## 1 Introduction  
In the Gobi Desert, mountain passes and other outfields, the total number of wind turbines assembled is growing rapidly. However, the performance of single machine is not significantly improved. One of the main reasons for this is the frequent failure of the turbines [1]. Frequent failures of wind turbines lead to unit shutdown, which on the one hand reduces the reliability of wind power, on the other hand, reduces the utilization rate of wind energy and increases the cost of operation and maintenance [2]. The automatic status monitoring and fault early warning of electronic motors can prevent the occurrence of faults to a certain extent and reduce unexpected downtime to the greatest extent. At present, many sensors related to supervisory control and data acquisition (SCADA) system have been installed in electronic motors. They can collect a large amount of measurement data, such as temperature parameters (such as bearing temperature, oil temperature), wind parameters (such as wind speed, wind direction) and energy conversion parameters (such as output power, rotor speed). Wind farm operators widely use these parameters to monitor the health of wind turbines. In addition, the SCADA system also records the comprehensive state parameters that may be fault points. In a word, the comprehensiveness and availability of SCADA data lay a solid foundation for status monitoring and fault early warning of wind turbines. Normal behavior modeling based on SCADA data is an effective method for abnormal identification of electronic motors [3]. The main advantage of the normal behavior model in monitoring motors' signals is that it does not need prior knowledge of the signal, and it can decouple the signal monitoring from the operation mode. This can effectively avoid the fluctuation caused by the operation mode. The normal behavior modeling usually establishes the input-output model when the electronic motor is in a healthy operation state. When the system break downs, the residual error between the model's output and the corresponding measurement value will become large. According to the variation of the residual error, the systematic fault can be identified. [PERSON] and [PERSON] [4] used signal reconstruction and the autoregressive method to monitor the abnormal temperature of the gearbox and stator. The selection of input variables in this model is particularly important. Based on nonlinear state estimation, [PERSON] and [PERSON] [5] predicted the cooling oil temperature in SCADA data, so as to realize the fault early warning of the gearbox. In the normal behavior model, various state parameters of wind turbines are modeled to detect the abnormality of the main components. The article [6] used an artificial neural network (ANN) to model the data of the main bearing to monitor the abnormality of the main bearing. [PERSON] et al. [7] used domain knowledge to select features of SCADA data, then used a neural network (NN) to establish the prediction model of state parameters, and defined abnormal level index (ALI) to measure the abnormal state parameters. Some researchers [8; 9] employed adaptive fuzzy neural networks to establish 45 normal behavior models of the key components of wind turbines. By using a fuzzy inference system (FIS) to analyze prediction error and failure mode, they demonstrated the advantages of the ANFIS model in normal behavior modeling. [PERSON] et al. [10] monitored the abnormal condition of the fan gearbox by implementing a DNN (deep neural network) model with dropout [11] based on lubricating oil pressure data. Additionally, they pointed out that the prediction performance of DNN was obviously better than the kNN, Lasso, feedforward neural networks, etc. In addition, model predictive control (MPC) has also attracted much attention in motor systems due to its simplicity and flexibility for multiple control constraints [12; 13; 14].