Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter

Abstract The use of Multilevel Inverters (MLI) have significantly increased in many industrial drive applications during the last two decades. Moreover, with increased penetration of natural resource such as solar energy harnessed through photovoltaic cell array, MLI have gained popularity for conve...
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Autor*in:	Singh, Vikram [verfasserIn] Yadav, Anamika Gupta, Shubhrata

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	MLI Cascaded H-bridge Fault detection Fault classification DWT ANN

Anmerkung:	© The Institution of Engineers (India) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Journal of the Institution of Engineers (India) - Springer India, 2012, 105(2023), 2 vom: 22. Dez., Seite 217-228
Übergeordnetes Werk:	volume:105 ; year:2023 ; number:2 ; day:22 ; month:12 ; pages:217-228

Links:	Volltext

DOI / URN:	10.1007/s40031-023-00964-2

Katalog-ID:	SPR055296351

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520			\|a Abstract The use of Multilevel Inverters (MLI) have significantly increased in many industrial drive applications during the last two decades. Moreover, with increased penetration of natural resource such as solar energy harnessed through photovoltaic cell array, MLI have gained popularity for conversion of dc-to ac. But the failure of a power electronics switch typically affects multilevel inverters. Thus, fault diagnosis has become more necessary in order to mitigate MLI damage in a higher fault probability environment. Detection and classification of faults in power semiconductor switches must be incorporated alongside conventional protection systems so as to preserve the reliability of the complete system. This paper discusses the identification of open-switch failures in PV-fed multilevel inverters using a wavelet-neuro hybrid diagnostic system. In order to analyze output voltage and current waveforms under different fault scenarios, simulation studies were conducted on cascaded H-bridge MLI with five levels of output voltage. Discrete wavelet transform (DWT) has been implemented as the signal decomposition technique to mine the salient features of the MLI output voltage and current signals. For fault classification, these features are used as inputs to an artificial neural network (ANN) which is trained offline. Once combined DWT–ANN created fault identifier and localizer is trained sufficiently, it is charity to pinpoint the faulty switch(es) in the MLI.
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allfields	10.1007/s40031-023-00964-2 doi (DE-627)SPR055296351 (SPR)s40031-023-00964-2-e DE-627 ger DE-627 rakwb eng 620 690 VZ Singh, Vikram verfasserin (orcid)0000-0002-1781-3919 aut Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of Multilevel Inverters (MLI) have significantly increased in many industrial drive applications during the last two decades. Moreover, with increased penetration of natural resource such as solar energy harnessed through photovoltaic cell array, MLI have gained popularity for conversion of dc-to ac. But the failure of a power electronics switch typically affects multilevel inverters. Thus, fault diagnosis has become more necessary in order to mitigate MLI damage in a higher fault probability environment. Detection and classification of faults in power semiconductor switches must be incorporated alongside conventional protection systems so as to preserve the reliability of the complete system. This paper discusses the identification of open-switch failures in PV-fed multilevel inverters using a wavelet-neuro hybrid diagnostic system. In order to analyze output voltage and current waveforms under different fault scenarios, simulation studies were conducted on cascaded H-bridge MLI with five levels of output voltage. Discrete wavelet transform (DWT) has been implemented as the signal decomposition technique to mine the salient features of the MLI output voltage and current signals. For fault classification, these features are used as inputs to an artificial neural network (ANN) which is trained offline. Once combined DWT–ANN created fault identifier and localizer is trained sufficiently, it is charity to pinpoint the faulty switch(es) in the MLI. MLI (dpeaa)DE-He213 Cascaded H-bridge (dpeaa)DE-He213 Fault detection (dpeaa)DE-He213 Fault classification (dpeaa)DE-He213 DWT (dpeaa)DE-He213 ANN (dpeaa)DE-He213 Yadav, Anamika (orcid)0000-0001-7396-8926 aut Gupta, Shubhrata (orcid)0000-0002-6962-4036 aut Enthalten in Journal of the Institution of Engineers (India) Springer India, 2012 105(2023), 2 vom: 22. Dez., Seite 217-228 (DE-627)722236980 (DE-600)2677588-8 2250-2114 nnns volume:105 year:2023 number:2 day:22 month:12 pages:217-228 https://dx.doi.org/10.1007/s40031-023-00964-2 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 105 2023 2 22 12 217-228
