On-line self-learning PID controller design of SSSC using self-recurrent wavelet neural networks

Soheil GANJEFAR; Mojtaba ALIZADEH

Makale detayları

On-line self-learning PID controller design of SSSC using self-recurrent wavelet neural networks

Yazarlar:

Soheil GANJEFAR | Mojtaba ALIZADEH

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 4, pp.980-1001

Belge Tipi:

Makale

Yayın Tarihi:

3/2013

Belge Dili:

Türkçe

Document ID (Org No.):

2000743255

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Conventionally, FACTS devices employ a proportional-integral  (PI) controller as a supplementary controller.  However, the  conventional PI controller has many disadvantages.  The present paper  aims to propose an on-line self-learning PI-derivative (PID)  controller design of a static synchronous series compensator for power  system stability enhancement and to overcome the PI controller  problems.  Unlike the PI controllers, the proposed PID controller has  a local nature because of its powerful adaption process, which is  based on the back-propagation (BP) algorithm that is carried out  through an adaptive self-recurrent wavelet neural network identifier  (ASRWNNI).  In fact, the PID controller parameters are updated in  on-line mode using the BP algorithm based on the information provided  by the ASRWNNI, which is a powerful and fast-acting identifier thanks  to its local nature, self-recurrent structure, and stable training  algorithm with adaptive learning rates based on the discrete Lyapunov  stability theorem.  The proposed control scheme is applied to a  2-machine, 2-area power system under different operating conditions  and disturbances to demonstrate its effectiveness and robustness.  Later on, the design problem is extended to a 4-machine, 2-area  benchmark system and the results show that the interarea modes of the  oscillations are well damped with the proposed approach.

 

Conventionally, FACTS devices employ a proportional-integral  (PI) controller as a supplementary controller.  However, the  conventional PI controller has many disadvantages.  The present paper  aims to propose an on-line self-learning PI-derivative (PID)  controller design of a static synchronous series compensator for power  system stability enhancement and to overcome the PI controller  problems.  Unlike the PI controllers, the proposed PID controller has  a local nature because of its powerful adaption process, which is  based on the back-propagation (BP) algorithm that is carried out  through an adaptive self-recurrent wavelet neural network identifier  (ASRWNNI).  In fact, the PID controller parameters are updated in  on-line mode using the BP algorithm based on the information provided  by the ASRWNNI, which is a powerful and fast-acting identifier thanks  to its local nature, self-recurrent structure, and stable training  algorithm with adaptive learning rates based on the discrete Lyapunov  stability theorem.  The proposed control scheme is applied to a  2-machine, 2-area power system under different operating conditions  and disturbances to demonstrate its effectiveness and robustness.  Later on, the design problem is extended to a 4-machine, 2-area  benchmark system and the results show that the interarea modes of the  oscillations are well damped with the proposed approach.

Makale detayları

On-line self-learning PID controller design of SSSC using self-recurrent wavelet neural networks

Yazarlar: Soheil GANJEFAR | Mojtaba ALIZADEH

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 4, pp.980-1001

Belge Tipi: Makale

Yayın Tarihi: 3/2013

Belge Dili: Türkçe

Document ID (Org No.): 2000743255

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Soheil GANJEFAR | Mojtaba ALIZADEH

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 4, pp.980-1001

Belge Tipi:

Makale

Yayın Tarihi:

3/2013

Belge Dili:

Türkçe

Document ID (Org No.):

2000743255

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632