一种采用电路理论节点-断路器（Node-breaker）模型的广义状态估计凸优化方法
A Convex Method of Generalized State Estimation using Circuit-theoretic Node-breaker Model
AI总结：
针对电网开关状态错误引发的拓扑误差及不良数据问题，本文提出基于电路理论的凸优化广义状态估计方法ckt-GSE，通过构建等效电路与线性规划求解，可联合估计交流状态与拓扑，兼具可扩展性与数据误差鲁棒性。
摘要：
准确且实时更新的拓扑结构对电网态势感知至关重要；然而，由物理损坏、通信错误或网络攻击导致的开关状态错误往往会引发拓扑误差。为在可能存在拓扑误差和不良数据的情况下维持态势感知，本文提出了ckt-GSE，这是一种采用节点-断路器（NB）模型的电路理论广义状态估计方法。ckt-GSE是一种凸优化且可扩展的模型，可联合估计交流状态变量和网络拓扑，对不同类型的数据误差具有鲁棒性。该方法首先通过构建并聚合SCADA计量装置、相量测量单元（PMUs）以及开关设备的线性电路模型，构建交流电网的等效电路表示。随后基于该电路，ckt-GSE采用加权最小绝对值（WLAV）目标函数定义了一个带约束的优化问题，以形成鲁棒估计器。该问题属于线性规划（LP）问题，其求解结果包含准确的交流状态以及用于识别拓扑误差和不良数据的噪声项稀疏向量。本文首次探索了将电路理论方法应用于受交流网络约束的广义状态估计算法，该算法具备以下特性：1）适用于真实数据场景；2）无需松弛即为凸优化问题，可通过本文的电路求解器实现可扩展计算；3）具有鲁棒性，能够识别并剔除不同类型的数据误差。
英文摘要：
An accurate and up-to-date topology is critical for situational awareness of a power grid; however, wrong switch statuses due to physical damage, communication error, or cyber-attack, can often result in topology errors. To maintain situation awareness under the possible topology errors and bad data, this paper develops ckt-GSE, a circuit-theoretic generalized state estimation method using node-breaker (NB) model. Ckt- GSE is a convex and scalable model that jointly estimates AC state variables and network topology, with robustness against different data errors. The method first constructs an equivalent circuit representation of the AC power grid by developing and aggregating linear circuit models of SCADA meters, phasor measurement units(PMUs), and switching devices. Then based on this circuit, ckt-GSE defines a constrained optimization problem using weighted least absolute value (WLAV) objective to form a robust estimator. The problem is a Linear Programming (LP) problem whose solution includes accurate AC states and a sparse vector of noise terms to identify topology errors and bad data.This paper is the first to explore a circuit-theoretic approach for an AC-network constrained GSE algorithm that is: 1) applicable to the real-world data setting, 2) convex without relaxation, scalable with our circuit-based solver; and 3) robust with the ability to identify and reject different data errors
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
