基于稀疏斜决策树的电力系统安全规则提取与嵌入
Sparse Oblique Decision Tree for Power System Security Rules Extraction and Embedding
摘要：
增加可变发电的渗透对电力系统的运行可靠性有实质性的影响。这种可变性所产生的更高程度的不确定性使得更难以确定给定的操作条件是安全的还是不安全的。数据驱动技术提供了一种有前途的方法来识别安全规则，可以嵌入到经济调度模型，以保持电力系统运行状态的安全。本文提出了使用稀疏加权斜决策树来学习准确，可理解和可嵌入的安全规则，这些规则是线性的，可以使用递归算法提取为稀疏矩阵。然后，这些矩阵可以很容易地嵌入作为安全约束的电力系统经济调度计算使用大M方法。在几个具有高可再生能源渗透率的大型数据集上的测试表明了该方法的有效性。特别是，稀疏加权斜决策树优于最先进的加权斜决策树，同时保持安全规则简单。当嵌入到经济调度中时，这些规则显着增加了安全状态的百分比，并减少了平均求解时间。
英文摘要：
Increasing the penetration of variable generation has a substantial effect on the operational reliability of power systems. The higher level of uncertainty that stems from this variability makes it more difficult to determine whether a given operating condition will be secure or insecure. Data-driven techniques provide a promising way to identify security rules that can be embedded in economic dispatch model to keep power system operating states secure. This paper proposes using a sparse weighted oblique decision tree to learn accurate, understandable, and embeddable security rules that are linear and can be extracted as sparse matrices using a recursive algorithm. These matrices can then be easily embedded as security constraints in power system economic dispatch calculations using the Big-M method. Tests on several large datasets with high renewable energy penetration demonstrate the effectiveness of the proposed method. In particular, the sparse weighted oblique decision tree outperforms the state-of-art weighted oblique decision tree while keeping the security rules simple. When embedded in the economic dispatch, these rules significantly increase the percentage of secure states and reduce the average solution time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
