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


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.


Electrical and Computer Engineering

Keywords and Phrases

Adaptive Particle Swarm Optimization Algorithm; Adaptive Particle Swarm Optimizations; Brushless Generators; Computation Techniques; Convergence Speed; Detailed Modeling; Exciter Machine; Field Currents; Field Voltage; Inertia Weight; Local Convergence; Model Parameters; Non-Sinusoidal; Optimization Problems; Real Systems; Real-Coded Genetic Algorithm; Rotor Position; Winding Inductance; Convergence of Numerical Methods; Genetic Algorithms; Identification (control Systems); Inductance; Parameter Estimation; Particle Swarm Optimization (PSO); Adaptive Particle Swarm Optimization; Genetic Algorithm

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