论文标题
网络微电网的神经动态状态估计
Neuro-Dynamic State Estimation for Networked Microgrids
论文作者
论文摘要
我们设计了神经动态状态估计（Neuro-DSE），这是一种基于学习的动态状态估计（DSE）算法，用于未知子系统下网络微电网（NMS）。我们的贡献包括：1）NMS DSE的数据驱动的神经-DSE算法具有部分未识别的动态模型，该模型将神经 - 异常 - 差异方程式（ODE-NET）纳入Kalman滤波器； 2）一种自动滤波，增强和纠正框架，可以在有限且嘈杂的测量下进行自动驱动DSE的自我修复神经-DSE算法（Neuro-DSE+）； 3）一种神经-Kalmannet-DSE算法，该算法将Kalmannet与Neuro-DSE进一步整合在一起，以缓解基于神经和物理的动态模型的模型不匹配； 4）增强神经-DSE，用于NMS状态和未知参数的联合估计（例如惯性）。广泛的案例研究表明，在不同的噪声水平，控制模式，电源，观察力和模型知识下，神经-DSE及其变体的疗效。
We devise neuro-dynamic state estimation (Neuro-DSE), a learning-based dynamic state estimation (DSE) algorithm for networked microgrids (NMs) under unknown subsystems. Our contributions include: 1) a data-driven Neuro-DSE algorithm for NMs DSE with partially unidentified dynamic models, which incorporates the neural-ordinary-differential-equations (ODE-Net) into Kalman filters; 2) a self-refining Neuro-DSE algorithm (Neuro-DSE+) which enables data-driven DSE under limited and noisy measurements by establishing an automatic filtering, augmenting and correcting framework; 3) a Neuro-KalmanNet-DSE algorithm which further integrates KalmanNet with Neuro-DSE to relieve the model mismatch of both neural- and physics-based dynamic models; and 4) an augmented Neuro-DSE for joint estimation of NMs states and unknown parameters (e.g., inertia). Extensive case studies demonstrate the efficacy of Neuro-DSE and its variants under different noise levels, control modes, power sources, observabilities and model knowledge, respectively.
