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    Abstract
2013 (Vol. 5, Issue: 05)
Article Information:

Designing a Robust and Adaptive PID Controller for Gas Turbine Connected to the Generator

Mohsen Khalilpour, Khalil Valipour, Hossein Shayeghi and Navid Razmjooy
Corresponding Author:  Navid Razmjooy 

Key words:  Artificial bee colony, control temperature and load-frequency, gas turbine, load disturbances, optimal control, robust control, stability analysis
Vol. 5 , (05): 1544-1551
Submitted Accepted Published
June 30, 2012 August 08, 2012 February 11, 2013
Abstract:

Gas turbines are increasingly spread throughout the world to provide mechanical and electrical power in consumer and industrial sections. To ensure an accurate control process temperature of gas turbine with no extortionary operator involvement, a proper controller is required. Load frequency control of gas turbine is also regulates the power flow between different areas while holding the frequency constant. The main idea in this study is to assemble these 2 controllers in a unit work; the area of robust control has grown to be one of the wealthy in terms of algorithms, design techniques, analytical tools and modifications. Several books and papers already exist on the topics of parameter estimation and adaptive control. In The proposed approach, a robust and evolutionary based Proportional, Integral, Derivative (PID) is utilized to control frequency-response and a robust evolutionary based Proportional, Integral (PI) is utilized to control temperature. The evolutionary algorithm is used to make an optimal Proportional-Integral-Derivative (PID) controller Tuning parameters. The new robust PID controller is compared with a normal classic controller (Ziegler-Nichols) designed by the method.
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  Cite this Reference:
Mohsen Khalilpour, Khalil Valipour, Hossein Shayeghi and Navid Razmjooy, 2013. Designing a Robust and Adaptive PID Controller for Gas Turbine Connected to the Generator.  Research Journal of Applied Sciences, Engineering and Technology, 5(05): 1544-1551.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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