spelling	10.1007/s40031-023-00964-2 doi (DE-627)SPR055296351 (SPR)s40031-023-00964-2-e DE-627 ger DE-627 rakwb eng 620 690 VZ Singh, Vikram verfasserin (orcid)0000-0002-1781-3919 aut Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of Multilevel Inverters (MLI) have significantly increased in many industrial drive applications during the last two decades. Moreover, with increased penetration of natural resource such as solar energy harnessed through photovoltaic cell array, MLI have gained popularity for conversion of dc-to ac. But the failure of a power electronics switch typically affects multilevel inverters. Thus, fault diagnosis has become more necessary in order to mitigate MLI damage in a higher fault probability environment. Detection and classification of faults in power semiconductor switches must be incorporated alongside conventional protection systems so as to preserve the reliability of the complete system. This paper discusses the identification of open-switch failures in PV-fed multilevel inverters using a wavelet-neuro hybrid diagnostic system. In order to analyze output voltage and current waveforms under different fault scenarios, simulation studies were conducted on cascaded H-bridge MLI with five levels of output voltage. Discrete wavelet transform (DWT) has been implemented as the signal decomposition technique to mine the salient features of the MLI output voltage and current signals. For fault classification, these features are used as inputs to an artificial neural network (ANN) which is trained offline. Once combined DWT–ANN created fault identifier and localizer is trained sufficiently, it is charity to pinpoint the faulty switch(es) in the MLI. MLI (dpeaa)DE-He213 Cascaded H-bridge (dpeaa)DE-He213 Fault detection (dpeaa)DE-He213 Fault classification (dpeaa)DE-He213 DWT (dpeaa)DE-He213 ANN (dpeaa)DE-He213 Yadav, Anamika (orcid)0000-0001-7396-8926 aut Gupta, Shubhrata (orcid)0000-0002-6962-4036 aut Enthalten in Journal of the Institution of Engineers (India) Springer India, 2012 105(2023), 2 vom: 22. Dez., Seite 217-228 (DE-627)722236980 (DE-600)2677588-8 2250-2114 nnns volume:105 year:2023 number:2 day:22 month:12 pages:217-228 https://dx.doi.org/10.1007/s40031-023-00964-2 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 105 2023 2 22 12 217-228
allfields_unstemmed	10.1007/s40031-023-00964-2 doi (DE-627)SPR055296351 (SPR)s40031-023-00964-2-e DE-627 ger DE-627 rakwb eng 620 690 VZ Singh, Vikram verfasserin (orcid)0000-0002-1781-3919 aut Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of Multilevel Inverters (MLI) have significantly increased in many industrial drive applications during the last two decades. Moreover, with increased penetration of natural resource such as solar energy harnessed through photovoltaic cell array, MLI have gained popularity for conversion of dc-to ac. But the failure of a power electronics switch typically affects multilevel inverters. Thus, fault diagnosis has become more necessary in order to mitigate MLI damage in a higher fault probability environment. Detection and classification of faults in power semiconductor switches must be incorporated alongside conventional protection systems so as to preserve the reliability of the complete system. This paper discusses the identification of open-switch failures in PV-fed multilevel inverters using a wavelet-neuro hybrid diagnostic system. In order to analyze output voltage and current waveforms under different fault scenarios, simulation studies were conducted on cascaded H-bridge MLI with five levels of output voltage. Discrete wavelet transform (DWT) has been implemented as the signal decomposition technique to mine the salient features of the MLI output voltage and current signals. For fault classification, these features are used as inputs to an artificial neural network (ANN) which is trained offline. Once