In practical AC/DC hybrid grids, the uncertainty sources such as wind speeds and loads are massive and bounded intrinsically, the latter feature of which is neglected in most probabilistic power flow (PPF) analyses unfortunately. In this paper, a scaled unscented transformation (SUT)-based PPF on the large AC/DC hybrid grids, in particular for addressing the boundary issue of these high-dimensional random input variables under Pearson correlation, is proposed and investigated. The empirical formula used for determining the scaling parameter of the SUT method is designed to obtain the sample points, with a purpose of capturing sufficient probability information from the preliminary standard normal distributions. Boundary inverse transformation (BIT) is developed to transform these samples into the original probability space via handling each dimensional boundary value, and to guarantee that all the sample points of SUT fall into a reasonable, physical interval. The effectiveness and advantage of the proposed method are validated by using a set of test results on the modified IEEE 1354-bus (PEGASE) system.

中文翻译：

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解决相关不确定性源边界问题的交直流混合电网的概率潮流


在实际的交直流混合电网中，风速和负荷等不确定性源是巨大的，并且本质上是有界的，不幸的是，后一个特征在大多数概率潮流（PPF）分析中都被忽略了。在本文中，提出并研究了大型交直流混合电网上基于缩放无迹变换（SUT）的PPF，特别是为了解决皮尔逊相关下这些高维随机输入变量的边界问题。设计用于确定SUT方法缩放参数的经验公式来获取样本点，目的是从初步的标准正态分布中捕获足够的概率信息。边界逆变换（BIT）是通过处理各维边界值将这些样本变换到原始概率空间，并保证SUT的所有样本点落入合理的物理区间内。通过使用改进的 IEEE 1354 总线（PEGASE）系统上的一组测试结果验证了所提出方法的有效性和优点。