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.
翻译:我们探讨是否有可能利用物理知情神经网络来大幅度加速解决支配动力系统动态的普通差位热核方程式的解决方案。在短暂的稳定评估方面,传统应用的方法要么具有重大的计算负担,要求模型简化,要么使用过于保守的替代模型。常规神经网络可以绕过这些限制,但面对高质量的培训数据集的高需求,而它们却忽视了基本方程式。物理知情神经网络不同:它们将动力系统差异的代数方程式直接纳入神经网络培训,并大大减少了培训数据的需求。本文对物理知情神经网络的运行进行了深入的潜伏,用于动力系统快速稳定评估。引入了新的神经网络培训程序,以便于进行彻底比较,我们探索物理学知情神经网络如何在计算时间、数据要求和预测准确性方面与传统的差异地质溶液和古典神经网络进行对比。我们展示了Kundur两地区系统的调查结果,并评估了物理知情神经网络的机会和挑战,以作为可能的横向稳定分析工具,以进一步发展这一稳定方法。