Optimization Techniques for the Analysis of Self-excited Induction Generator

Saha, Swarup Kumar; Sandhu, K. S.

doi:10.1016/j.procs.2017.12.053

Cited by 13 publications

(1 citation statement)

References 7 publications

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“…Boora et al [10] have used the concept of symmetrical components with the fsolve algorithm to find the unknown parameters of a capacitive excited induction generator (CEIG) under unbalanced operating conditions. Saha and Sandhu [11] have made a comparison for the implementation of genetic algorithm (GA), particle swarm optimization (PSO) and simulated annealing (SA) algorithm for predicting the steady state performance of SEIG feeding balanced resistive load.…”

Section: Introductionmentioning

confidence: 99%

Optimization algorithms for steady state analysis of self excited induction generator

Athamnah,

Anagreh,

Anagreh

2023

IJECE

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The current publication is directed to evaluate the steady state performance of three-phase self-excited induction generator (SEIG) utilizing particle swarm optimization (PSO), grey wolf optimization (GWO), wale optimization algorithm (WOA), genetic algorithm (GA), and three MATLAB optimization functions (fminimax, fmincon, fminunc). The behavior of the output voltage and frequency under a vast range of variation in the load, rotational speed and excitation capacitance is examined for each optimizer. A comparison made shows that the most accurate results are obtained with GA followed by GWO. Consequently, GA optimizer can be categorized as the best choice to analyze the generator under various conditions.

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Section: Introductionmentioning

confidence: 99%