基于Bagging集成学习的电力系统暂态稳定在线评估

针对传统机器学习在处理暂态稳定评估时所表现出的稳定性差、精度低等问题以及离线训练的局限性，提出一种基于多模型融合Bagging集成学习方式的电力系统暂态稳定在线评估模型。首先，结合人工智能前沿理论研究，分析了暂态稳定评估中常用的7种机器学习算法的原理及实现方式，通过Bagging方法进行集成，充分发挥各个模型的优势。其次，给出Bagging集成的数学实现方法并进行了仿真实验。当原系统拓扑结构发生改变时，采用Boosting算法和迁移成分分析，分别对原电网历史数据进行样本迁移和特征迁移，完成对所提模型的在线更新。通过采用IEEE10机39节点系统和IEEE16机68节点系统进行分析，结果表明所提方法比传统机器学习模型精度更高。当数据中掺杂噪声时能够保持稳定运行，在系统拓扑改变时能够通过迁移历史数据进行准确的暂态稳定评估。

On-line transient stability assessment of a power system based on Bagging ensemble learning

To solve the problems of poor stability and low accuracy of traditional machine learning in transient stability assessment and the limitations of offline training, an online transient stability assessment model based on multi-model fusion Bagging ensemble learning method is proposed. First, in combination with research on the frontier theory of artificial intelligence, the principles and implement methods of seven machine learning algorithms commonly used in transient stability assessment are analyzed, and the Bagging method is used to integrate them to give full play to the advantages of each model. Secondly, the mathematical method of Bagging ensemble learning is given and a simulation experiment is carried out. When the topological structure of the original system changes, a Boosting algorithm and transfer component analysis are used to carry out sample and feature transfer of the original grid historical data to complete the online update of the proposed model. IEEE10-machine 39-bus system and IEEE16-machine 68-bus system are used in the simulation analysis, and the results show that the proposed method is more accurate than the traditional machine learning model. It can maintain stable operation when the data is mixed with noise, and accurately evaluate transient stability by transferring the historical data when the system topology changes. This work is supported by the National Key Research and Development Program of China (No. 2017YFB0902600).