For many actual distribution systems, an accurate system model might not be available, so the operator has to fit an approximate power flow model over a set of field measurements. To completely shield the adverse impact of outliers that are unavoidable in a training dataset, this letter proposes a novel outlier-immune method to construct a data-driven linear power flow (DD-LPF) model that exhibits a much better out-of-sample accuracy than those constructed by common approaches. Moreover, a continuous relaxation-rounding algorithm is proposed to further accelerate the training process. The computational time of constructing this proposed DD-LPF model is satisfactory enough, which underlines its potential applicability for field applications.

中文翻译：

---

为模型缺失的配电系统构建异常值免疫数据驱动的潮流模型


对于许多实际的配电系统，可能无法获得准确的系统模型，因此操作员必须在一组现场测量中拟合近似的潮流模型。为了完全屏蔽训练数据集中不可避免的异常值的不利影响，这封信提出了一种新的异常值免疫方法来构建数据驱动的线性功率流 （DD-LPF） 模型，该模型表现出比常用方法构建的样本外准确性高得多。此外，还提出了一种连续的松弛舍入算法，以进一步加速训练过程。构建该 DD-LPF 模型的计算时间足够令人满意，这凸显了其在现场应用的潜在适用性。