Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach

The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence...
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Gespeichert in:

Autor*in:	Nsaif, Younis M. [verfasserIn] Hossain Lipu, M.S. [verfasserIn] Hussain, Aini [verfasserIn] Ayob, Afida [verfasserIn] Yusof, Yushaizad [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Island detection Distributed generation Fault detection Variational mode decomposition Ensemble bagged trees method

Übergeordnetes Werk:	Enthalten in: International journal of electrical power & energy systems - Amsterdam [u.a.] : Elsevier Science, 1979, 154
Übergeordnetes Werk:	volume:154

DOI / URN:	10.1016/j.ijepes.2023.109468

Katalog-ID:	ELV064066754

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100	1		\|a Nsaif, Younis M. \|e verfasserin \|0 (orcid)0000-0003-2402-1650 \|4 aut
245	1	0	\|a Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach
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520			\|a The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence of islanding must be detected, and then distributed generators must be disconnected from the main network. In this article, a fast and accurate island detection method is proposed for photovoltaic distributed generations with a near-zero non-detection zone. A new island detection approach is developed by combining signal processing and machine learning techniques. Variational mode decomposition is used as a signal processing technique. Whereas the ensemble bagged-trees method is used as a machine learning technique. Variational mode decomposition is used to process positive- and negative-sequence component voltage signals along with power signal measurements acquired from the point of common coupling in order to identify intrinsic-mode functions. Next, the ensemble bagged-trees method is utilized to detect islanding during active and reactive power mismatch events and inconvenient quality factors. The results demonstrate that the suggested technique is able to discriminate between islanding and non-islanding events such as capacitive switching, fault emulation, and distribution generation cut-off. Besides, it has a minimum non-detection zone of less than 4% and a 4.8 ms detection time. Therefore, it is a reliable and reasonable solution for the distribution grid.
650		4	\|a Island detection
650		4	\|a Distributed generation
650		4	\|a Fault detection
650		4	\|a Variational mode decomposition
650		4	\|a Ensemble bagged trees method
700	1		\|a Hossain Lipu, M.S. \|e verfasserin \|4 aut
700	1		\|a Hussain, Aini \|e verfasserin \|4 aut
700	1		\|a Ayob, Afida \|e verfasserin \|4 aut
700	1		\|a Yusof, Yushaizad \|e verfasserin \|0 (orcid)0000-0002-3823-2961 \|4 aut
773	0	8	\|i Enthalten in \|t International journal of electrical power & energy systems \|d Amsterdam [u.a.] : Elsevier Science, 1979 \|g 154 \|h Online-Ressource \|w (DE-627)320411907 \|w (DE-600)2001425-9 \|w (DE-576)259271101 \|x 0142-0615 \|7 nnns
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publishDate	2023
allfields	10.1016/j.ijepes.2023.109468 doi (DE-627)ELV064066754 (ELSEVIER)S0142-0615(23)00525-2 DE-627 ger DE-627 rda eng 620 VZ 53.30 bkl Nsaif, Younis M. verfasserin (orcid)0000-0003-2402-1650 aut Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence of islanding must be detected, and then distributed generators must be disconnected from the main network. In this article, a fast and accurate island detection method is proposed for photovoltaic distributed generations with a near-zero non-detection zone. A new island detection approach is developed by combining signal processing and machine learning techniques. Variational mode decomposition is used as a signal processing technique. Whereas the ensemble bagged-trees method is used as a machine learning technique. Variational mode decomposition is used to process positive- and negative-sequence component voltage signals along with power signal measurements acquired from the point of common coupling in order to identify intrinsic-mode functions. Next, the ensemble bagged-trees method is utilized to detect islanding during active and reactive power mismatch events and inconvenient quality factors. The results demonstrate that the suggested technique is able to discriminate between islanding and non-islanding events such as capacitive switching, fault emulation, and distribution generation cut-off. Besides, it has a minimum non-detection zone of less than 4% and a 4.8 ms detection time. Therefore, it is a reliable and reasonable solution for the distribution grid. Island detection Distributed generation Fault detection Variational mode decomposition Ensemble bagged trees method Hossain Lipu, M.S. verfasserin aut Hussain, Aini verfasserin aut Ayob, Afida verfasserin aut Yusof, Yushaizad verfasserin (orcid)0000-0002-3823-2961 aut Enthalten in International journal of electrical power & energy systems Amsterdam [u.a.] : Elsevier Science, 1979 154 Online-Ressource (DE-627)320411907 (DE-600)2001425-9 (DE-576)259271101 0142-0615 nnns volume:154 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.30 Elektrische Energietechnik: Allgemeines VZ AR 154
