电流(流体)
控制理论(社会学)
电压
指数函数
权力分享
计算机科学
电子工程
电气工程
工程类
控制(管理)
数学
物理
功率(物理)
量子力学
数学分析
人工智能
作者
Pulkit Nahata,Mustafa Şahin Turan,Giancarlo Ferrari‐Trecate
标识
DOI:10.1109/tcst.2021.3120321
摘要
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knowledge of the grid.However, reliable information concerning the system topology and parameters may be missing or outdated for temporally varying electric distribution networks.This paper proposes an online learning procedure to estimate the network admittance matrix capturing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 a modified IEEE testbed.
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