The machine learning-based transient stability assessment (TSA) has shown satisfactory accuracy while been limited by the lack of interpretability. This letter thereby presents a novel deep learning paradigm that naturally embeds the Lyapunov stability theory of dynamic systems, in which approximating Lyapunov functions (LFs) is transformed into traditional regression or classification tasks. The Lyapunov stability theory is firstly extended and then integrated into a specific neural network structure, which consists of a flexible LF approximator and its corresponding gradient adjoint network. It is originally revealed that transient stability binary classification by deep Lyapunov learning (DLL) is equivalent to constructing a semi-analytical LF in the state space. Case studies validate the effectiveness of the proposed DLL scheme.

中文翻译：

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深度 Lyapunov 学习：将 Lyapunov 稳定性理论嵌入可解释神经网络以进行瞬态稳定性评估


基于机器学习的瞬态稳定性评估 （TSA） 显示出令人满意的准确性，但受到缺乏可解释性的限制。因此，这封信提出了一种新的深度学习范式，它自然地嵌入了动态系统的 Lyapunov 稳定性理论，其中近似 Lyapunov 函数 （LFs） 被转化为传统的回归或分类任务。首先将 Lyapunov 稳定性理论扩展并集成到特定的神经网络结构中，该结构由灵活的 LF 近似器及其相应的梯度伴随网络组成。最初揭示的是，通过深度 Lyapunov 学习 （DLL） 进行瞬态稳定性二元分类相当于在状态空间中构建一个半解析 LF。案例研究验证了所提出的 DLL 方案的有效性。