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Determination of induction motor parameters with differential evolution algorithm

Neural Computing and Applications · Kasım 2012

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YÖKSİS Kayıtları
Determination of induction motor parameters with differential evolution algorithm
Neural Computing and Applications · 2012 SCI-Expanded
Doç. Dr. TAHİR SAĞ →
Determination of induction motor parameters with differential evolution algorithm
Neural Computing and Applications · 2012 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 20 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
Automatic detection and classification of rotor cage faults in squirrel cage induction motor
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Determination of induction motor parameters with differential evolution algorithm
2012 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Short term load forecasting using fuzzy logic and ANFIS
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Fuzzy logic based induction motor protection system
2013 ISSN: 0941-0643 SCI-Expanded
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Determination of induction motor parameters with differential evolution algorithm
2012 ISSN: 0941-0643 SCI-Expanded
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A combination of Genetic Algorithm Particle Swarm Optimization and Neural Network for palmprint recognition
2013 ISSN: 0941-0643 SCI-Expanded 1 atıf
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Cost optimization of mixed feeds with the particle swarm optimization method
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Fuzzy logic based induction motor protection system
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A new MILP model proposal in feed formulation and using a hybrid linear binary PSO H LBP approach for alternative solutions
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A new denoising method for fMRI based on weighted three-dimentional wavelet transform
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FPGA-based self-organizing fuzzy controller for electromagnetic filter
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A new MILP model proposal in feed formulation and using a hybrid-linear binary PSO (H-LBP) approach for alternative solutions
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Hybrid breast cancer detection tem via neural network and feature ion based on SBS SFS and PCA
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Makale Bilgileri

ISSN09410643
Yayın TarihiKasım 2012
Cilt / Sayfa21 · 1995-2004
Özet In this study, the determination of equivalent circuit parameters of induction motors is carried out with differential evolution algorithm (DEA) and genetic algorithm (GA). As an objective function in the algorithms, the sum torque error at zero speed, pull-out, and rated speed is used. The determination of equivalent circuit parameters is performed with three induction motors of 2.2, 5.5, and 37 kW. In particular, the search ability of DEA is compared with GA by using the same population size, number of iteration, and crossover rate. In addition, the effects of the obtained equivalent circuit parameters on induction motors characteristics are investigated and presented with graphics. The results show that the use of DEA instead of GA increases the convergence sensitivity and reduces the simulation time. © 2011 Springer-Verlag London Limited.

Yazarlar (3)

1
Mustafa Arslan
2
Mehmet Çunkaş
3
Tahir Saǧ

Anahtar Kelimeler

Differential evolution algorithm Genetic algorithm Inductions motor Parameter determination

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Neural Computing and Applications
Q1
SJR Skoru1,102
H-Index146
YayıncıSpringer London
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Software (Q1)
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Metrikler

28
Atıf
3
Yazar
4
Anahtar Kelime

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