The dynamics of the power system are described by a system of differential-algebraic equations. Time-domain simulations are used to understand the evolution of the system dynamics. These simulations can be computationally expensive due to the stiffness of the system which requires the use of finely discretized time-steps. By increasing the allowable time-step size, we aim to accelerate such simulations. In this paper, we use the observation that even though the individual components are described using both algebraic and differential equations, their coupling only involves algebraic equations. Following this observation, we use Neural Networks (NNs) to approximate the components' state evolution, leading to fast, accurate, and numerically stable approximators, which enable larger time-steps. To account for effects of the network on the components and vice-versa, the NNs take the temporal evolution of the coupling algebraic variables as an input for their prediction. We initially estimate this temporal evolution and then update it in an iterative fashion using the Newton-Raphson algorithm. The involved Jacobian matrix is calculated with Automatic Differentiation and its size depends only on the network size but not on the component dynamics. We demonstrate this NN-based simulator on the IEEE 9-bus test case with 3 generators.

翻译：电力系统动力学由一组微分代数方程描述。时域仿真用于理解系统动力学的演化过程。由于系统刚性问题需要使用精细离散化的时间步长，这些仿真可能计算成本高昂。通过增大允许的时间步长，我们旨在加速此类仿真。本文中，我们注意到尽管各组件同时采用代数方程和微分方程描述，但它们之间的耦合仅涉及代数方程。基于此发现，我们利用神经网络近似组件的状态演化，从而获得快速、准确且数值稳定的近似器，进而实现更大的时间步长。为考虑网络对组件的影响以及反之亦然，神经网络将耦合代数变量的时间演化作为其预测的输入。我们首先估计这一时间演化，然后利用牛顿-拉夫逊算法进行迭代更新。涉及的雅可比矩阵通过自动微分计算，其规模仅取决于网络规模，而与组件动态无关。我们在包含3台发电机的IEEE 9节点测试算例上验证了该基于神经网络的仿真器。