Offline parameter estimation of a modified permanent magnet generator using GSA and GSA-PSO

In this paper, we present a new optimization method to estimate the parameters and torques of an induction motor (IM). The proposed method is known as the Vortex Search Algorithm (VSA), whose performance is based on the behavior of the vortices created by stirred fluids. This algorithm was compared with other four optimization methods reported in the specialized literature (grasshopper optimization algorithm, particle swarm optimization, salp swarm algorithm and sine cosine algorithm), and its solution quality, precision, and robustness were validated using two test motors. During the tests, we analyzed the minimum error between the estimated values and the values provided by the manufacturer, as well as the global error of each method and their required processing time. The results show that the VSA is an excellent alternative to estimate the parameters of an IM, as it exhibited the best performance when compared to the other optimization methods.

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Non-line-of-sight identification from WiFi CSI using particle swarm optimization with multi-dimensional feature fusion

Li,

Guo,

Fei

et al. 2025

Measurement

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Offline parameter estimation of a modified permanent magnet generator using GSA and GSA-PSO

Cited by 4 publications

References 26 publications

Elitist random swapped particle swarm optimization embedded with variable k-nearest neighbour classification: a new PSO variant applied to gene identification

Elitist random swapped particle swarm optimization embedded with variable k-nearest neighbour classification: a new PSO variant applied to gene identification

Estimating the Parameters of a Three-Phase Induction Motor using the Vortex Search Algorithm

Non-line-of-sight identification from WiFi CSI using particle swarm optimization with multi-dimensional feature fusion

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