Using Fuzzy Logic to Control Combined Cycle Gas Turbine During Ambient Computing Environment

Using Fuzzy Logic to Control Combined Cycle Gas Turbine During Ambient Computing Environment

Mostafa A. Elhosseini (Taibah University, Medina, Saudi Arabia)
Copyright: © 2020 |Pages: 25
DOI: 10.4018/IJACI.2020070106
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Abstract

The main aim of this article is to analyse and control a combined cycle gas turbine (CCGT) under normal and perturbation loading using a Fuzzy Logic Control (FLC) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) through an ambient computing environment. The main characteristics of ambient computing is invisible, embedded, easy to use, and adaptive to name a few. The current article proposes the employment of FLC and to control the operation of CCGT considering the system inputs uncertainty. The target of the FLC is to maintain the system speed, exhaust temperature, and airflow within the desired interval. ANFIS helps to get the optimal control parameter and construct the proper rule base with an appropriate membership function with reasonable accuracy. The simulation results demonstrate the ANFIS controller's superior performance over FLC as well as the traditional controller for normal operating conditions and load perturbation.
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Introduction

Due to the continuously increased load demand and the shortage of the generation, Egypt suffered from daily disconnection of electricity in the last few years (Anonymous, 2019). The Emergence of CCGT-based generation units has a substantial spread significant to keep system frequency inside boundaries and also increase the reliability as well as the security of the power system. CCGT has a lot of technical, economic and environmental (Colpier et al., 2002) benefits as:

  • Helps in grid frequency regulation, high energy efficiency, quicker reaction time and therefore leads to high flexibility against load fluctuations;

  • Low installation and maintenance costs;

  • Low emission compared to other conventional thermal units.

Literature reported that, continuous efforts of presenting accurate mathematical models of CCGT had been carried out. Many researchers have built mathematical models for the crucial part of CCGT, gas turbines (GT), based on Rowen's heavy GT model (Rowen, 1983; Rowen, 1992; Ivanova et al., 2017; Li et al., 2017). Some authors have driven the further development of CCGT model. IEEE working groups (De Mello et al., 1994) investigate the IEEE dynamic performance model by the. Kunitomi et al. (Kunitomi et al., 1994) modeled the multi-stage model of the steam turbine (ST) and heat recovery steam generator (HRSG). The significant difficulties for accurate representation in long term simulations of the power system of this model are a large number of parameters, and non-linear behaves of HRSG and ST. Besides, CCGT outputs are always regulated by the gas turbine control system and rarely regulated by HRSG and steam turbine during dynamic characteristic analysis. There are many pieces of research (Shalan et al., 2010; Hasan et al., 2014; Baba et al., 2003) describe the HRSG and steam turbine in two simple transfer function modules. The parametric examination of the CCGT with the temperature control was studied by Ning and Lu (Ning et al., 2006) due to its significant effect on CCGT output and its importance for investigating unit performance.

Many researchers considered the change in CCGT dynamic response due to the change in system frequency (Jafari et al., 2010; Kunitomi et al., 2001; Sasaki et al., 2001; Olga et al., 2017; Rai et al., 2018). However, N. Kakimoto and K. Baba (Baba et al., 2003) have studied the detailed analysis that has been lacked in the previous researches which explain how the plant variables behave in frequency drop. Other concepts have been presented in (Iliescu et al., 2008) (Tica et al., 2012) which discussed acceleration control effects on the dynamic performance of the GT during startup and shutdown periods. Applying Proportional Integral Derivative (PID) controller to the governor gas turbine has been suggested in (Hannett et al., 1993; Zhang et al., 2000) to enhance the CCGT dynamic performance. The simulation results indicated that the dynamic system response has improved. Nevertheless, the optimal constant gains of the PID controller for automated generation control are found at specific operating conditions.

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