Power system control with an embedded neural network in hybrid system modeling

Seung Mook Baek, Jung Wook Park, Gamesh Kumar Venayagamoorthy

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Output limits of the power system stabilizer (PSS) can improve the system damping performance immediately following a large disturbance. Due to nonsmooth nonlinearities arising from the saturation limits, these values cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures can been used. A feedforward neural network (with a structure of multilayer perceptron neural network) is applied to identify the dynamics of an objective function formed by the states and, thereafter, to compute the gradients required in the nonlinear parameter optimization. Moreover, its derivative information is used to replace that obtained from the trajectory sensitivities based on the hybrid system model with the differential-algebraic-impulsive-switched structure. The optimal output limits of the PSS tuned by the proposed method are evaluated by time-domain simulation in both a single-machine infinite bus system and a multimachine power system.

Original languageEnglish
Pages (from-to)1458-1465
Number of pages8
JournalIEEE Transactions on Industry Applications
Volume44
Issue number5
DOIs
Publication statusPublished - 2008 Oct 13

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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