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An algorithm with LightGBM + SVM fusion model for the assessment of dynamic security region

机译:一种具有用于动态安全区域评估的LightGBM + SVM融合模型的算法

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With the development of energy transition, the complexity of power systems’ structure, planning and operation is continuously increasing. As to quickly and accurately assess the dynamic security region of power system, there are prominent problems with traditional manual analysis method, i.e. the rules’ roughness and a low calculation efficiency while data mining approach could provide a new way to get off such problems. Considering that the performance of SVM algorithm depends on feature selection and the LightGBM, a fast and efficient classification algorithm, can be used for feature selection, this paper proposes a new algorithm based on a fusion model. With the CEPRI-36 bus power system, the results of different algorithms are compared and the proposed algorithm verified.
机译:随着能源转型的发展,电力系统结构,规划和操作的复杂性不断增加。 为了快速准确地评估动态安全区域的电力系统,传统的手动分析方法存在突出的问题,即规则“粗糙度和低计算效率,而数据挖掘方法可以提供脱离此类问题的新方法。 考虑到SVM算法的性能取决于特征选择和LightGBM,快速高效的分类算法可用于特征选择,本文提出了一种基于融合模型的新算法。 通过CEPRI-36总线电力系统,比较了不同算法的结果,并验证了所提出的算法。

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