combined DWT–ANN created fault identifier and localizer is trained sufficiently, it is charity to pinpoint the faulty switch(es) in the MLI. MLI (dpeaa)DE-He213 Cascaded H-bridge (dpeaa)DE-He213 Fault detection (dpeaa)DE-He213 Fault classification (dpeaa)DE-He213 DWT (dpeaa)DE-He213 ANN (dpeaa)DE-He213 Yadav, Anamika (orcid)0000-0001-7396-8926 aut Gupta, Shubhrata (orcid)0000-0002-6962-4036 aut Enthalten in Journal of the Institution of Engineers (India) Springer India, 2012 105(2023), 2 vom: 22. Dez., Seite 217-228 (DE-627)722236980 (DE-600)2677588-8 2250-2114 nnns volume:105 year:2023 number:2 day:22 month:12 pages:217-228 https://dx.doi.org/10.1007/s40031-023-00964-2 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 105 2023 2 22 12 217-228
allfieldsGer	10.1007/s40031-023-00964-2 doi (DE-627)SPR055296351 (SPR)s40031-023-00964-2-e DE-627 ger DE-627 rakwb eng 620 690 VZ Singh, Vikram verfasserin (orcid)0000-0002-1781-3919 aut Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of Multilevel Inverters (MLI) have significantly increased in many industrial drive applications during the last two decades. Moreover, with increased penetration of natural resource such as solar energy harnessed through photovoltaic cell array, MLI have gained popularity for conversion of dc-to ac. But the failure of a power electronics switch typically affects multilevel inverters. Thus, fault diagnosis has become more necessary in order to mitigate MLI damage in a higher fault probability environment. Detection and classification of faults in power semiconductor switches must be incorporated alongside conventional protection systems so as to preserve the reliability of the complete system. This paper discusses the identification of open-switch failures in PV-fed multilevel inverters using a wavelet-neuro hybrid diagnostic system. In order to analyze output voltage and current waveforms under different fault scenarios, simulation studies were conducted on cascaded H-bridge MLI with five levels of output voltage. Discrete wavelet transform (DWT) has been implemented as the signal decomposition technique to mine the salient features of the MLI output voltage and current signals. For fault classification, these features are used as inputs to an artificial neural network (ANN) which is trained offline. Once combined DWT–ANN created fault identifier and localizer is trained sufficiently, it is charity to pinpoint the faulty switch(es) in the MLI. MLI (dpeaa)DE-He213 Cascaded H-bridge (dpeaa)DE-He213 Fault detection (dpeaa)DE-He213 Fault classification (dpeaa)DE-He213 DWT (dpeaa)DE-He213 ANN (dpeaa)DE-He213 Yadav, Anamika (orcid)0000-0001-7396-8926 aut Gupta, Shubhrata (orcid)0000-0002-6962-4036 aut Enthalten in Journal of the Institution of Engineers (India) Springer India, 2012 105(2023), 2 vom: 22. Dez., Seite 217-228 (DE-627)722236980 (DE-600)2677588-8 2250-2114 nnns volume:105 year:2023 number:2 day:22 month:12 pages:217-228 https://dx.doi.org/10.1007/s40031-023-00964-2 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 105 2023 2 22 12 217-228
allfieldsSound	10.1007/s40031-023-00964-2 doi (DE-627)SPR055296351 (SPR)s40031-023-00964-2-e DE-627 ger DE-627 rakwb eng 620 690 VZ Singh, Vikram verfasserin (orcid)0000-0002-1781-3919 aut Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of Multilevel Inverters (MLI) have significantly increased in many industrial drive applications during the last two decades. Moreover, with increased penetration of natural resource such as solar energy harnessed through photovoltaic cell array, MLI have gained popularity for conversion of dc-to ac. But the failure of a power electronics switch typically affects multilevel inverters. Thus, fault diagnosis has become more necessary in order to mitigate MLI damage in a higher fault probability environment. Detection and classification of faults in power semiconductor switches must be incorporated alongside conventional protection systems so as to preserve the reliability of the complete system. This paper discusses the identification of open-switch failures in PV-fed multilevel inverters using a wavelet-neuro hybrid diagnostic system. In order to analyze output voltage and current waveforms under different fault scenarios, simulation studies were conducted on cascaded H-bridge MLI with five levels of output voltage. Discrete wavelet transform (DWT) has been