The increasing penetration of intermittent distributed energy resources in power networks calls for novel planning and control methodologies which hinge on detailed knowledge of the grid. However, reliable information concerning the system topology and parameters may be missing or outdated for temporally varying electric distribution networks. This paper proposes an online learning procedure to estimate the network admittance matrix capturing topological information and line parameters. We start off by providing a recursive identification algorithm exploiting phasor measurements of voltages and currents. With the goal of accelerating convergence, we subsequently complement our base algorithm with a design-of-experiment procedure which maximizes the information content of data at each step by computing optimal voltage excitations. Our approach improves on existing techniques, and its effectiveness is substantiated by numerical studies on realistic testbeds.

翻译：断断续续分布的能源资源日益渗透到电力网络中,这就要求采用新的规划和控制方法,这取决于对电网的详细了解,然而,系统地形学和参数的可靠信息可能缺失或过时,供时间变化的电力分配网络使用。本文件建议采用在线学习程序来估计网络接纳矩阵,以收集地形信息和线参数。我们首先提供循环识别算法,利用电压和电流的散射测量。为了加速趋同,我们随后以设计实验程序补充我们的基础算法,通过计算最佳电压插图,最大限度地增加每一步骤的数据信息内容。我们的方法改进了现有技术,并通过对现实测试床进行数字研究来证实其有效性。