## 1 Introduction
Thus, they are more suitable for new energy systems that have high dimensionality of variables, strong coupling, and highly nonlinearized characteristics. Standard artificial intelligence algorithms include, for example, genetic algorithms, particle swarm optimization, differential evolution, and the JAYA algorithm, among others. [PERSON] et al. [27] proposed a PMSM parameter identification method based on an improved cuckoo search algorithm. [PERSON] et al. [28] proposed a genetic algorithm (GA)-based method for PMSG model parameter identification. [PERSON] et al. [29] proposed an improved pollination algorithm for identifying the parameters of PMSM. [PERSON] et al. [30] introduced a PSO algorithm based on chaotic mapping and Gaussian perturbation for parameter identification of PMSG motors. However, these optimization methods suffer from slow convergence, easy premature convergence, and low identification accuracy [31; 32]. To address these problems, [PERSON] et al. [33] proposed a Grey Wolf Optimizer (GWO) algorithm with an information-sharing search strategy to solve the parameter identification problem of doubly fed converters. In [34], a chaotic adaptive search GWO algorithm is proposed to recognize the parameters of PMSM. [PERSON] et al. [35] proposed a parameter estimation method for the PMSM drive system based on an improved particle swarm algorithm to solve the parameter estimation model of the PMSM drive system. [PERSON] et al. [36] indicates that the traditional photovoltaic cell model parameter identification method has several shortcomings, including low accuracy, slow convergence speed, and a high risk of falling into local optima. Therefore, this study proposes a photovoltaic cell model parameter identification method that improves upon the elephant swarm optimization algorithm. [PERSON] et al. [37] proposed a parameter identification method based on an atomic search algorithm; the experimental results demonstrate that the algorithm exhibits high accuracy and a fast convergence speed, achieving better results in PV cell model parameter identification. [PERSON] et al. [38] In order to accurately identify the circuit model parameters, an improved optimization algorithm based on particle swarm optimization is used, which combines the optimization strategies and hybrid algorithms proposed in recent years to make up for the fact that the traditional particle swarm algorithm tends to get stuck in the local optimal solution. The efficiency of the multidimensional search is reduced. In summary, although the new energy transient model parameter identification has achieved some research results, there are still the following difficulties:  
(1) The new energy unit \"black box\" controller fault traversal characteristics are complex, linear and nonlinear coupling is strong, parameter identification is difficult, identification results are poorly adapted, and accuracy is low. (2) The dynamic response of new energy units under symmetrical and asymmetrical fault types, different operating conditions, and various high and low voltage ride-throughs exhibits significant differences and shows strong sequential consistency. Its control strategy and parameters are diverse and intensive, with low identification efficiency. (3) The fault ride-through control mechanism of the new energy unit \"black box\" controller is unknown, and equivalent modeling is difficult;  
To solve these problems, the main contributions of this paper can be summarized as follows:  
(1) A new energy unit \"black box\" controller fault traversal parameter identification method based on the genetic Newton algorithm is proposed, which can accurately identify the actual doubly fed wind turbine control strategy and parameters under symmetrical/asymmetrical fault types, different operating conditions, and different high and low voltage traversals.