Maximum Tolerance to Load Uncertainty of a Multiple-Model-Based Topology Detector
Maximum Tolerance to Load Uncertainty of a Multiple-Model-Based Topology Detector
复制标题
基于多模型的拓扑检测器对负载不确定性的最大容忍度
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
N. E. Wu
中科院分区：
文献类型：
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作者：
Morteza Sarailoo;N. E. Wu
提出并求解了一组额定负载下多模型检测器对负载不确定度的最大容限问题。该检测器包含用于每个预期电路拓扑的一个设计模型。每个模型用于确定特定于拓扑的检测阈值，只有当测量残差来自匹配的模型-电路对时，才选择该阈值来定义测量残差的小。为了量化探测器对负载不确定度的容忍度，提出了一种迭代算法来计算最大椭球，该最大椭球表示在没有检测误差的情况下可以容忍的最严重负载电流不确定度。每次迭代从以额定负载电流矢量为中心的不确定参数空间中的足够大的椭球开始，求解凸优化问题以识别最坏情况下的负载不确定性，对照一组检测阈值进行验证，然后减小不确定性椭球的体积，直到解决了所有违反阈值的问题。对于IEEE 9节点测试系统，针对由测量残差的2范数定义的1个正常回路和6个开路阈值，计算了最差容许负荷不确定度。阈值是通过仿真确定的。(通过仿真)该阈值对于预测的不确定性椭球内的负载扰动是稳健的。我们最近开发的网络划分的概念和方法使大规模网络的应用成为可能。
A problem to find the maximum tolerance to load uncertainty of a multiple-model-based detector designed for a set of nominal loads is formulated and solved. The detector contains one design model for each anticipated circuit topology. Each model is used to determine a topology-specific detection threshold, selected to define the smallness of measurement residuals only if they are from the matched model-circuit pair. To quantity the detector’s tolerance to load uncertainty, an iterative algorithm is developed for computing the largest ellipsoid representing the most severe load current uncertainty tolerable without detection errors. Each iteration starts from a sufficiently large ellipsoid in an uncertain parameter space centered at nominal load current vector, solves a convex optimization problem to identify the worst-case load uncertainty, verifies against the set of detection thresholds, then reduces the volume of the uncertainty ellipsoid until all violations of thresholds are resolved. The worst tolerable load uncertainty is computed for the IEEE 9-bus test system with respect to one normal circuit and 6 open-circuit thresholds defined by 2-norms of measurement residuals. The thresholds are determined through simulations. The thresholds are shown (by simulations) to be robust for load perturbations within the predicted uncertainty ellipsoid. Application to large scale networks is enabled by our recently developed concept and method of network partition.