论文标题
使用神经Lyapunov方法对网络微电网进行瞬时稳定性评估
Transient Stability Assessment of Networked Microgrids Using Neural Lyapunov Methods
论文作者
论文摘要
本文提出了一种基于神经Lyapunov方法的网络微电网的新型短暂稳定性评估工具。评估瞬态稳定性被提出为估计网络微电网动态安全区域的问题。我们利用神经网络在状态空间中学习本地Lyapunov功能。根据学到的神经Lyapunov功能估算最大的安全区域，并用于表征网络微电网可以耐受的干扰。在网格连接的微电网，三个具有混合接口动力学的网络微电网和IEEE 123节点馈线中测试并验证了所提出的方法。案例研究表明，该提出的方法可以解决具有异质界面动力学的网络微电网，并且与基于二次Lyapunov函数的常规方法相比，可以以较低的保守性来表征安全区域。
This paper proposes a novel transient stability assessment tool for networked microgrids based on neural Lyapunov methods. Assessing transient stability is formulated as a problem of estimating the dynamic security region of networked microgrids. We leverage neural networks to learn a local Lyapunov function in the state space. The largest security region is estimated based on the learned neural Lyapunov function, and it is used for characterizing disturbances that the networked microgrids can tolerate. The proposed method is tested and validated in a grid-connected microgrid, three networked microgrids with mixed interface dynamics, and the IEEE 123-node feeder. Case studies suggest that the proposed method can address networked microgrids with heterogeneous interface dynamics, and in comparison with conventional methods that are based on quadratic Lyapunov functions, can characterize the security regions with much less conservativeness.
