论文标题
基于优化的探索快速数据收集的可行电流空间
Optimization-Based Exploration of the Feasible Power Flow Space for Rapid Data Collection
论文作者
论文摘要
本文对各种非线性目标函数进行了系统的研究，该功能可用于探索与最佳功率流问题相关的可行空间。总共测试了40个非线性目标函数，并将其结果与新型详尽拒绝采样常规产生的数据进行了比较。然后使用Hausdorff距离，即最小值集合度量，然后用于评估每个非线性目标函数的表现效果（即测试的目标函数能够探索非convex功率流量空间）。从五个PGLIB测试案例中收集了详尽的测试结果，并进行了系统分析。
This paper provides a systematic investigation into the various nonlinear objective functions which can be used to explore the feasible space associated with the optimal power flow problem. A total of 40 nonlinear objective functions are tested, and their results are compared to the data generated by a novel exhaustive rejection sampling routine. The Hausdorff distance, which is a min-max set dissimilarity metric, is then used to assess how well each nonlinear objective function performed (i.e., how well the tested objective functions were able to explore the non-convex power flow space). Exhaustive test results were collected from five PGLib test-cases and systematically analyzed.
