Synchronous generator parameters estimation using genetic algorithm via DC decay field tests

Jahromi, M.E.; Rasouli, Mohammad; Ranjbar, Mohammad

doi:10.1109/ptc.2005.4524728

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2019

2022

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [5], an Artificial Neural Network (ANN) based modelling technique for the rotor body parameters of synchronous generator is developed and tested. 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.…”

Section: Introductionmentioning

confidence: 99%

Parameters Identification of Synchronous Machine Based on Particale Swarm Optimization

2022

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%