Instantaneous Electromechanical Dynamics Monitoring in Smart Transmission Grid

Junbo Zhang, C. Y. Chung, Zejing Wang, Xiangtian Zheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)

Abstract

Measurement sensors installed in the smart transmission system can acquire big data for electromechanical dynamics monitoring. The time-series data obtained carry information of instantaneous relationship of system oscillation modes with respect to operating conditions. To extract this information, this paper proposes a parallel processed online supervised learning algorithm called k-nearest neighbors 'locally weighted linear regression' (KNN-LWLR), which is an extensive combination of two famous machine-learning algorithms: 1) the KNN learning; and 2) LWLR learning. Its mathematical derivation, implementation, parameter tuning, and application to electromechanical oscillation mode prediction are first described. The proposed algorithm is then validated based on an 8-generator 36-node system with the real operations data.

Original languageEnglish
Article number7302048
Pages (from-to)844-852
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume12
Issue number2
DOIs
Publication statusPublished - Apr 2016
Externally publishedYes

Keywords

  • instantaneous electromechanical dynamics monitoring
  • k-nearest neighbors learning
  • locally weighted regression learning
  • smart transmission grid

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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