Induction motor parameter estimation using metaheuristic methods

Ali İhsan ÇANAKOĞLU; Asım Gökhan YETGİN; Hasan TEMURTAŞ; Mustafa TURAN

Makale detayları

Induction motor parameter estimation using metaheuristic methods

Yazarlar:

Ali İhsan ÇANAKOĞLU | Asım Gökhan YETGİN | Hasan TEMURTAŞ | Mustafa TURAN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 5, pp.1177-1192

Belge Tipi:

Makale

Yayın Tarihi:

4/2014

Belge Dili:

Türkçe

Document ID (Org No.):

2000742853

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

The steady-state equivalent circuit parameters of an  induction motor can be estimated using the operation characteristics that  are provided by manufacturers. The characteristics of the motor used in  estimation methods are the starting, maximum, and nominal torque values; the  power factor; and efficiency. The operation characteristics of a motor given  in data sheets are generally based on design parameters and are not suitable  with real values. For this reason, in this paper, the data used in the  parameter estimation for induction motors are taken from the literature.  Using an optimization method for parameter estimation is useful for  comparing the manufacturer values and values at the end of estimation, as  well as minimizing the error in between. There are many methods in the  literature for the parameter estimation of induction motors. In this study,  the estimation is made using the charged system search (CSS), differential  evolution algorithm (DEA), particle swarm optimization, and genetic  algorithm optimization techniques. The CSS algorithm is first applied for  estimation of the parameters of an induction motor. The results obtained  from all of the methods show that the CSS algorithm is suitable with the  DEA. From the obtained results, it is understood that an exact approach can  be made to equivalent circuit parameters in case the values given by the  manufacturer model the motor properly.

 

The steady-state equivalent circuit parameters of an  induction motor can be estimated using the operation characteristics that  are provided by manufacturers. The characteristics of the motor used in  estimation methods are the starting, maximum, and nominal torque values; the  power factor; and efficiency. The operation characteristics of a motor given  in data sheets are generally based on design parameters and are not suitable  with real values. For this reason, in this paper, the data used in the  parameter estimation for induction motors are taken from the literature.  Using an optimization method for parameter estimation is useful for  comparing the manufacturer values and values at the end of estimation, as  well as minimizing the error in between. There are many methods in the  literature for the parameter estimation of induction motors. In this study,  the estimation is made using the charged system search (CSS), differential  evolution algorithm (DEA), particle swarm optimization, and genetic  algorithm optimization techniques. The CSS algorithm is first applied for  estimation of the parameters of an induction motor. The results obtained  from all of the methods show that the CSS algorithm is suitable with the  DEA. From the obtained results, it is understood that an exact approach can  be made to equivalent circuit parameters in case the values given by the  manufacturer model the motor properly.

Makale detayları

Induction motor parameter estimation using metaheuristic methods

Yazarlar: Ali İhsan ÇANAKOĞLU | Asım Gökhan YETGİN | Hasan TEMURTAŞ | Mustafa TURAN

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 5, pp.1177-1192

Belge Tipi: Makale

Yayın Tarihi: 4/2014

Belge Dili: Türkçe

Document ID (Org No.): 2000742853

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Ali İhsan ÇANAKOĞLU | Asım Gökhan YETGİN | Hasan TEMURTAŞ | Mustafa TURAN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 5, pp.1177-1192

Belge Tipi:

Makale

Yayın Tarihi:

4/2014

Belge Dili:

Türkçe

Document ID (Org No.):

2000742853

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632