Robust online dynamic security assessment using adaptive ensemble decision-tree learning

Miao He, Junshan Zhang, Vijay Vittal

Research output: Contribution to journalArticle

96 Scopus citations

Abstract

Online dynamic security assessment (DSA) is examined in a data-mining framework by taking into account the operating condition (OC) variations and possible topology changes of power systems during the operating horizon. Specifically, a robust scheme is proposed based on adaptive ensemble decision tree (DT) learning. In offline training, a boosting algorithm is employed to build a classification model as a weighted voting of multiple unpruned small-height DTs. Then, the small-height DTs are periodically updated by incorporating new training cases that account for OC variations or the possible changes of system topology; the voting weights of the small-height DTs are also updated accordingly. In online DSA, the updated classification model is used to map the real-time measurements of the present OC to security classification decisions. The proposed scheme is first illustrated on the IEEE 39-bus test system, and then applied to a regional grid of the Western Electricity Coordinating Council (WECC) system. The results of case studies, using a variety of realized OCs, illustrate the effectiveness of the proposed scheme in dealing with OC variation and system topology change.

Original languageEnglish
Pages (from-to)4089-4098
Number of pages10
JournalIEEE Transactions on Power Systems
Volume28
Issue number4
DOIs
StatePublished - 2013

Keywords

  • Boosting
  • Data mining
  • Decision tree
  • Ensemble learning
  • Online dynamic security assessment
  • Transient stability

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