论文标题
评估机器学习模型，以快速识别应急情况
Evaluating Machine Learning Models for the Fast Identification of Contingency Cases
论文作者
论文摘要
功率流量结果的快速近似对电力系统计划和实时操作有益。在计划中，如果要考虑多年，不同的控制策略或应急政策，则需要数百万的电力流量计算。在实时操作中，网格操作员必须在短时间内评估网格状态是否符合应急要求。在本文中，我们比较回归和分类方法，以预测多变量结果，例如总线电压尺寸和线路负载，或时间步骤的二进制分类，以识别关键负载情况。我们在15分钟内基于时间序列和一年的5分钟分辨率的时间序列上测试了三个逼真的电源系统的方法。我们比较了不同的机器学习模型，例如多层感知器（MLP），决策树，k-nearest邻居，梯度提升，并评估所需的培训时间和预测时间以及预测错误。我们还确定每种方法所需的训练数据量，并显示结果，包括未经训练的生成量的近似。关于比较的方法，我们确定MLP最适合任务。基于MLP的模型可以预测精度为97-98％的关键情况，而假阴性预测数量为0.0-0.64％。
Fast approximations of power flow results are beneficial in power system planning and live operation. In planning, millions of power flow calculations are necessary if multiple years, different control strategies or contingency policies are to be considered. In live operation, grid operators must assess if grid states comply with contingency requirements in a short time. In this paper, we compare regression and classification methods to either predict multi-variable results, e.g. bus voltage magnitudes and line loadings, or binary classifications of time steps to identify critical loading situations. We test the methods on three realistic power systems based on time series in 15 min and 5 min resolution of one year. We compare different machine learning models, such as multilayer perceptrons (MLPs), decision trees, k-nearest neighbours, gradient boosting, and evaluate the required training time and prediction times as well as the prediction errors. We additionally determine the amount of training data needed for each method and show results, including the approximation of untrained curtailment of generation. Regarding the compared methods, we identified the MLPs as most suitable for the task. The MLP-based models can predict critical situations with an accuracy of 97-98 % and a very low number of false negative predictions of 0.0-0.64 %.
