Data Reduction for Dynamic Stability Classification in Power System
作者: Ngoc Au NguyenTrong Nghia LeHuy Anh QuyenThi Thanh Binh Phan
作者单位: 1Faculty of Electrical and Electronics Engineering , HCMC University of Technology and Education , Ho Chi Minh City , Vietnam
刊名: IETE Journal of Research, 2019, Vol.65 (2), pp.148-156
来源数据库: Taylor & Francis Journal
DOI: 10.1080/03772063.2017.1417752
关键词: Data clusteringFeature selectionPower system stabilityPrediction/recognition/classification
原始语种摘要: Abstract(#br)A large oscillation of power system caused by faults must be quickly treated so that the opportunity driving power system into re-stability state can be easier. Necessity is to select a compact data-set that is the representative of all data-sets with the aim of reducing computing costs, reducing computer memory, and improving classification accuracy. The K-means (KM) algorithm is the most commonly used clustering algorithm because it can be easily implemented and is the most effective one in terms of the execution time on large data size. The key problem of KM is that it is sensitive to initial center and may converge to a local optimization. In this paper, we proposed the use of Hybrid K-means (HKM) data clustering algorithm that can avoid being trapped in a local optimal...
全文获取路径: Taylor & Francis  (合作)
影响因子:0.2 (2011)

  • clustering 聚类
  • Data 数据
  • reducing 还原
  • quickly 快速地
  • accuracy 准确度
  • stability 稳定性
  • recognition 识别
  • converge 收敛
  • optimization 最佳化
  • classifier 分级机