Increasing renewable energy penetrations bring complex feasibility and stability problems. Data-driven methods are applied in extracting and embedding these feasibility and stability rules in power system operations and planning. This paper presents a method of alternate support vector machine decision trees for rule extraction problems. The method significantly improves the classical decision-tree-based algorithms' efficiency, stability, and versatility. Finally, we apply the method to several power and energy system scenarios to show its effectiveness.

中文翻译：

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用于电力系统规则提取的备用支持向量机决策树

可再生能源普及率的提高带来了复杂的可行性和稳定性问题。数据驱动方法被应用于提取这些可行性和稳定性规则并将其嵌入到电力系统运行和规划中。本文提出了一种用于规则抽取问题的交替支持向量机决策树方法。该方法显着提高了经典的基于决策树的算法的效率、稳定性和通用性。最后，我们将该方法应用于多个电力和能源系统场景以证明其有效性。