## 3 Parameter Identification of Black Box New Energy Units
### Processing of Measured Data and Selection of Optimization Algorithms  
#### 3.3.1 Data Segmentation Preprocessing Based on Statistical Modal Features  
When carrying out the identification of active and reactive three-segment control models for each fault type, the selection of a precise segmentation of the training set data and a reliable optimization algorithm will determine the accuracy of the identification results. Therefore, this paper firstly analyzes the statistical characteristics of the measured data and proposes a segmentation method based on the statistical modal characteristics, which utilizes the centralized trend characteristics of voltage, active, and reactive data during fault ride-through and recovery to segment the data and divides the whole data segment into six parts shown in Figure 13. The specific segmentation steps are shown below:  
(1) Based on the received controller fault ride-through voltage thresholds and voltage data, initially determine the starting point Sin for entering the fault ride-through state and the termination point Sout for exiting the fault ride-through state for the working condition in which it is located.  
(2) Extract the statistical modal eigenvalues of voltage, active power, and reactive power during the fault ride-through phase, i.e., the steady-state values during the fault ride-through period, using the initial starting and ending data point locations.  
(3) Setting the deviation threshold and combining with Equation (2), first determine the data start and end points of voltage, active power, and reactive power during the fault ride-through period, and then take a large for the data start point of each variable to obtain the start point S1 of the fault ride-through period, and take a small for the data endpoint of each variable to obtain the endpoint S2 of the fault ride-through period.  
\[\begin{split} S1_{\text{var}}&=\min\left\{i| \frac{data_{\text{var}}(i)-mode_{\text{var}}}{mode_{\text{var}}}\mid<DEV \right\}\\ S2_{\text{var}}&=\max\left\{i|\frac{data_{\text{var }}(i)-mode_{\text{var}}}{mode_{\text{var}}}\mid<DEV\right\}\end{split} \tag{10}\]  
where \(S1_{\text{var}}\) and \(S2_{\text{var}}\) are the start and end point positions during fault ride-through for each variable; \(data_{\text{var}}\) is the fault ride-through data for each variable; \(mode_{\text{var}}\) is the steady-state value during fault ride-through for each variable; and \(DEV\) is the deviation threshold.  
(4) Similarly, the data of each variable starts from the endpoint S2 of the fault ride-through period, firstly extracts the normal steady-state value of each variable, and then combines with Equation (2) to obtain the starting point position of each variable to enter into the normal steady-state operation after the fault, i.e., the position of the endpoint of each variable's fault recovery process, and then takes the larger one to obtain the data's endpoint S4 of the fault recovery process.  
(5) For the fault recovery starting point phase, the starting point is the endpoint S2 of the fault ride-through period, and the endpoint S3 generally defaults to be the 10% to 20%  
Figure 13: Segmentation method for fault condition data based on statistical modal features.  
position of the period from the endpoint S2 of the fault ride-through period to the endpoint S4 of the fault recovery process;