implemented as the signal decomposition technique to mine the salient features of the MLI output voltage and current signals. For fault classification, these features are used as inputs to an artificial neural network (ANN) which is trained offline. Once combined DWT–ANN created fault identifier and localizer is trained sufficiently, it is charity to pinpoint the faulty switch(es) in the MLI. MLI (dpeaa)DE-He213 Cascaded H-bridge (dpeaa)DE-He213 Fault detection (dpeaa)DE-He213 Fault classification (dpeaa)DE-He213 DWT (dpeaa)DE-He213 ANN (dpeaa)DE-He213 Yadav, Anamika (orcid)0000-0001-7396-8926 aut Gupta, Shubhrata (orcid)0000-0002-6962-4036 aut Enthalten in Journal of the Institution of Engineers (India) Springer India, 2012 105(2023), 2 vom: 22. Dez., Seite 217-228 (DE-627)722236980 (DE-600)2677588-8 2250-2114 nnns volume:105 year:2023 number:2 day:22 month:12 pages:217-228 https://dx.doi.org/10.1007/s40031-023-00964-2 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 105 2023 2 22 12 217-228
language	English
source	Enthalten in Journal of the Institution of Engineers (India) 105(2023), 2 vom: 22. Dez., Seite 217-228 volume:105 year:2023 number:2 day:22 month:12 pages:217-228
sourceStr	Enthalten in Journal of the Institution of Engineers (India) 105(2023), 2 vom: 22. Dez., Seite 217-228 volume:105 year:2023 number:2 day:22 month:12 pages:217-228
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	MLI Cascaded H-bridge Fault detection Fault classification DWT ANN
dewey-raw	620
isfreeaccess_bool	false
container_title	Journal of the Institution of Engineers (India)
authorswithroles_txt_mv	Singh, Vikram @@aut@@ Yadav, Anamika @@aut@@ Gupta, Shubhrata @@aut@@
publishDateDaySort_date	2023-12-22T00:00:00Z
hierarchy_top_id	722236980
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author	Singh, Vikram
spellingShingle	Singh, Vikram ddc 620 misc MLI misc Cascaded H-bridge misc Fault detection misc Fault classification misc DWT misc ANN Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter
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topic_title	620 690 VZ Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter MLI (dpeaa)DE-He213 Cascaded H-bridge (dpeaa)DE-He213 Fault detection (dpeaa)DE-He213 Fault classification (dpeaa)DE-He213 DWT (dpeaa)DE-He213 ANN (dpeaa)DE-He213
topic	ddc 620 misc MLI misc Cascaded H-bridge misc Fault detection misc Fault classification misc DWT misc ANN
topic_unstemmed	ddc 620 misc MLI misc Cascaded H-bridge misc Fault detection misc Fault classification misc DWT misc ANN
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title	Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter
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title_full	Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter
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author_browse	Singh, Vikram Yadav, Anamika Gupta, Shubhrata
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title_sort	combined wavelet and ann-based open-switch fault detection and classification in pv-fed multilevel inverter
title_auth	Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter
abstract	Abstract The use of Multilevel Inverters (MLI) have significantly increased in many industrial drive applications during the last two decades. Moreover, with increased penetration of natural resource such as solar energy harnessed through photovoltaic cell array, MLI have gained popularity for conversion of dc-to ac. But the failure of a power electronics switch typically affects multilevel inverters. Thus, fault diagnosis has become more necessary in order to mitigate MLI damage in a higher fault probability environment. Detection and classification of faults in power semiconductor switches must be incorporated alongside conventional protection systems so as to preserve the reliability of the complete system. This paper discusses the identification of open-switch failures in PV-fed multilevel inverters using a wavelet-neuro hybrid diagnostic system. In order to analyze output voltage and current waveforms under different fault scenarios, simulation studies were conducted on cascaded H-bridge MLI with five levels of output voltage. Discrete wavelet transform (DWT) has been implemented as the signal decomposition technique to mine the salient features of the MLI output voltage and current signals. For fault classification, these features