In this paper, we propose a robust data-driven process model whose hyperparameters are robustly estimated using the Schweppe-type generalized maximum likelihood estimator. The proposed model is trained on recorded time-series data of voltage phasors and power injections to perform a time-series stochastic power flow calculation. Power system data are often corrupted with outliers caused by large errors, fault conditions, power outages, and extreme weather, to name a few. The proposed model downweights vertical outliers and bad leverage points in the measurements of the training dataset. The weights used to bound the influence of the outliers are calculated using projection statistics, which are a robust version of Mahalanobis distances of the time series data points. The proposed method is demonstrated on the IEEE 33-Bus power distribution system and a real-world unbalanced 240-bus power distribution system heavily integrated with renewable energy sources. Our simulation results show that the proposed robust model can handle up to 25% of outliers in the training data set.
翻译:在本文中,我们建议采用Schweppe型通用最大概率估计器对超光速数据驱动程序模型进行强力估算。 提议模型在记录电压振荡器和电喷射的时间序列数据方面进行了培训,以进行时间序列断裂电流计算。 电源系统数据往往被大量错误、断层、断电断电和极端天气造成的外部线所腐蚀。 提议的模型在测量培训数据集时垂直偏差和不良杠杆点。 用于约束外部线影响的权重是用预测统计数据计算的,这是时间序列数据点的马哈拉诺比斯距离的可靠版本。 拟议方法在IEEEE 33-Bus电源配电系统和实时不平衡的240-Bus电源配电系统上展示,它们与可再生能源高度融合。 我们的模拟结果表明,拟议的强力模型可以在培训数据集中处理高达25%的外部线。