Identification and control of power plant de-superheater using soft computing techniques

Ali Ghaffari, Ali Reza Mehrabian, Morteza Mohammad-Zaheri

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

Tight turbine steam temperature control is a necessity for obtaining long lifetime, high efficiency, high load following capability and high availability in power plants. The present work reports a systematic approach for the control strategy design of power plants with non-linear characteristics. The presented control strategy is developed based on optimized PI control with genetic algorithms (GAs) and investigates performance and robustness of the GA-based PI controller (GAPI). In order to design the controller, an effective neuro-fuzzy model of the de-superheating process is developed based on recorded data. Results indicate a successful identification of the high-order de-superheating process as well as improvements in the performance of the steam temperature controller.

Original languageEnglish
Pages (from-to)273-287
Number of pages15
JournalEngineering Applications of Artificial Intelligence
Volume20
Issue number2
DOIs
Publication statusPublished - Mar 2007

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Keywords

  • Fuzzy systems
  • Genetic algorithms
  • Industrial power systems
  • Neuro-fuzzy identification
  • Temperature control

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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