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Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

Metaheuristic Design and Optimization of Fuzzy-Based Gas Turbine Engine Fuel Controller Using Hybrid Invasive Weed Optimization/Particle Swarm Optimization Algorithm

[+] Author and Article Information
E. Mohammadi

e-mail: ehs_mohammadi@iust.ac.ir

M. Montazeri-Gh

Professor
e-mail: montazeri@iust.ac.ir

P. Khalaf

e-mail: P_Khalaf@mecheng.iust.ac.ir
Systems Simulation and Control Laboratory,
Department of Mechanical Engineering,
Iran University of Science and Technology (IUST),
Tehran 16846-13114, Iran

1Corresponding author.

Contributed by the Controls, Diagnostics and Instrumentation Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received April 3, 2013; final manuscript received October 20, 2013; published online November 27, 2013. Editor: David Wisler.

J. Eng. Gas Turbines Power 136(3), 031601 (Nov 27, 2013) (9 pages) Paper No: GTP-13-1093; doi: 10.1115/1.4025884 History: Received April 03, 2013; Revised October 20, 2013

This paper presents the metaheuristic design and optimization of fuzzy-based gas turbine engine (GTE) fuel flow controller by means of a hybrid invasive weed optimization/particle swarm optimization (IWO/PSO) algorithm as an innovative guided search technique. In this regard, first, a Wiener model for the GTE as a block-structured model is developed and validated against experimental data. Subsequently, because of the nonlinear nature of GTE, a fuzzy logic controller (FLC) strategy is considered for the engine fuel system. For this purpose, an initial FLC is designed and the parameters are then tuned using a hybrid IWO/PSO algorithm where the tuning process is formulated as an engineering optimization problem. The fuel consumption, engine safety, and time response are the performance indices of the defined objective function. In addition, two sets of weighting factors for objective function are considered, whereas in one of them savings in fuel consumption and in another achieving a short response time for the engine is a priority. Moreover, the optimization process is performed in two stages during which the database and the rule base of the initial FLC are tuned sequentially. The simulation results confirm that the IWO/PSO-FLC approach is effective for GTE fuel controller design, resulting in improved engine performance as well as ensuring engine protection against physical limitations.

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Figures

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Fig. 1

Schematic of the closed-loop control system

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Fig. 2

Optimization strategy (tuning RB and DB sequentially)

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Fig. 3

Encoding the DB parameters

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Fig. 4

Encoding the RB parameters

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Fig. 5

Rotor speed—comparison of model results to engine test data

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Fig. 6

Membership functions for (a) first input, (b) second input, (c) output variable, case 2

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Fig. 7

Transient fuel flow versus rotor speed error and its derivation, case 2

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Fig. 8

Convergence of best and mean objective values in DB and RB optimization process, case 2

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Fig. 9

The engine parameters before and after the optimization

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Fig. 10

Rotor acceleration

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