Wind turbine generator temperature rise fault early warning method based on TensorFlow

The invention discloses a TensorFlow-based wind turbine generator temperature rise fault early warning method. The method comprises the steps of model data processing, model establishment and training, parameter adjustment and optimization, model operation to obtain an early warning result, stored model loading, data needing to be predicted importing, and fault point judgment through comparison of a predicted value and an actual value. According to the method, the defects in the prior art are overcome, aiming at the characteristics of large SCADA data volume and multiple data dimensions of the wind turbine generator, the rapid and efficient characteristics of TensorFlow are utilized, a Keras Tuner parameter adjustment framework is combined, and the problems of difficulty in fault early warning modeling and low accuracy of the generator of the wind turbine generator are well solved. 一种基于TensorFlow的风电机组发电机温升故障预警方法，包括：模型数据处理，模型建立与训练，调参优化，运行模型得出预警结果，加载保存的模型，导入需要预测的数据，通过预测值与实际值的比较来判断故障点。本发明克服了现有技术的不足，针对风电机组SCADA数据