Publication Type : Journal Article
Publisher : Taylor & Francis
Source : Electric Power Components and Systems, Taylor & Francis, Volume 37, Number 11, p.1275–1287 (2009)
Keywords : Fuzzy logic, Gas Turbine, Genetic algorithm, neural network, proportional-integral-derivative controller
Campus : Coimbatore
School : School of Engineering
Department : Electrical and Electronics
Year : 2009
Abstract : Gas turbine generators, normally used in isolated operation, require an effective control and design for their parallel operation. Otherwise, the load variations and set-point variations may cause severe stability problems. Soft computing techniques, such as genetic algorithms, artificial neural networks, and fuzzy logic, have been utilized for developing a controller for a gas turbine plant. The proportional-integral-derivative controller is used to control the gas turbine plant because of its versatility, high reliability, and ease of operation. For better performance, the gains of the proportional-integral-derivative controller have been tuned using the Ziegler–Nichols method and genetic algorithm. The artificial neural network and fuzzy controllers are developed, and the performance is compared with the conventional proportional-integral-derivative controller. The results show that the optimal time domain performance of the system can be achieved with the fuzzy logic controller. The fuzzy logic controller removes the steady-state error in less time with no overshoot and oscillation.
Cite this Research Publication : Dr. Balamurugan S., R Joseph Xavier, and A Ebenezer Jeyakumar, “Control of Heavy-duty Gas Turbine Plants for Parallel Operation Using Soft Computing Techniques”, Electric Power Components and Systems, vol. 37, pp. 1275–1287, 2009.