首页> 外文期刊>IEEE Transactions on Signal Processing >Power Systems Topology and State Estimation by Graph Blind Source Separation
【24h】

Power Systems Topology and State Estimation by Graph Blind Source Separation

机译:通过图形盲源分离的电力系统拓扑和状态估计

获取原文
获取原文并翻译 | 示例
           

摘要

In this paper, we consider the problem of blind estimation of states and topology (BEST) in power systems. We use the linearized dc model of real power measurements with unknown voltage phases (i.e., states) and an unknown admittance matrix (i.e., topology) and show that the BEST problem can be formulated as a blind source separation (BSS) problem with a weighted Laplacian mixing matrix. We develop the constrained maximum likelihood (ML) estimator of the Laplacian matrix for this graph BSS problem with Gaussian-distributed states. The ML-BEST is shown to be only a function of the states' second-order statistics. Since the topology recovery stage of the ML-BEST approach results in a high-complexity optimization problem, we propose two low-complexity methods to implement it: First, two-phase topology recovery, which is based on solving the relaxed convex optimization and then finding the closest Laplacian matrix, and second, augmented Lagrangian topology recovery. We derive a closed-form expression for the associated Cramer-Rao bound (CRB) on the topology matrix estimation. The performance of the proposed methods is evaluated for three case studies: the IEEE-14 bus system, the IEEE 118-bus system, and a random network, and compared with the oracle minimum mean-squared-error state estimator and with the proposed CRB.
机译:在本文中,我们考虑了电力系统中状态和拓扑(最佳)盲估计问题。我们使用具有未知电压阶段(即状态)和未知导纳矩阵(即拓扑)的线性化直流模型,并显示最佳问题可以用加权作为盲源分离(BSS)问题。拉普拉斯混合基质。对于Gaussian分布状态,我们开发了Laplacian矩阵的约束最大可能性(ML)估计器。 ML-BEST被证明只是国家二阶统计的函数。由于ML-BEST方法的拓扑恢复阶段导致高度复杂性优化问题,我们提出了两种低复杂性方法来实现它:第一,两相拓扑恢复,这是基于解决轻松的凸优化和然后找到最接近的拉普拉斯矩阵,第二,增强拉格朗日拓扑恢复。我们在拓扑矩阵估计上获得了相关的Cramer-Rao绑定(CRB)的封闭形式表达式。评估所提出的方法的性能,用于三种案例研究:IEEE-14总线系统,IEEE 118总线系统和随机网络,并与Oracle最小均值误差状态估计器和提出的CRB相比。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号