This paper presents a residual-informed machine learning approach for replacing algebraic loops in equation-based Modelica models with neural network surrogates. A feedforward neural network is trained using the residual (error) of the algebraic loop directly in its loss function, eliminating the need for a supervised dataset. This training strategy also resolves the issue of ambiguous solutions, allowing the surrogate to converge to a consistent solution rather than averaging multiple valid ones. Applied to the large-scale IEEE 14-Bus system, our method achieves a 60% reduction in simulation time compared to conventional simulations, while maintaining the same level of accuracy through error control mechanisms.

翻译：本文提出了一种基于残差信息的机器学习方法，用于将基于方程的Modelica模型中的代数环替换为神经网络代理模型。该方法在前馈神经网络的损失函数中直接使用代数环的残差（误差）进行训练，从而无需监督数据集。这种训练策略同时解决了多解歧义性问题，使代理模型能够收敛到一致解而非多个有效解的平均值。在大型IEEE 14-Bus系统上的应用表明，通过误差控制机制在保持相同精度水平的前提下，我们的方法相比传统仿真实现了60%的模拟时间缩减。