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文章摘要
基于深度森林的电力系统暂态稳定评估方法
Power System Transient Stability Assessment Based on Deep Forest
Received:July 25, 2019  Revised:July 25, 2019
DOI：10.19753/j.issn1001-1390.2021.02.009
中文关键词: 深度学习  暂态稳定评估  深度森林  
英文关键词: deep learning  transient stability assessment  deep forest
基金项目:湖北省电力公司科技项目(521505190005)
Author NameAffiliationE-mail
Li Miao State Grid Hubei Electric Power Company 1024625318@qq.com 
Lei Ming State Grid Hubei Electric Power Company 421411856@qq.com 
Zhou Ting* School of Electrical Engineering and Automation, Wuhan University 348902798@qq.com 
Li Yonglong State Grid Hubei Electric Power Company 496755865@qq.com 
Xiao Yi State Grid Hubei Electric Power Company xiaoyith@163.com 
Yan Binjun State Grid Hubei Electric Power Company 85220710@qq.com 
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中文摘要:
      快速准确地实现暂态稳定评估，是电力系统安全运行的重要保障。近年来迅速发展的深度学习技术已经成为解决这一问题的有效手段，然而基于神经网络的深度学习模型存在着调参困难、训练时间长和样本需求量大等缺点。本文将故障切除时刻系统的物理量作为输入特征，以系统的暂态稳定状态作为输出结果，采用集成决策树方法，构建了基于深度森林的电力系统暂态稳定评估模型。新英格兰39节点系统的算例分析表明，所提方法与深度神经网络相比，参数设置简单、训练速度更快，即使在训练样本数量较少时也能有效避免过拟合，具有良好的泛化能力。
英文摘要:
      
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