are used as inputs to an artificial neural network (ANN) which is trained offline. Once combined DWT–ANN created fault identifier and localizer is trained sufficiently, it is charity to pinpoint the faulty switch(es) in the MLI. © The Institution of Engineers (India) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract The use of Multilevel Inverters (MLI) have significantly increased in many industrial drive applications during the last two decades. Moreover, with increased penetration of natural resource such as solar energy harnessed through photovoltaic cell array, MLI have gained popularity for conversion of dc-to ac. But the failure of a power electronics switch typically affects multilevel inverters. Thus, fault diagnosis has become more necessary in order to mitigate MLI damage in a higher fault probability environment. Detection and classification of faults in power semiconductor switches must be incorporated alongside conventional protection systems so as to preserve the reliability of the complete system. This paper discusses the identification of open-switch failures in PV-fed multilevel inverters using a wavelet-neuro hybrid diagnostic system. In order to analyze output voltage and current waveforms under different fault scenarios, simulation studies were conducted on cascaded H-bridge MLI with five levels of output voltage. Discrete wavelet transform (DWT) has been implemented as the signal decomposition technique to mine the salient features of the MLI output voltage and current signals. For fault classification, these features are used as inputs to an artificial neural network (ANN) which is trained offline. Once combined DWT–ANN created fault identifier and localizer is trained sufficiently, it is charity to pinpoint the faulty switch(es) in the MLI. © The Institution of Engineers (India) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract The use of Multilevel Inverters (MLI) have significantly increased in many industrial drive applications during the last two decades. Moreover, with increased penetration of natural resource such as solar energy harnessed through photovoltaic cell array, MLI have gained popularity for conversion of dc-to ac. But the failure of a power electronics switch typically affects multilevel inverters. Thus, fault diagnosis has become more necessary in order to mitigate MLI damage in a higher fault probability environment. Detection and classification of faults in power semiconductor switches must be incorporated alongside conventional protection systems so as to preserve the reliability of the complete system. This paper discusses the identification of open-switch failures in PV-fed multilevel inverters using a wavelet-neuro hybrid diagnostic system. In order to analyze output voltage and current waveforms under different fault scenarios, simulation studies were conducted on cascaded H-bridge MLI with five levels of output voltage. Discrete wavelet transform (DWT) has been implemented as the signal decomposition technique to mine the salient features of the MLI output voltage and current signals. For fault classification, these features are used as inputs to an artificial neural network (ANN) which is trained offline. Once combined DWT–ANN created fault identifier and localizer is trained sufficiently, it is charity to pinpoint the faulty switch(es) in the MLI. © The Institution of Engineers (India) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter
url	https://dx.doi.org/10.1007/s40031-023-00964-2
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author2	Yadav, Anamika Gupta, Shubhrata
author2Str	Yadav, Anamika Gupta, Shubhrata
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doi_str	10.1007/s40031-023-00964-2
up_date	2024-07-03T14:41:25.249Z
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The use of Multilevel Inverters (MLI) have significantly increased in many industrial drive applications during the last two decades. Moreover, with increased penetration of natural resource such as solar energy harnessed through photovoltaic cell array, MLI have gained popularity for conversion of dc-to ac. But the failure of a power electronics switch typically affects multilevel inverters. Thus, fault diagnosis has become more necessary in order to mitigate MLI damage in a higher fault probability environment. 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