Direct heuristic dynamic programming for damping oscillations in a large power system

Chao Lu, Jennie Si, Xiaorong Xie

Research output: Contribution to journalArticle

92 Scopus citations

Abstract

This paper applies a neural-network-based approximate dynamic programming method, namely, the direct heuristic dynamic programming (direct HDP), to a large power system stability control problem. The direct HDP is a learning- and approximation-based approach to addressing nonlinear coordinated control under uncertainty. One of the major design parameters, the controller learning objective function, is formulated to directly account for network-wide low-frequency oscillation with the presence of nonlinearity, uncertainty, and coupling effect among system components. Results include a novel learning control structure based on the direct HDP with applications to two power system problems. The first case involves static var compensator supplementary damping control, which is used to provide a comprehensive evaluation of the learning control performance. The second case aims at addressing a difficult complex system challenge by providing a new solution to a large interconnected power network oscillation damping control problem that frequently occurs in the China Southern Power Grid.

Original languageEnglish (US)
Pages (from-to)1008-1013
Number of pages6
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume38
Issue number4
DOIs
StatePublished - Aug 1 2008

Keywords

  • Approximate dynamic programming (ADP)
  • Direct heuristic dynamic programming (direct HDP)
  • Neural networks
  • Power system stability control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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