کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10401520 | 891316 | 2011 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Partial discharge signal denoising with spatially adaptive wavelet thresholding and support vector machines
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Partial discharge signal denoising with spatially adaptive wavelet thresholding and support vector machines Partial discharge signal denoising with spatially adaptive wavelet thresholding and support vector machines](/preview/png/10401520.png)
چکیده انگلیسی
In this paper an improved method to denoise partial discharge (PD) signals is presented. The method is based on the wavelet transform (WT) and support vector machines (SVM) and is distinct from other WT-based denoising strategies in the sense that it exploits the high spatial correlations presented by PD wavelet decompositions as a way to identify and select the relevant coefficients. PD spatial correlations are characterized by WT modulus maxima propagation along decomposition levels (scales), which are a strong indicative of the their time-of-occurrence. Denoising is performed by identification and separation of PD-related maxima lines by an SVM pattern classifier. The results obtained confirm that this method has superior denoising capabilities when compared to other WT-based methods found in the literature for the processing of Gaussian and discrete spectral interferences. Moreover, its greatest advantages become clear when the interference has a pulsating or localized shape, situation in which traditional methods usually fail.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Electric Power Systems Research - Volume 81, Issue 2, February 2011, Pages 644-659
Journal: Electric Power Systems Research - Volume 81, Issue 2, February 2011, Pages 644-659
نویسندگان
Hilton de Oliveira Mota, Leonardo Chaves Dutra da Rocha, Thiago Cunha de Moura Salles, Flávio Henrique Vasconcelos,