spelling	10.1016/j.ijepes.2023.109468 doi (DE-627)ELV064066754 (ELSEVIER)S0142-0615(23)00525-2 DE-627 ger DE-627 rda eng 620 VZ 53.30 bkl Nsaif, Younis M. verfasserin (orcid)0000-0003-2402-1650 aut Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence of islanding must be detected, and then distributed generators must be disconnected from the main network. In this article, a fast and accurate island detection method is proposed for photovoltaic distributed generations with a near-zero non-detection zone. A new island detection approach is developed by combining signal processing and machine learning techniques. Variational mode decomposition is used as a signal processing technique. Whereas the ensemble bagged-trees method is used as a machine learning technique. Variational mode decomposition is used to process positive- and negative-sequence component voltage signals along with power signal measurements acquired from the point of common coupling in order to identify intrinsic-mode functions. Next, the ensemble bagged-trees method is utilized to detect islanding during active and reactive power mismatch events and inconvenient quality factors. The results demonstrate that the suggested technique is able to discriminate between islanding and non-islanding events such as capacitive switching, fault emulation, and distribution generation cut-off. Besides, it has a minimum non-detection zone of less than 4% and a 4.8 ms detection time. Therefore, it is a reliable and reasonable solution for the distribution grid. Island detection Distributed generation Fault detection Variational mode decomposition Ensemble bagged trees method Hossain Lipu, M.S. verfasserin aut Hussain, Aini verfasserin aut Ayob, Afida verfasserin aut Yusof, Yushaizad verfasserin (orcid)0000-0002-3823-2961 aut Enthalten in International journal of electrical power & energy systems Amsterdam [u.a.] : Elsevier Science, 1979 154 Online-Ressource (DE-627)320411907 (DE-600)2001425-9 (DE-576)259271101 0142-0615 nnns volume:154 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.30 Elektrische Energietechnik: Allgemeines VZ AR 154
allfields_unstemmed	10.1016/j.ijepes.2023.109468 doi (DE-627)ELV064066754 (ELSEVIER)S0142-0615(23)00525-2 DE-627 ger DE-627 rda eng 620 VZ 53.30 bkl Nsaif, Younis M. verfasserin (orcid)0000-0003-2402-1650 aut Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence of islanding must be detected, and then distributed generators must be disconnected from the main network. In this article, a fast and accurate island detection method is proposed for photovoltaic distributed generations with a near-zero non-detection zone. A new island detection approach is developed by combining signal processing and machine learning techniques. Variational mode decomposition is used as a signal processing technique. Whereas the ensemble bagged-trees method is used as a machine learning technique. Variational mode decomposition is used to process positive- and negative-sequence component voltage signals along with power signal measurements acquired from the point of common coupling in order to identify intrinsic-mode functions. Next, the ensemble bagged-trees method is utilized to detect islanding during active and reactive power mismatch events and inconvenient quality factors. The results demonstrate that the suggested technique is able to discriminate between islanding and non-islanding events such as capacitive switching, fault emulation, and distribution generation cut-off. Besides, it has a minimum non-detection zone of less than 4% and a 4.8 ms detection time. Therefore, it is a reliable and reasonable solution for the distribution grid. Island detection Distributed generation Fault detection Variational mode decomposition Ensemble bagged trees method Hossain Lipu, M.S. verfasserin aut Hussain, Aini verfasserin aut Ayob, Afida verfasserin aut Yusof, Yushaizad verfasserin (orcid)0000-0002-3823-2961 aut Enthalten in International journal of electrical power & energy systems Amsterdam [u.a.] : Elsevier Science, 1979 154 Online-Ressource (DE-627)320411907 (DE-600)2001425-9 (DE-576)259271101 0142-0615 nnns volume:154 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.30 Elektrische Energietechnik: Allgemeines VZ AR 154
allfieldsGer	10.1016/j.ijepes.2023.109468 doi (DE-627)ELV064066754 (ELSEVIER)S0142-0615(23)00525-2 DE-627 ger DE-627 rda eng 620 VZ 53.30 bkl Nsaif, Younis M. verfasserin (orcid)0000-0003-2402-1650 aut Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence of islanding must be detected, and then distributed generators must be disconnected from the main network. In this article, a fast and accurate island detection method is proposed for photovoltaic distributed generations with a near-zero non-detection zone. A new island detection approach is developed by combining signal processing and machine learning techniques. Variational mode decomposition is used as a signal processing technique. Whereas the ensemble bagged-trees method is used as a machine learning technique. Variational mode decomposition is used to