Rotor imbalances present a serious problem for wind turbines. In particular, for offshore wind turbines, aerodynamic imbalance can have a severe impact because of the large rotor size. In this study, the impact of the aerodynamic imbalance is investigated. A novel framework for detecting aerodynamic imbalance is proposed. Firstly, a model of a 3MW direct-driven wind turbine was developed. The signals were acquired to test and verify the impact of aerodynamic imbalance. Secondly, a method based on optimized maximum correlated kurtosis deconvolution was proposed for the primary detection. The intrinsic mode functions of nacelle vibration were adopted as the input variable. The weak unbalanced signals could be discerned. Moreover, the azimuth of rotor allows the unbalanced blades to be obtained. Thirdly, a convolutional neural network with a new structure was used to determine the magnitudes of aerodynamic imbalances. The first layer of the convolutional neural network is sufficiently wide for improving feature extraction, it could make nacelle acceleration as the input. This structure exhibits accuracy and robustness satisfactorily. Finally, the framework was demonstrated in a high-fidelity simulation environment. Different scenarios of aerodynamic imbalance were tested, the results demonstrate the satisfactory performance of the proposed framework.

转子不平衡对风力涡轮机来说是一个严重的问题。特别是对于海上风力涡轮机，由于转子尺寸较大，气动不平衡会产生严重影响。在这项研究中，研究了空气动力不平衡的影响。提出了一种用于检测空气动力不平衡的新框架。首先，开发了3MW直驱风力发电机模型。获取信号以测试和验证空气动力不平衡的影响。其次，提出了一种基于优化最大相关峰度反卷积的初步检测方法。采用机舱振动的固有模态函数作为输入变量。可以识别出微弱的不平衡信号。此外，转子的方位角允许获得不平衡的叶片。第三，使用具有新结构的卷积神经网络来确定空气动力学不平衡的大小。卷积神经网络的第一层足够宽以改进特征提取，它可以将机舱加速作为输入。这种结构令人满意地表现出准确性和鲁棒性。最后，该框架在高保真仿真环境中进行了演示。测试了不同的气动不平衡场景，结果证明了所提出框架的令人满意的性能。该框架在高保真仿真环境中进行了演示。测试了不同的气动不平衡场景，结果证明了所提出框架的令人满意的性能。该框架在高保真仿真环境中进行了演示。测试了不同的气动不平衡场景，结果证明了所提出框架的令人满意的性能。