Modeling and Control of Combined Cycle Gas Turbines Using Rowen’s and Vournas’s Models


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Abstract


The gas turbine is known to feature low capital cost, high flexibility, high reliability without complexity, short delivery time, early commissioning and commercial operation, and fast starting as well as loading. The gas turbine is further recognized for its better environmental performance apparent in reducing air pollution as well as the greenhouse effect. The gas turbine can be configured as a simple (stand-alone) cycle plant or a part of one of the new technologies adopted in last decades for improvement which known as "Combined Cycle Power Plant". A Combined-Cycle Power Plant (CCPP) can be seen as coupling of a Gas Turbine (GT) and a Steam Turbine (ST) through a Heat Recovery Steam Generator (HRSG). Overall system efficiency can be greatly improved by linking together these two different thermal cycles. Recently, several gas turbine models have been suggested with different degree of complexity and success. The purpose of this paper is improving the behaviour of the gas turbine-based plants to investigate the associated power system control problems. Two gas turbine models will be used in this article which are Rowen’s and Vournas’s models. These models are studied when PID controller is used in its construction for speed and temperature control. Improving the gas turbine behaviour can be achieved by applying different artificial intelligent adaptation techniques on the PID controller of the dynamic models of Combined Cycle Power Plants (CCPPs). Tuning the parameters of a PID controller is very important. The well-known method to tune the coefficients of a PID controller is the Ziegler-Nichols method. This tuning method is very simple, but cannot guarantee to be always effective. For this reason, this paper investigates the tuning for a PID using Fuzzy, Genetic Algorithm and Particle Swarm Optimization methods. The results of tuning methods will be compared, analyzed and conclusion will be drawn out of the simulation results.
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Keywords


Combined Cycle Power Plants; Fuzzy Logic Control; Genetic Algorithm; Rowen’s and Vournas’s Models; Particle Swarm Optimization; PID Controller

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