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Application of Wavelet Transformation Symlet Type and Coiflet Type for Partial Discharge Signals Denoising
Zaeni A.a, Kasnalestari T.a, Khayam U.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 IEEE.Partial Discharge which contained in isolation can be detected by various tools and methods such as RC detector, HFCT, Loop Antenna and others. However, some Partial Discharge detection devices have limitations in providing detection results so that the result of partrial discharge signal is still mixed with the noise signal. Given this problem, it is necessary to find or develop a method to obtain the original signal from the partial discharge so that the process of partial discharge diagnosis can be continued to the next step. In general, the main technique for removing noise from partial discharge signals can be realized in time domain or in frequency domain through fourier transform method. But this method has a weakness that when the signal is processed in the frequency domain, the time domain information becomes lost so that the method needs to be developed in order to obtain the appropriate results. One effort that can be done is to develop the method into Wavelet Transformation. Recent years The wavelet transform (WT) has become an effective new method of signal processing. This method has been successfully applied in various fields. In this study, experiments will be conducted to denody the partial discharge signals using the Symlet and Coiflet Wavelet Transformations.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Discharge diagnosis,Fourier transform method,Frequency domains,noise,Partial discharge detection,Partial discharge signal,Tools and methods,Wavelet transformations[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]noise,Partial Discharge,Wavelet Transformation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEVT.2018.8628460[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]