基于物理信息神经网络的同步发电机建模OA北大核心CSTPCD

The synchronous generator(SG)models constructed based on physical principles have limitations in accurately capturing the nonlinear characteristics of the SG.Hence,the power system is continuously researching and adopting new SG models to enhance their accuracy.Data-driven models have stronger nonlinear expression capabilities,but they face challenges in practical applications for SG modeling,such as weak model generalization and high data requirements.To overcome these problems,this paper proposes a physics-informed neural network(PINN)model for SG,employing recurrent neural network as the fundamental model architecture.The modelling of the PINN is guided by the physical mechanism of SG and based on the principles of neural network modeling.The PINN can express the magnetic saturation characteristic of SG with high accuracy and has strong generalization ability.It can achieve higher fitting accuracy for SG models of various orders with small-scale data and can be applied to existing electromechanical transient simulation algorithms.