中国电机工程学报2024，Vol.44Issue(12)：4924-4932,中插27,10.DOI:10.13334/j.0258-8013.pcsee.230305
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
摘要
Abstract
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.关键词
物理信息神经网络/数据驱动/同步发电机/磁饱和效应Key words
physics-informed neural network/data-driven/synchronous generator/magnetic saturation effect分类
信息技术与安全科学引用本文复制引用
杨珂,王鑫,凌佳杰,耿光超,江全元..基于物理信息神经网络的同步发电机建模[J].中国电机工程学报,2024,44(12):4924-4932,中插27,10.基金项目
国家自然科学基金项目(52177120) （52177120）
南方电网数字电网集团有限公司科技项目(670000KK52210032). Project Supported by National Natural Science Foundation of China(52177120) （670000KK52210032）
Research Project of China Southern Power Grid Digital Power Grid Group Co.,Ltd.(6700KK52210032). （6700KK52210032）
