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

Authors:
Ali Ghaffari;Ali Reza Mehrabian;Morteza Mohammad-Zaheri
Affiliations:
Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran;School of Mechanical Engineering, Department of Engineering, University of Tehran, P.O. Box 14875-347, Tehran, Iran;Engineering Department of Islamic Azad University, Semnan Branch, P.O. Box 16535-335, Tehran, Iran
Venue:
Engineering Applications of Artificial Intelligence
Year:
2007

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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.