Since the high penetration of renewable energy complicates the dynamic characteristics of the AC power electronic system (ACPES), it is essential to establish an accurate dynamic model to obtain its dynamic behavior for ensure the safe and stable operation of the system. However, due to the no or limited internal control details, the state-space modeling method cannot be realized. It leads to the ACPES system becoming a black-box dynamic system. The dynamic modeling method based on deep neural network can simulate the dynamic behavior using port data without obtaining internal control details. However, deep neural network modeling methods are rarely systematically evaluated. In practice, the construction of neural network faces the selection of massive data and various network structure parameters. However, different sample distributions make the trained network performance quite different. Different network structure hyperparameters also mean different convergence time. Due to the lack of systematic evaluation and targeted suggestions, neural network modeling with high precision and high training speed cannot be realized quickly and conveniently in practical engineering applications. To fill this gap, this paper systematically evaluates the deep neural network from sample distribution and structural hyperparameter selection. The influence on modeling accuracy is analyzed in detail, then some modeling suggestions are presented. Simulation results under multiple operating points verify the effectiveness of the proposed method.

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With the increasing penetration of wind power, large-scale integrated wind turbine brings stability and security risks to the power grid. For the aggregated modeling of large wind farms, it is crucial to consider low voltage ride-through (LVRT) characteristics. However, in aggregation methods, the approximate neglect behavior is essential, which leads to inevitable errors in the aggregation process. Moreover, the lack of parameters in practice brings new challenges to the modeling of a wind farm. To address these issues, a novel cyber-physical modeling method is proposed. This method not only overcomes the aggregation problem under the black-box wind farm but also accurately realizes the aggregation error fitting according to the operation data. The simulation results reveal that the proposed method can accurately simulate the dynamic behaviors of the wind farm in various scenarios, whether in LVRT mode or normal mode.

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