发表机构
Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于神经网络的电力系统分散式稳定性条件构建方法，利用定制损失函数迭代训练，降低保守性，且在大规模系统训练下具备泛化能力。
AI 中文摘要
本文提出了一种建立电力系统分散式稳定性条件（DSCs）的新方法。该方法基于对现有解析推导的DSCs结构与性质的洞察，利用精心构造的神经网络来表述DSCs。进一步设计了一个定制的损失函数，以迭代训练神经网络，确保DSCs的有效性并降低其保守性。数值结果表明，所得的神经DSCs比传统解析DSCs更不保守，并且若使用较大规模系统进行训练，则具有泛化能力。
英文摘要
This letter proposes a novel method to establish decentralized stability conditions (DSCs) of power systems. This method formulates DSCs with meticulously crafted neural networks, based on the insight into the structure and properties of the existing analytically derived DSCs. A tailored loss function is further devised to iteratively train the neural networks, ensuring validity and reducing conservatism of the DSCs. Numerical results demonstrate that the resultant neural DSCs are less conservative than the conventional analytical DSCs, and become generalizable if a relatively large-scale system is used for training.
