We explore the possibility to use physics-informed neural networks to drastically accelerate the solution of ordinary differential-algebraic equations that govern the power system dynamics. When it comes to transient stability assessment, the traditionally applied methods either carry a significant computational burden, require model simplifications, or use overly conservative surrogate models. Conventional neural networks can circumvent these limitations but are faced with high demand of high-quality training datasets, while they ignore the underlying governing equations. Physics-informed neural networks are different: they incorporate the power system differential algebraic equations directly into the neural network training and drastically reduce the need for training data. This paper takes a deep dive into the performance of physics-informed neural networks for power system transient stability assessment. Introducing a new neural network training procedure to facilitate a thorough comparison, we explore how physics-informed neural networks compare with conventional differential-algebraic solvers and classical neural networks in terms of computation time, requirements in data, and prediction accuracy. We illustrate the findings on the Kundur two-area system, and assess the opportunities and challenges of physics-informed neural networks to serve as a transient stability analysis tool, highlighting possible pathways to further develop this method.
A Physics-Informed Hybrid Neural Operator for Transient Magnetization Prediction in Power Magnetics
School of Electrical and Data Engineering, the University of Technology Sydney（悉尼科技大学电气与数据工程学院）；School of Electrical and Computer Engineering, the University of Sydney（悉尼大学电气与计算机工程学院）；National Railway Research and Design Institute of Signal and Communication（中国铁路通信信号集团公司通信信号研究设计院）
Physics-Informed Neural Network Digital Twin for Dynamic Tray-Wise Modeling of Distillation Columns under Transient Operating Conditions
Department of Chemical Engineering, Veer Surendra Sai University of Technology（维杰·苏雷纳·赛大学化学工程系）；Department of Computer Science & Engineering, Veer Surendra Sai University of Technology（维杰·苏雷纳·赛大学计算机科学与工程系）
Physics-informed post-processing of stabilized finite element solutions for transient convection-dominated problems
Department of Computer Technologies, Antalya Bilim University（安塔利亚比林大学计算机技术系）；Institute of Applied Mathematics, Middle East Technical University（中东技术大学应用数学研究所）；Department of Mathematics, Indian Institute of Technology Guwahati（印度理工学院古瓦哈提数学系）
Droplet-LNO: Physics-Informed Laplace Neural Operators for Accurate Prediction of Droplet Spreading Dynamics on Complex Surfaces
Department of Mechanical Engineering, IIT Kharagpur（印度克里希纳加尔帕大学机械工程系）；Department of Computer Science & Engineering, IIT Kharagpur（印度克里希纳加尔帕大学计算机科学与工程系）
MoE-GraphSAGE-Based Integrated Evaluation of Transient Rotor Angle and Voltage Stability in Power Systems
Arizona State University（亚利桑那州立大学）；Xinjiang University（新疆大学）；Tsinghua University（清华大学）；Zhengzhou Weiguang Semiconductor Co., Ltd.（郑州威光半导体有限公司）；City University of Hong Kong（香港城市大学）
Physics-Informed Machine Learning for Characterizing System Stability
School of Aerospace Engineering（航空航天工程学院）；AIAA Student Member（美国航空航天学会学生会员）；Schools of Aerospace Engineering & Computational Science and Engineering（航空航天工程与计算科学与工程学院）；AIAA Member（美国航空航天学会会员）
Linear Stability Analysis of Physics-Informed Random Projection Neural Networks for ODEs
Modelling Engineering Risk and Complexity, Scuola Superiore Meridionale（工程风险与复杂性建模，南方高级学院）；Dept. of Electrical and Computer Engineering, Clarkson University（电气与计算机工程系，克林顿大学）；Department of Statistics, Athens University of Economics and Business（统计学系，雅典经济与商业大学）
