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摘要翻译：
本文提出了逆潮流问题，即从多个节点的电压和电流相量推断节点导纳矩阵（即电力系统的网络结构）。我们证明了当系统中的每个节点都装有测量装置时，导纳矩阵可以从对应于不同稳态的测量序列中唯一地识别出来；即使系统中的某些节点（隐节点）没有被监视，也可以确定一个Kron约化导纳矩阵。在此基础上，我们提出了一种基于图论的有效算法来揭示含有隐藏节点的径向系统的实际导纳矩阵。我们为恢复的导纳矩阵提供了理论保证，并证明了在一些温和的假设下，即使从Kron约化导纳矩阵中也可以完全恢复实际导纳矩阵。在标准测试系统上的仿真证实，这些算法能够从噪声传感器数据中提供准确的导纳矩阵估计。
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英文标题：
《Inverse Power Flow Problem》
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作者：
Ye Yuan, Steven Low, Omid Ardakanian, Claire Tomlin
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最新提交年份：
2021
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Systems and Control 系统与控制
分类描述：This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类：Computer Science 计算机科学
二级分类：Systems and Control 系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类：Mathematics 数学
二级分类：Optimization and Control 优化与控制
分类描述：Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学，线性规划，控制论，系统论，最优控制，博弈论
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英文摘要：
This paper formulates the inverse power flow problem which is to infer the nodal admittance matrix (hence the network structure of the power system) from voltage and current phasors measured at a number of buses. We show that the admittance matrix can be uniquely identified from a sequence of measurements corresponding to different steady states when every node in the system is equipped with a measurement device, and a Kron-reduced admittance matrix can be determined even if some nodes in the system are not monitored (hidden nodes). Furthermore, we propose effective algorithms based on graph theory to uncover the actual admittance matrix of radial systems with hidden nodes. We provide theoretical guarantees for the recovered admittance matrix and demonstrate that the actual admittance matrix can be fully recovered even from the Kron-reduced admittance matrix under some mild assumptions. Simulations on standard test systems confirm that these algorithms are capable of providing accurate estimates of the admittance matrix from noisy sensor data.
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PDF链接：
https://arxiv.org/pdf/1610.06631
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