Adaptive neuro-fuzzy inference system to improve the power quality of variable-speed wind power generation system

Yüksel OĞUZ; İrfan GÜNEY

Makale detayları

Adaptive neuro-fuzzy inference system to improve the power quality of variable-speed wind power generation system

Yazarlar:

Yüksel OĞUZ | İrfan GÜNEY

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2010 Vol.18 Issue 4, pp.625-646

Belge Tipi:

Makale

Yayın Tarihi:

3/2010

Belge Dili:

Türkçe

Document ID (Org No.):

2000743909

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

In this study, an adaptive neuro-fuzzy inference system is  designed for output voltage and frequency control of a variable-speed  wind power generation system.  Variable-speed wind power generation  systems (VSWPGS) provide the opportunity to capture more power than  fixed speed turbines.  On the other hand, the variable-speed wind  turbine output can be variable voltage and variable frequency for  fluctuating wind speeds.  The quality of output power can be improved  if adequate controls are incorporated in the system.  To bring the  output voltage and frequency of system by means of control of blade  pitch angle of wind turbine to a desirable value, an adaptive  neuro-fuzzy inference system (ANFIS) is used in this paper.  Based on  the dynamic performance of VSWPGS, ANFIS is designed.  Control and  dynamic performance analysis of VSWPGS is made depending on various  loading situations.  Dynamic modeling, control and simulation study of  the wind power generation system is performed with MATLAB/Simulink  program.  The simulation results obtained through various load  situations and detailed analysis of so established simulation model is  given in this study.  As it can be seen in simulation results, the  output voltage and frequency of ANFIS controlled variable-speed wind  power generation system reach to desirable operation values in a very  short time.

 

In this study, an adaptive neuro-fuzzy inference system is  designed for output voltage and frequency control of a variable-speed  wind power generation system.  Variable-speed wind power generation  systems (VSWPGS) provide the opportunity to capture more power than  fixed speed turbines.  On the other hand, the variable-speed wind  turbine output can be variable voltage and variable frequency for  fluctuating wind speeds.  The quality of output power can be improved  if adequate controls are incorporated in the system.  To bring the  output voltage and frequency of system by means of control of blade  pitch angle of wind turbine to a desirable value, an adaptive  neuro-fuzzy inference system (ANFIS) is used in this paper.  Based on  the dynamic performance of VSWPGS, ANFIS is designed.  Control and  dynamic performance analysis of VSWPGS is made depending on various  loading situations.  Dynamic modeling, control and simulation study of  the wind power generation system is performed with MATLAB/Simulink  program.  The simulation results obtained through various load  situations and detailed analysis of so established simulation model is  given in this study.  As it can be seen in simulation results, the  output voltage and frequency of ANFIS controlled variable-speed wind  power generation system reach to desirable operation values in a very  short time.

Makale detayları

Adaptive neuro-fuzzy inference system to improve the power quality of variable-speed wind power generation system

Yazarlar: Yüksel OĞUZ | İrfan GÜNEY

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2010 Vol.18 Issue 4, pp.625-646

Belge Tipi: Makale

Yayın Tarihi: 3/2010

Belge Dili: Türkçe

Document ID (Org No.): 2000743909

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Yüksel OĞUZ | İrfan GÜNEY

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2010 Vol.18 Issue 4, pp.625-646

Belge Tipi:

Makale

Yayın Tarihi:

3/2010

Belge Dili:

Türkçe

Document ID (Org No.):

2000743909

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632