VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets

Generating electricity from enormous energy contained in oceans is an important means to develop and utilize marine sustainable energy. An offshore marine current generator set (MCGS) is a system that runs in seas to produce electricity from tremendous energy in tidal streams. MCGSs operate in ocean...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gang Yao [verfasserIn] Shuxiu Pang [verfasserIn] Tingting Ying [verfasserIn] Mohamed Benbouzid [verfasserIn] Mourad Ait-Ahmed [verfasserIn] Mohamed Fouad Benkhoris [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	fault detection and diagnosis marine current generation five-phase permanent magnet synchronous generator third harmonic windings empirical modal decomposition Hilbert transform

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 13(2020), 22, p 6004
Übergeordnetes Werk:	volume:13 ; year:2020 ; number:22, p 6004

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en13226004

Katalog-ID:	DOAJ084635495

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allfieldsSound	10.3390/en13226004 doi (DE-627)DOAJ084635495 (DE-599)DOAJ9ee258a9636d490f8e5e76dc3a563bb9 DE-627 ger DE-627 rakwb eng Gang Yao verfasserin aut VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Generating electricity from enormous energy contained in oceans is an important means to develop and utilize marine sustainable energy. An offshore marine current generator set (MCGS) is a system that runs in seas to produce electricity from tremendous energy in tidal streams. MCGSs operate in oceanic environments with high humidity, saline-alkali water, and impacts of marine organisms and waves, and consequently malfunctions can happen along with the need for expensive inspection and maintenance. In order to achieve effective fault diagnosis of MCGSs in events of failure, this paper focuses on fault detection and diagnosis (FDD) of MCGSs based on five-phase permanent magnet synchronous generators (FP-PMSGs) with the third harmonic windings (THWs). Firstly, mathematical models were built for a hydraulic turbine and the FP-PMSG with THWs; then, a fault detection method based on empirical mode decomposition (EMD) and Hilbert transform (HT) was studied to detect different open-circuit faults (OCFs) of the generator; afterwards, a variable-parameter particle swarm optimization (VPSO) was designed to optimize the penalty and kernel function parameters of a support vector machine (SVM), which was named the VPSO-SVM method in this paper and used to perform fault diagnosis of the FP-PMSG. Finally, simulation blocks were built with MATLAB/Simulink to realize the mathematical models of the MCGS, and the proposed FDD method was coded with MATLAB. The effectiveness of the proposed VPSO-SVM method was validated by simulation results analysis and comparisons. fault detection and diagnosis marine current generation five-phase permanent magnet synchronous generator third harmonic windings empirical modal decomposition Hilbert transform Technology T Shuxiu Pang verfasserin aut Tingting Ying verfasserin aut Mohamed Benbouzid verfasserin aut Mourad Ait-Ahmed verfasserin aut Mohamed Fouad Benkhoris verfasserin aut In Energies MDPI AG, 2008 13(2020), 22, p 6004 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:13 year:2020 number:22, p 6004 https://doi.org/10.3390/en13226004 kostenfrei https://doaj.org/article/9ee258a9636d490f8e5e76dc3a563bb9 kostenfrei https://www.mdpi.com/1996-1073/13/22/6004 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 22, p 6004
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author	Gang Yao
spellingShingle	Gang Yao misc fault detection and diagnosis misc marine current generation misc five-phase permanent magnet synchronous generator misc third harmonic windings misc empirical modal decomposition misc Hilbert transform misc Technology misc T VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets
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topic_title	VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets fault detection and diagnosis marine current generation five-phase permanent magnet synchronous generator third harmonic windings empirical modal decomposition Hilbert transform
topic	misc fault detection and diagnosis misc marine current generation misc five-phase permanent magnet synchronous generator misc third harmonic windings misc empirical modal decomposition misc Hilbert transform misc Technology misc T
topic_unstemmed	misc fault detection and diagnosis misc marine current generation misc five-phase permanent magnet synchronous generator misc third harmonic windings misc empirical modal decomposition misc Hilbert transform misc Technology misc T
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title	VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets
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title_full	VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets
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title_sort	vpso-svm-based open-circuit faults diagnosis of five-phase marine current generator sets
title_auth	VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets
