## 3 Parameter Identification of Black Box New Energy Units
### Processing of Measured Data and Selection of Optimization Algorithms
#### 3.3.2 Genetic Proposed Newton Hybrid Optimization Algorithm  
After completing the segmentation and categorization of all working condition data in the training set, combined with the proposed structured three-segment control model for active and reactive fault ride-through, the active and reactive multi-control strategy models contained in each stage are identified with their respective parameters. The control strategy with the smallest identification error is selected as the optimal control strategy for testing at that stage. Due to the existence of both linear and nonlinear models in the control model to be identified and the large size and complexity of the measured sample set data of the model, if only a linear optimization algorithm is used, it is impossible to optimize the nonlinear model effectively. If only a nonlinear algorithm is used, it is easy for the optimization process to fall into a local optimum and fail to find the global optimal solution, which affects the quality of the optimization results and the accuracy of the model. To overcome the aforementioned problems, this paper employs a hybrid optimization approach combining a genetic algorithm with L-BFGS for parameter identification. Genetic algorithm (GA) is a computational model simulating Darwinian natural selection, featuring strong global search capability that enables rapid approximation of optimal solutions in early stages. However, its local search ability tends to weaken in later stages. On the other hand, the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, as a quasi-Newton method, demonstrates excellent local convergence properties but is sensitive to initial values. Recognizing these complementary characteristics, this study develops a novel hybrid approach that organically combines both algorithms into a genetic quasi-Newton method [48]. The hybrid approach leverages the complementary strengths of both algorithms: the genetic algorithm's strong global search capability and rapid initial exploration, combined with the Newton method's (L-BFGS) fast local convergence. This integration addresses key limitations of each method--the genetic algorithm's weakening local search ability in later stages and the Newton method's sensitivity to initial values. The implementation involves two key components: (1) the genetic algorithm performs initial global exploration to identify promising solution regions, and (2) automatic switching to L-BFGS when population diversity falls below a threshold or fitness improvement stagnates. This synergistic combination effectively solves problems of slow optimization while preserving each algorithm's strengths. The specific implementation method is as follows:  
(1) Initialize the parameters related to the genetic Newtonian algorithm, including the population size \(M\), the maximum number of iterations \(G_{\text{max}}\), and the fitness threshold. Randomly generate the initial population \(P=\{k_{1},k_{2},\ldots,k_{M}\}\), and the population individual \(k_{i}\) is a parameter vector, where the \(j\)th parameter takes the value range of \([k_{\text{min},j},k_{\text{max},j}]\). (2) Calculate the fitness \(f(k_{i})\) of each individual in the initial population using the objective function and rank them according to their size, and the fitness calculation formula is as follows:  
\[f(k_{i})=\frac{1}{J(k_{i})+\epsilon} \tag{11}\]  
where \(\epsilon\) is the anti-zero division positive number. (3) Perform a selection crossover mutation operation according to the principle that the larger the fitness value, the better; use roulette to select the two parent individuals with higher fitness for a single-point crossover operation, and carry out a mutation operation on the generated offspring individuals to increase the diversity of the population to avoid the population from falling into the local optimal solution.