论文标题
线性灵敏度分布因子的数据驱动和在线估计：一种低级方法
Data-Driven and Online Estimation of Linear Sensitivity Distribution Factors: A Low-rank Approach
论文作者
论文摘要
电气传输系统中灵敏度矩阵的估计使网格操作员可以实时评估电力注射的变化如何反映到功率流的变化。在本文中，我们提出了一种强大的低级最小化方法，以根据电力注射和功率流的测量来估计灵敏度矩阵。提出了一种在线近端梯度方法，以从实时测量中估算灵敏度。与基于最小二乘方法的现有方法相比，当回归模型不确定时，提出的方法获得了有意义的估计值。此外，我们的方法还可以识别错误的测量并处理丢失的数据。在这项工作中，融合以动态遗憾的形式导致结果。数值测试证实了新方法的有效性以及缺失测量和异常值的鲁棒性。
Estimation of sensitivity matrices in electrical transmission systems allows grid operators to evaluate in real-time how changes in power injections reflect into changes in power flows. In this paper, we propose a robust low-rank minimization approach to estimate sensitivity matrices based on measurements of power injections and power flows. An online proximal-gradient method is proposed to estimate sensitivities on-the-fly from real-time measurements. The proposed method obtains meaningful estimates with fewer measurements when the regression model is underdetermined, in contrast with existing methods based on least-squares approaches. In addition, our method can also identify faulty measurements and handle missing data. In this work, convergence results in terms of dynamic regret are presented. Numerical tests corroborate the effectiveness of the novel approach and the robustness of missing measurements and outliers.
