入了识别或跟踪相关频率的方法，这需要在进行任何故障分析之前需要另外一组处理算法来定位这些与故障相关的峰值。在这项工作中 提出了一种简化方法，通过引入一组带通滤波器来绕过变速（和频移峰值）问题，该带通滤波器涵盖了预期出现峰值的范围。这些滤波器旨在捕获与故障相关的光谱信息，以训练用于自动故障检测的分类器，而与峰值的特定位置无关。初步的实验结果表明，该方法在变速方面具有较强的鲁棒性，并且在显示出训练期间未出现的速度和条件下的故障时，还具有很好的通用性。在对提出的故障检测系统进行了培训和调试之后，对该系统进行了“看不见的”数据测试，并获得了97.4％的高分类精度，证明了该方法的有效性。