Abstract Operation of wind turbines under fault state will directly affect the power output efficiency of wind farms. This paper proposes a new automatic fault diagnosis method for wind turbines. A fault diagnosis system framework is constructed and data of vibration status of wind turbines collected is processed and used for fault diagnosis. Firstly, wavelet coefficients are obtained using a discrete wavelet transform (DWT) for vibration acceleration signals collected from wind turbines. Then, the wavelet coefficients are sequentially subjected to phase space reconstruction (PSR) and singular value decomposition (SVD) to extract the fault features. Finally, an extreme learning machine (ELM) is used to classify the faults. Experimental results show that the proposed method is more effective and accurate than other fault diagnosis methods for wind turbines, such as support vector machine (SVM) and multiscale convolutional neural network (MSCNN).

中文翻译：

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风力发电机组故障自动诊断系统的设计与实现

摘要 风机在故障状态下的运行将直接影响风电场的功率输出效率。本文提出了一种新的风力涡轮机故障自动诊断方法。构建了故障诊断系统框架，对采集到的风电机组振动状态数据进行处理，用于故障诊断。首先，对于从风力涡轮机收集的振动加速度信号，使用离散小波变换（DWT）获得小波系数。然后，小波系数依次进行相空间重构（PSR）和奇异值分解（SVD）以提取故障特征。最后，使用极限学习机（ELM）对故障进行分类。