论文标题
电力系统可靠性评估的端到端拓扑感知机器学习
End-to-End Topology-Aware Machine Learning for Power System Reliability Assessment
论文作者
论文摘要
常规电力系统的可靠性遭受了蒙特卡洛模拟的长期和分析枚举方法的维度。本文提出了对端到端机器学习的初步研究，以直接预测可靠性指数，例如，负载概率损失（LOLP）。通过将系统接收矩阵编码到输入功能中，建议的机器学习管道可以考虑由于常规的传输线维护而导致的特定拓扑变化的影响。两种型号（支持向量机和增强树）经过训练和比较。还讨论了有关培训数据创建和预处理的详细信息。最后，在IEEE RTS-79系统上进行了实验。结果证明了拟议的端到端机器学习管道在可靠性评估中的适用性。
Conventional power system reliability suffers from the long run time of Monte Carlo simulation and the dimension-curse of analytic enumeration methods. This paper proposes a preliminary investigation on end-to-end machine learning for directly predicting the reliability index, e.g., the Loss of Load Probability (LOLP). By encoding the system admittance matrix into the input feature, the proposed machine learning pipeline can consider the impact of specific topology changes due to regular maintenances of transmission lines. Two models (Support Vector Machine and Boosting Trees) are trained and compared. Details regarding the training data creation and preprocessing are also discussed. Finally, experiments are conducted on the IEEE RTS-79 system. Results demonstrate the applicability of the proposed end-to-end machine learning pipeline in reliability assessment.