process positive- and negative-sequence component voltage signals along with power signal measurements acquired from the point of common coupling in order to identify intrinsic-mode functions. Next, the ensemble bagged-trees method is utilized to detect islanding during active and reactive power mismatch events and inconvenient quality factors. The results demonstrate that the suggested technique is able to discriminate between islanding and non-islanding events such as capacitive switching, fault emulation, and distribution generation cut-off. Besides, it has a minimum non-detection zone of less than 4% and a 4.8 ms detection time. Therefore, it is a reliable and reasonable solution for the distribution grid. Island detection Distributed generation Fault detection Variational mode decomposition Ensemble bagged trees method Hossain Lipu, M.S. verfasserin aut Hussain, Aini verfasserin aut Ayob, Afida verfasserin aut Yusof, Yushaizad verfasserin (orcid)0000-0002-3823-2961 aut Enthalten in International journal of electrical power & energy systems Amsterdam [u.a.] : Elsevier Science, 1979 154 Online-Ressource (DE-627)320411907 (DE-600)2001425-9 (DE-576)259271101 0142-0615 nnns volume:154 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.30 Elektrische Energietechnik: Allgemeines VZ AR 154
allfieldsSound	10.1016/j.ijepes.2023.109468 doi (DE-627)ELV064066754 (ELSEVIER)S0142-0615(23)00525-2 DE-627 ger DE-627 rda eng 620 VZ 53.30 bkl Nsaif, Younis M. verfasserin (orcid)0000-0003-2402-1650 aut Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence of islanding must be detected, and then distributed generators must be disconnected from the main network. In this article, a fast and accurate island detection method is proposed for photovoltaic distributed generations with a near-zero non-detection zone. A new island detection approach is developed by combining signal processing and machine learning techniques. Variational mode decomposition is used as a signal processing technique. Whereas the ensemble bagged-trees method is used as a machine learning technique. Variational mode decomposition is used to process positive- and negative-sequence component voltage signals along with power signal measurements acquired from the point of common coupling in order to identify intrinsic-mode functions. Next, the ensemble bagged-trees method is utilized to detect islanding during active and reactive power mismatch events and inconvenient quality factors. The results demonstrate that the suggested technique is able to discriminate between islanding and non-islanding events such as capacitive switching, fault emulation, and distribution generation cut-off. Besides, it has a minimum non-detection zone of less than 4% and a 4.8 ms detection time. Therefore, it is a reliable and reasonable solution for the distribution grid. Island detection Distributed generation Fault detection Variational mode decomposition Ensemble bagged trees method Hossain Lipu, M.S. verfasserin aut Hussain, Aini verfasserin aut Ayob, Afida verfasserin aut Yusof, Yushaizad verfasserin (orcid)0000-0002-3823-2961 aut Enthalten in International journal of electrical power & energy systems Amsterdam [u.a.] : Elsevier Science, 1979 154 Online-Ressource (DE-627)320411907 (DE-600)2001425-9 (DE-576)259271101 0142-0615 nnns volume:154 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.30 Elektrische Energietechnik: Allgemeines VZ AR 154
language	English
source	Enthalten in International journal of electrical power & energy systems 154 volume:154
sourceStr	Enthalten in International journal of electrical power & energy systems 154 volume:154
format_phy_str_mv	Article
bklname	Elektrische Energietechnik: Allgemeines
institution	findex.gbv.de
topic_facet	Island detection Distributed generation Fault detection Variational mode decomposition Ensemble bagged trees method
dewey-raw	620
isfreeaccess_bool	false
container_title	International journal of electrical power & energy systems
authorswithroles_txt_mv	Nsaif, Younis M. @@aut@@ Hossain Lipu, M.S. @@aut@@ Hussain, Aini @@aut@@ Ayob, Afida @@aut@@ Yusof, Yushaizad @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320411907
dewey-sort	3620
id	ELV064066754
language_de	englisch
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author	Nsaif, Younis M.
spellingShingle	Nsaif, Younis M. ddc 620 bkl 53.30 misc Island detection misc Distributed generation misc Fault detection misc Variational mode decomposition misc Ensemble bagged trees method Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach
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topic_title	620 VZ 53.30 bkl Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach Island detection Distributed generation Fault detection Variational mode decomposition Ensemble bagged trees method
topic	ddc 620 bkl 53.30 misc Island detection misc Distributed generation misc Fault detection misc Variational mode decomposition misc Ensemble bagged trees method
topic_unstemmed	ddc 620 bkl 53.30 misc Island detection misc Distributed generation misc Fault detection misc Variational mode decomposition misc Ensemble bagged trees method
