基于斜回归树及其集成算法的静态电压稳定规则提取

可再生能源渗透率的增加给电力系统安全稳定运行带来持续性的挑战，传统方法分析系统稳定性、控制电网稳定运行变得愈加困难。针对这一难题，提出了内嵌安全稳定约束的电力系统优化运行框架以及用于电力系统安全稳定规则提取的斜回归树及其集成算法。该算法首先优化斜划分系数以训练单棵斜回归树，然后利用boosting思想集成斜回归树，并通过正则化方法保证树的稀疏度，增强算法的可解释性。相比神经网络等黑箱模型，文中提出的方法能够提取显式安全稳定规则，为内嵌安全稳定约束的电力系统优化运行奠定了基础。最后，以静态电压稳定问题为例验证算法的有效性，算例验证结果表明所提算法具有良好的可解释性、较强的表示能力和较高的集成效率。

Extracting of Static Voltage Stability Rule Based on Oblique Regression Tree and Its Ensemble Algorithm

The increase of the penetration of renewable energy brings continuous challenges to the safe and stable operation of the power system. It becomes more and more difficult to analyze the system stability and control the stable operation of the power system by traditional methods. To solve this problem, a power system optimal operation framework with embedded security and stability constraints and an oblique regression tree and its ensemble algorithm for extracting power system security and stability rules are proposed. The algorithm first optimizes the oblique split coefficient to train a single oblique regression tree, then uses the boosting idea to integrate the oblique regression tree, and uses the regularization method to ensure the sparsity of the tree and enhance the interpretability of the algorithm. Compared with the black box model such as neural network, the proposed method can extract explicit security and stability rules, which lays a foundation for the optimal operation of the power system with embedded security and stability constraints. Finally, the static voltage stability problem is taken as an example to verify the effectiveness of the algorithm. The results show that the algorithm has good interpretability, strong representation ability and high ensemble efficiency.