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Alternate Support Vector Machine Decision Trees for Power Systems Rule Extractions
  • ZHANG JIAWEI ,
  • JIA HONGYANG ,
  • Zhang Ning
ZHANG JIAWEI
Tsinghua University

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JIA HONGYANG
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Zhang Ning
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Abstract

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 has significant improvements to the classical decision-tree-based algorithms in terms of efficiency, stability and versatility. Finally, we apply the method to several power and energy system scenarios to show its effectiveness.