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title	Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach
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title_full	Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach
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author_browse	Nsaif, Younis M. Hossain Lipu, M.S. Hussain, Aini Ayob, Afida Yusof, Yushaizad
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title_sort	island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach
title_auth	Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach
abstract	The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence of islanding must be detected, and then distributed generators must be disconnected from the main network. In this article, a fast and accurate island detection method is proposed for photovoltaic distributed generations with a near-zero non-detection zone. A new island detection approach is developed by combining signal processing and machine learning techniques. Variational mode decomposition is used as a signal processing technique. Whereas the ensemble bagged-trees method is used as a machine learning technique. Variational mode decomposition is used to process positive- and negative-sequence component voltage signals along with power signal measurements acquired from the point of common coupling in order to identify intrinsic-mode functions. Next, the ensemble bagged-trees method is utilized to detect islanding during active and reactive power mismatch events and inconvenient quality factors. The results demonstrate that the suggested technique is able to discriminate between islanding and non-islanding events such as capacitive switching, fault emulation, and distribution generation cut-off. Besides, it has a minimum non-detection zone of less than 4% and a 4.8 ms detection time. Therefore, it is a reliable and reasonable solution for the distribution grid.
abstractGer	The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence of islanding must be detected, and then distributed generators must be disconnected from the main network. In this article, a fast and accurate island detection method is proposed for photovoltaic distributed generations with a near-zero non-detection zone. A new island detection approach is developed by combining signal processing and machine learning techniques. Variational mode decomposition is used as a signal processing technique. Whereas the ensemble bagged-trees method is used as a machine learning technique. Variational mode decomposition is used to process positive- and negative-sequence component voltage signals along with power signal measurements acquired from the point of common coupling in order to identify intrinsic-mode functions. Next, the ensemble bagged-trees method is utilized to detect islanding during active and reactive power mismatch events and inconvenient quality factors. The results demonstrate that the suggested technique is able to discriminate between islanding and non-islanding events such as capacitive switching, fault emulation, and distribution generation cut-off. Besides, it has a minimum non-detection zone of less than 4% and a 4.8 ms detection time. Therefore, it is a reliable and reasonable solution for the distribution grid.
abstract_unstemmed	The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence of islanding must be detected, and then distributed generators must be disconnected from the main network. In this article, a fast and accurate island detection method is proposed for photovoltaic distributed generations with a near-zero non-detection zone. A new island detection approach is developed by combining signal processing and machine learning techniques. Variational mode decomposition is used as a signal processing technique. Whereas the ensemble bagged-trees method is used as a machine learning technique. Variational mode decomposition is used to process positive- and negative-sequence component voltage signals along with power signal measurements acquired from the point of common coupling in order to identify intrinsic-mode functions. Next, the ensemble bagged-trees method is utilized to detect islanding during active and reactive power mismatch events and inconvenient quality factors. The results demonstrate that the suggested technique is able to discriminate between islanding and non-islanding events such as capacitive switching, fault emulation, and distribution generation cut-off. Besides, it has a minimum non-detection zone of less than 4% and a 4.8 ms detection time. Therefore, it is a reliable and reasonable solution for the distribution grid.
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title_short	Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach
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author2	Hossain Lipu, M.S. Hussain, Aini Ayob, Afida Yusof, Yushaizad
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up_date	2024-07-06T20:10:53.420Z
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Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach

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