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 language | English |
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Pages (from-to) | 273-287 |
Number of pages | 15 |
Journal | Engineering Applications of Artificial Intelligence |
Volume | 20 |
Issue number | 2 |
DOIs | |
Publication status | Published - Mar 2007 |
Externally published | Yes |
Keywords
- Fuzzy systems
- Genetic algorithms
- Industrial power systems
- Neuro-fuzzy identification
- Temperature control
ASJC Scopus subject areas
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering