Adaptive Frequency Estimation of Distorted Power System Signals Using Modified Extended Kalman Filter

Cengiz UZUNOĞLU; Serap ÇEKLİ; Mukden UĞUR

Makale detayları

Adaptive Frequency Estimation of Distorted Power System Signals Using Modified Extended Kalman Filter

Yazarlar:

Cengiz UZUNOĞLU | Serap ÇEKLİ | Mukden UĞUR

Yayıncı:

Gazi University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Gazi University Journal of Science 2011 Vol.24 Issue 1, pp.85-89

Belge Tipi:

Makale

Yayın Tarihi:

1/2011

Belge Dili:

İngilizce

Document ID (Org No.):

2000429683

eISSN:

2147-1762

Özet:

 


This research presents a method for frequency estimation of distorted power system signals using a modified Extended Kalman Filter (EKF). Distorted signals which have been obtained from measurements may involve noises which may affect the accuracy of frequency measurement in a power system. Thus, the proposed method is employed to eliminate and filter noise and hence to improve the efficiency of frequency estimation. Computer simulations have been carried out for the performance analysis of the proposed method and the comparison of the results of the proposed method with the former Extended Kalman Filter results are presented. The effectiveness of the new approach in power system frequency estimation was emphasized.

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Makale detayları

Adaptive Frequency Estimation of Distorted Power System Signals Using Modified Extended Kalman Filter

Yazarlar: Cengiz UZUNOĞLU | Serap ÇEKLİ | Mukden UĞUR

Yayıncı: Gazi University

Ana Başlıklar: Uygulamalı fizik

Dergi: Gazi University Journal of Science 2011 Vol.24 Issue 1, pp.85-89

Belge Tipi: Makale

Yayın Tarihi: 1/2011

Belge Dili: İngilizce

Document ID (Org No.): 2000429683

eISSN: 2147-1762

Sayı

Yazarlar:

Cengiz UZUNOĞLU | Serap ÇEKLİ | Mukden UĞUR

Yayıncı:

Gazi University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Gazi University Journal of Science 2011 Vol.24 Issue 1, pp.85-89

Belge Tipi:

Makale

Yayın Tarihi:

1/2011

Belge Dili:

İngilizce

Document ID (Org No.):

2000429683

eISSN:

2147-1762