Abstract
This paper, deals with a meta-heuristic method, the Particle Swarm Optimization (PSO), for operational parameters identification of synchronous machine. The considered method consists of minimizing quadratic criterion that represents the difference between simulated model at standstill frequency response output and those computed from the model to be identified. The obtained results by simulation show that the method based on particle swarm optimization is efficient in terms of convergence speed and gives optimal solution.
Highlights
In the last years, there has been a growing interest to the field of accurate determination of the d-q axis equivalent circuit parameters for the simulation of synchronous machines
The current paper presents a model-based parameter identification algorithm for synchronous machine equivalent circuit using particle swarm optimization
The paper is organized as follows: in Section 2, we introduce the mechanism of particle swarm optimization
Summary
There has been a growing interest to the field of accurate determination of the d-q axis equivalent circuit parameters for the simulation of synchronous machines. Much analytical methods such as Marquardt algorithm [1], Hartley Series [2], Volterra Series [3], maximum likelihood estimation (MLE) [4] have been proposed. In [6], a Genetic Algorithms (GAs) approach is used to identify the desired parameters Another method based on Hybrid genetic algorithms is used in [7] to find high-order equivalent circuits of synchronous machines. The current paper presents a model-based parameter identification algorithm for synchronous machine equivalent circuit using particle swarm optimization.
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