基于物理信息神经网络的同步发电机建模

Modeling of Synchronous Generator Based on Physics-informed Neural Network

采用物理机理建立的同步发电机模型在表达发电机真实的非线性特性方面存在不足,电力系统正在不断研究和采纳新的同步发电机模型以增强模型的准确性。数据驱动模型具有更强的非线性表达能力,但在同步发电机建模实际应用中面临着模型泛化性不强和所需数据量大等问题。为克服上述问题,该文结合神经元建模原理,以循环神经网络为基本框架,在同步发电机物理机理的引导下,提出基于物理信息神经网络的同步发电机模型。经过算例验证,所提模型可准确表达同步发电机磁饱和特性,并且具有较强的泛化性。该模型可在小规模数据下对同步发电机各阶模型达到更高的拟合准确度,并可应用于现有机电暂态仿真算法。

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.Datadriven 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.