SVC supplementary damping control using direct neural dynamic programming

Chao Lu, Jennie Si, Xiaorong Xie, Luyuan Tong, James Dankert

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The great scales, nonlinearities and uncertainties in modern power systems mean that they are among the most intractable problems in dynamic control. In the present paper, direct neural dynamic programming (direct NDP) is introduced for a real time supplementary control application. Direct NDP is an on-line learning control paradigm that learns to improve system performance by following the computed gradient toward meeting the overall learning objective. As such the method makes use of on-line measurements to generate proper control actions. This feature is of critical significance when dealing with dynamic systems that are difficult to model or model precisely. In this paper, a static var compensator (SVC) supplementary damping control in a 4-generator 2-area system is implemented using direct NDP. The self-learning and adaptive abilities of direct NDP are analyzed in the MATLAB environment. Simulation results demonstrate the advantages of direct NDP over conventional control.

Original languageEnglish (US)
Title of host publicationIEEE International Symposium on Intelligent Control - Proceedings
Pages270-274
Number of pages5
StatePublished - 2004
EventProceedings of the 2004 IEEE International Symposium on Intelligent Control - 2004 ISIC - Taipei, Taiwan, Province of China
Duration: Sep 2 2004Sep 4 2004

Other

OtherProceedings of the 2004 IEEE International Symposium on Intelligent Control - 2004 ISIC
CountryTaiwan, Province of China
CityTaipei
Period9/2/049/4/04

Keywords

  • Direct neural dynamic programming
  • Dynamic stability
  • Power system
  • Svc supplementary control

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'SVC supplementary damping control using direct neural dynamic programming'. Together they form a unique fingerprint.

  • Cite this

    Lu, C., Si, J., Xie, X., Tong, L., & Dankert, J. (2004). SVC supplementary damping control using direct neural dynamic programming. In IEEE International Symposium on Intelligent Control - Proceedings (pp. 270-274)