Research Papers

Employing Adaptive Particle Swarm Optimization Algorithm for Parameter Estimation of an Exciter Machine

[+] Author and Article Information
Ahmad Darabi, Bahare Kiumarsi

Alireza Alfi

 Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood 36199-95161, Irana_alfi@shahroodut.ac.ir

Hamidreza Modares

 Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad 91775-1111, IranHa.modares@um.ac.ir

J. Dyn. Sys., Meas., Control 134(1), 011013 (Dec 05, 2011) (7 pages) doi:10.1115/1.4005371 History: Received April 26, 2010; Revised September 07, 2011; Published December 05, 2011; Online December 05, 2011

Winding inductances of an exciter machine of brushless generator normally consist of nonsinusoidal terms versus rotor position angle, so evaluations of the inductances necessitate detailed modeling and complicated parameter identification procedures. In this paper, an adaptive particle swarm optimization (APSO), which is a novel heuristic computation technique, is proposed to identify parameters of an exciter machine. The proposed approach evaluates the model parameters just knowing the main field impedance, measured exciter field voltage and current. APSO is employed to solve the optimization problem of minimizing the difference between output quantities (exciter field current) of the model and real systems. Two modifications are incorporated into the conventional particle swarm optimization (PSO) scheme that prevents local convergence and provides excellent quality of final result. Performance of the proposed APSO is compared with those of the real-coded genetic algorithm (GA) and PSO with linearly decreasing inertia weight (LDW-PSO), in terms of the parameter accuracy and convergence speed. Simulation results illustrated in the paper show that the proposed APSO is more successful in comparison with LDW-PSO and GA.

Copyright © 2012 by American Society of Mechanical Engineers
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Figure 1

Exciter field and armature windings

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Figure 2

Exciter and rectifier load

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Figure 3

Comparison of convergence of objective function

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Figure 4

Measured and estimated self inductance of the exciter field

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Figure 5

Measured and estimated mutual inductance of phase “a” and “c

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Figure 6

Measured and estimated self inductance of phase “a

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Figure 7

Measured and estimated mutual inductance of the exciter field and phase “a

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Figure 8

APSO result for one of the phase voltage

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Figure 9

APSO result for exciter field current




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