## 4 Discussion  
There are many factors that affect the modeling of the wind turbine operation status and the calculation of health indicators, including the accuracy and real-time performance of health indicator calculation. For example, increasing the data window width yields a higher accuracy in health indicator modeling and health status identification and an earlier warning of abnormal status identification. However, with the increase in the width of the data window, the time required for the computer to model and identify health indicators and the time required to identify abnormal states increase. As another example, a smaller time increment in the sliding window model yields better real-time performance, allowing faster identification of an abnormal operating health status. However, the time increment should be sufficient to complete calculations involved in operating health identification and to achieve real-time identification. Therefore, the selection of specific health indicators should be combined with the actual situation of wind turbines to coordinate the accuracy and real-time performance of health assessment. From the analysis of the examples in this paper, the method of identifying the operating state of wind turbines based on SCADA data was effective because advanced warning was much larger than the delay. The discussion of this problem involves SCADA system configuration. The better the system configuration is, the shorter the time required for the same calculation workload is. The calculation workload required for health index identification is also critical. For example, considering more relationships between wind turbine operating parameter data requires a higher computation workload and thus longer computation times for the same system configuration. Consider the following large wind turbine SCADA system configuration as an example: Intel i7-4790 CPU, 16 GB of operating memory, 1 TB hard disk, Windows 7 operating system. The system was used for modeling the relationship between wind speed data of WT1 and power data of WT1 in a window width by data fitting to analyze the time consumption of the data fitting calculation statistically. The width of the data window was set equal to 24, 30, 36, 42, and 48 h. The direct fitting method was used and then compared to the preprocessing and refitting method proposed in [12]. The modeling time is shown in Figure 15. The computation duration was shown to increase as the data window width increased, particularly when using the preprocessing and refitting method proposed in [12]. When the window width was 24 h, the computation duration of the direct fitting method was 5.67 s, and that of the preprocessing and fitting method was 11.79 s, which means that the computation duration time of the method proposed in [12] was approximately twice as long as that of the direct fitting method. Based on the computation durations required for data processing, fitting, modeling, and identification, it could be assumed that if a wind farm had 20 wind turbine sets, each wind turbine set required 10 operation parameter input and output relation models concurrently, and the computer  
Figure 15: Comparison of the computation durations of different data processing methods. configured by the SCADA was used for processing, the system would required 1134 s using the method proposed in this paper. The same system, using the method proposed in [12], required 2358 s. To ensure that enough time was available to complete the operation state identification calculation, the time increment in the sliding window model could not be less than 1134 s (i.e., approximately 0.32 h). However, using the method proposed in [12], the time increment in the sliding window model could not be less than 2358 s (i.e., approximately 0.66 h). Therefore, the data processing method of direct fitting to the raw data had better real-time performance than the preprocessing and refitting method proposed in [12] and could identify the running health status of wind turbine generator units in a more timely manner.