e-journal

Condition Monitoring of Wind Power System With Nonparametric Regression Analysis

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Condition monitoring helps reduce the operations and maintenance costs by providing information about the physical condition of wind power systems. This study proposes to use a statistical method for effective condition monitoring. The turbine operation is significantly affected by external weather conditions.We
model the wind turbine response as a function of weather variables, using a nonparametric regression method named least squares support vector regression. In practice, online condition monitoring of
wind power systems often relies on datasets contaminated with outliers. This study proposes to use a weighted version of least squares support vector regression that provides a formal procedure for removing
the outlier effects. We determine the decision boundaries to distinguish faulty conditions from normal conditions by examining the variations in the operational responses that are significantly affected by external weather. The results show that the proposed method effectively detects anomalies.
Index Terms—Control chart, fault diagnosis, statistical process control, support vector regression (SVR), wind energy.

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Ketersediaan

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON ENERGY CONVERSION

No. Panggil

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Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 29, NO. 2, JUNE 2014

Bahasa

English

ISBN/ISSN

0885-8969

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 29, NO. 2, JUNE 2014

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

Lampiran Berkas

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