abstract	Generating electricity from enormous energy contained in oceans is an important means to develop and utilize marine sustainable energy. An offshore marine current generator set (MCGS) is a system that runs in seas to produce electricity from tremendous energy in tidal streams. MCGSs operate in oceanic environments with high humidity, saline-alkali water, and impacts of marine organisms and waves, and consequently malfunctions can happen along with the need for expensive inspection and maintenance. In order to achieve effective fault diagnosis of MCGSs in events of failure, this paper focuses on fault detection and diagnosis (FDD) of MCGSs based on five-phase permanent magnet synchronous generators (FP-PMSGs) with the third harmonic windings (THWs). Firstly, mathematical models were built for a hydraulic turbine and the FP-PMSG with THWs; then, a fault detection method based on empirical mode decomposition (EMD) and Hilbert transform (HT) was studied to detect different open-circuit faults (OCFs) of the generator; afterwards, a variable-parameter particle swarm optimization (VPSO) was designed to optimize the penalty and kernel function parameters of a support vector machine (SVM), which was named the VPSO-SVM method in this paper and used to perform fault diagnosis of the FP-PMSG. Finally, simulation blocks were built with MATLAB/Simulink to realize the mathematical models of the MCGS, and the proposed FDD method was coded with MATLAB. The effectiveness of the proposed VPSO-SVM method was validated by simulation results analysis and comparisons.
abstractGer	Generating electricity from enormous energy contained in oceans is an important means to develop and utilize marine sustainable energy. An offshore marine current generator set (MCGS) is a system that runs in seas to produce electricity from tremendous energy in tidal streams. MCGSs operate in oceanic environments with high humidity, saline-alkali water, and impacts of marine organisms and waves, and consequently malfunctions can happen along with the need for expensive inspection and maintenance. In order to achieve effective fault diagnosis of MCGSs in events of failure, this paper focuses on fault detection and diagnosis (FDD) of MCGSs based on five-phase permanent magnet synchronous generators (FP-PMSGs) with the third harmonic windings (THWs). Firstly, mathematical models were built for a hydraulic turbine and the FP-PMSG with THWs; then, a fault detection method based on empirical mode decomposition (EMD) and Hilbert transform (HT) was studied to detect different open-circuit faults (OCFs) of the generator; afterwards, a variable-parameter particle swarm optimization (VPSO) was designed to optimize the penalty and kernel function parameters of a support vector machine (SVM), which was named the VPSO-SVM method in this paper and used to perform fault diagnosis of the FP-PMSG. Finally, simulation blocks were built with MATLAB/Simulink to realize the mathematical models of the MCGS, and the proposed FDD method was coded with MATLAB. The effectiveness of the proposed VPSO-SVM method was validated by simulation results analysis and comparisons.
abstract_unstemmed	Generating electricity from enormous energy contained in oceans is an important means to develop and utilize marine sustainable energy. An offshore marine current generator set (MCGS) is a system that runs in seas to produce electricity from tremendous energy in tidal streams. MCGSs operate in oceanic environments with high humidity, saline-alkali water, and impacts of marine organisms and waves, and consequently malfunctions can happen along with the need for expensive inspection and maintenance. In order to achieve effective fault diagnosis of MCGSs in events of failure, this paper focuses on fault detection and diagnosis (FDD) of MCGSs based on five-phase permanent magnet synchronous generators (FP-PMSGs) with the third harmonic windings (THWs). Firstly, mathematical models were built for a hydraulic turbine and the FP-PMSG with THWs; then, a fault detection method based on empirical mode decomposition (EMD) and Hilbert transform (HT) was studied to detect different open-circuit faults (OCFs) of the generator; afterwards, a variable-parameter particle swarm optimization (VPSO) was designed to optimize the penalty and kernel function parameters of a support vector machine (SVM), which was named the VPSO-SVM method in this paper and used to perform fault diagnosis of the FP-PMSG. Finally, simulation blocks were built with MATLAB/Simulink to realize the mathematical models of the MCGS, and the proposed FDD method was coded with MATLAB. The effectiveness of the proposed VPSO-SVM method was validated by simulation results analysis and comparisons.
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title_short	VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets
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Firstly, mathematical models were built for a hydraulic turbine and the FP-PMSG with THWs; then, a fault detection method based on empirical mode decomposition (EMD) and Hilbert transform (HT) was studied to detect different open-circuit faults (OCFs) of the generator; afterwards, a variable-parameter particle swarm optimization (VPSO) was designed to optimize the penalty and kernel function parameters of a support vector machine (SVM), which was named the VPSO-SVM method in this paper and used to perform fault diagnosis of the FP-PMSG. Finally, simulation blocks were built with MATLAB/Simulink to realize the mathematical models of the MCGS, and the proposed FDD method was coded with MATLAB. 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VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets

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