Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network

In view of the fact that the wavelet packet transform (WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component analysis (PCA) to the inverter-side fault diagnosis of multi-terminal hybrid highvolta...

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Autores principales:	Tao Li, Yongli Li, Xiaolong Chen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Fault diagnosis hybrid high-voltage direct current (HVDC) wavelet packet transform (WPT) principal component analysis (PCA) Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830
Acceso en línea:	https://doaj.org/article/814a461c17e2486884ae53ed13e542e3
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spelling	oai:doaj.org-article:814a461c17e2486884ae53ed13e542e32021-11-30T00:00:31ZFault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network2196-542010.35833/MPCE.2021.000362https://doaj.org/article/814a461c17e2486884ae53ed13e542e32021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9627870/https://doaj.org/toc/2196-5420In view of the fact that the wavelet packet transform (WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component analysis (PCA) to the inverter-side fault diagnosis of multi-terminal hybrid highvoltage direct current (HVDC) network, which can significantly improve the speed and accuracy of fault diagnosis. Firstly, current amplitude and current slope are used to sample the data, and the WPT is used to extract the energy spectrum of the signal. Secondly, an energy matrix is constructed, and the PCA method is used to calculate whether the squared prediction error (SPE) statistics of various signals that can reflect the degree of deviation of the measured value from the principal component model at a certain time exceed the limit to judge the occurrence of the fault. Further, its maximum value is compared to determine the fault types. Finally, based on a large number of MATLAB/Simulink simulation results, it is shown that the PCA method using the current slope as the sampled data can detect the occurrence of a ground fault with small transition resistance within 2 ms, and identify the fault types within 10 ms, without being affected by the sampling frequency.Tao LiYongli LiXiaolong ChenIEEEarticleFault diagnosishybrid high-voltage direct current (HVDC)wavelet packet transform (WPT)principal component analysis (PCA)Production of electric energy or power. Powerplants. Central stationsTK1001-1841Renewable energy sourcesTJ807-830ENJournal of Modern Power Systems and Clean Energy, Vol 9, Iss 6, Pp 1312-1326 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Fault diagnosis hybrid high-voltage direct current (HVDC) wavelet packet transform (WPT) principal component analysis (PCA) Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830
spellingShingle	Fault diagnosis hybrid high-voltage direct current (HVDC) wavelet packet transform (WPT) principal component analysis (PCA) Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830 Tao Li Yongli Li Xiaolong Chen Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network
description	In view of the fact that the wavelet packet transform (WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component analysis (PCA) to the inverter-side fault diagnosis of multi-terminal hybrid highvoltage direct current (HVDC) network, which can significantly improve the speed and accuracy of fault diagnosis. Firstly, current amplitude and current slope are used to sample the data, and the WPT is used to extract the energy spectrum of the signal. Secondly, an energy matrix is constructed, and the PCA method is used to calculate whether the squared prediction error (SPE) statistics of various signals that can reflect the degree of deviation of the measured value from the principal component model at a certain time exceed the limit to judge the occurrence of the fault. Further, its maximum value is compared to determine the fault types. Finally, based on a large number of MATLAB/Simulink simulation results, it is shown that the PCA method using the current slope as the sampled data can detect the occurrence of a ground fault with small transition resistance within 2 ms, and identify the fault types within 10 ms, without being affected by the sampling frequency.
format	article
author	Tao Li Yongli Li Xiaolong Chen
author_facet	Tao Li Yongli Li Xiaolong Chen
author_sort	Tao Li
title	Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network
title_short	Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network
title_full	Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network
title_fullStr	Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network
title_full_unstemmed	Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network
title_sort	fault diagnosis with wavelet packet transform and principal component analysis for multi-terminal hybrid hvdc network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/814a461c17e2486884ae53ed13e542e3
work_keys_str_mv	AT taoli faultdiagnosiswithwaveletpackettransformandprincipalcomponentanalysisformultiterminalhybridhvdcnetwork AT yonglili faultdiagnosiswithwaveletpackettransformandprincipalcomponentanalysisformultiterminalhybridhvdcnetwork AT xiaolongchen faultdiagnosiswithwaveletpackettransformandprincipalcomponentanalysisformultiterminalhybridhvdcnetwork
_version_	1718406873199149056

Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network

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