论文标题
基于测量的强大非高斯工艺模拟器应用于数据驱动的随机功率流
A Measurement-Based Robust Non-Gaussian Process Emulator Applied to Data-Driven Stochastic Power Flow
论文作者
论文摘要
在本文中，我们提出了一个基于Schweppe型概括的最大似然估计量的强大非高斯工艺模拟器，该估计量在电压时间序列的电压相量和功率注射量中进行了训练，以执行随机功率流。电源系统数据通常被故障条件，停电和极端天气引起的异常值所破坏，仅举几例。提出的模拟器使用基于投影统计计算的权重来界定异常值的影响，这是与因子空间的行向量相关的数据点的稳健距离。具体而言，开发的估计器对垂直异常值和不良杠杆点具有鲁棒性，同时在训练数据集的测量中保留了良好的杠杆点。提出的方法在不平衡的径向IEEE 33总线系统上与可再生能源集成在一起。
In this paper, we propose a robust non-Gaussian process emulator based on the Schweppe-type generalized maximum likelihood estimator, which is trained on metered time series of voltage phasors and power injections to perform stochastic power flow. Power system data are often corrupted with outliers caused by fault conditions, power outages, and extreme weather, to name a few. The proposed emulator bounds the influence of the outliers using weights calculated based on projection statistics, which are robust distances of the data points associated with the rows vectors of the factor space. Specifically, the developed estimator is robust to vertical outliers and bad leverage points while retaining good leverage points in the measurements of the training dataset. The proposed method is demonstrated on an unbalanced radial IEEE 33-Bus system heavily integrated with renewable energy sources.
