Alternate Support Vector Machine Decision Trees for Power Systems Rule Extractions

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.