随着风电产业的快速发展，大量风电机组投入使用。为使风电机组高效运行，减少因故障停机而造成的高维护成本，对风电机组故障预测方法进行研究。以一台2 000 kW双馈风电机组14个月的数据采集与监视控制（supervisory control and data acquisition, SCADA）数据为基础，建立卷积神经网络-双向长短时记忆网络（convolutional neural network-bidirectional long short-term memory, CNN-BiLSTM）算法模型，首先采用卷积神经网络剔除输入数据中的异常噪声数据，提取输入数据的关键局部特征，简化输入数据复杂度，然后将处理后的输入数据放入双向长短时记忆网络中进行有功功率预测，对有功功率真实值与预测值的残差进行分析，完成风电机组故障预测。结果表明：所构建的算法模型具备故障预测的稳定性，而且可以消除多种因素导致的误预测，比风电机组SCADA系统提前4 d做出故障预测，为避免因故障恶化而引起突然停机提供了保障。

With the rapid development of wind power industry, a large number of wind turbines are put into use. In order to make wind turbine operate efficiently and reduce the high maintenance cost due to faulty downtime, wind turbine fault prediction methods were researched. Based on the supervisory control and data acquisition(SCADA) data of a 2 000 kW doubly fed wind turbine for 14 months, a convolutional neural network-bidirectional long short-term memory(CNN-BiLSTM) algorithm model was established. Firstly, a convolutional neural network was used to reject the abnormal noise data in the input data, extract the key local features of the input data, and simplify the complexity of the input data. Then the processed input data was put into a bi-directional long and short-term memory network to perform active power prediction, and the active power prediction was performed in a bi-directional long and short-term memory network. Finally, the processed input data was put into a two-way long-short time memory network for active power prediction, and the residual difference between the real and predicted values of active power was analyzed to complete the prediction of wind turbine faults. The results show that the constructed algorithm model has the stability of fault prediction, and can eliminate the misprediction caused by many factors, and make the fault prediction 4 days earlier than the SCADA system of the wind turbine, which provides a guarantee for avoiding the sudden shutdown due to the deterioration of the fault.