Enhancement of two-step state estimation performance in unbalanced distribution networks

The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based...
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Gespeichert in:

Autor*in:	Amiri, Karim [verfasserIn] Kazemzadeh, Rasool [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	State estimation Two-step state estimation Measurement locating process Network reduction process Unbalanced three-phase networks Weighted least square error

Übergeordnetes Werk:	Enthalten in: Computers & electrical engineering - Amsterdam [u.a.] : Elsevier Science, 1973, 86
Übergeordnetes Werk:	volume:86

DOI / URN:	10.1016/j.compeleceng.2020.106724

Katalog-ID:	ELV004801105

Internformat


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245	1	0	\|a Enhancement of two-step state estimation performance in unbalanced distribution networks
264		1	\|c 2020
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520			\|a The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based on the results obtained from SE. ADN is intrinsically unbalanced. A two-step state estimation is applied to an unbalanced DN using a network reduction process (NRP) and a meter locating process (MLP) in this paper. Considering the shortage of measurements in DN, obtaining accurate primary information from network conditions enhances SE. Primary SE runs on the reduced network and its outputs are used as measurements to enhance total estimation (secondary SE). This method resolves the shortage of accurate measurements and elevates SE accuracy. The considered method is executed in a 37-bus unbalanced DN, and the results of the simulation are used to validate the proposed method.
650		4	\|a State estimation
650		4	\|a Two-step state estimation
650		4	\|a Measurement locating process
650		4	\|a Network reduction process
650		4	\|a Unbalanced three-phase networks
650		4	\|a Weighted least square error
700	1		\|a Kazemzadeh, Rasool \|e verfasserin \|0 (orcid)0000-0001-9511-4454 \|4 aut
773	0	8	\|i Enthalten in \|t Computers & electrical engineering \|d Amsterdam [u.a.] : Elsevier Science, 1973 \|g 86 \|h Online-Ressource \|w (DE-627)306715872 \|w (DE-600)1501325-X \|w (DE-576)094531293 \|x 1879-0755 \|7 nnns
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912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
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912			\|a GBV_ILN_2006
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912			\|a GBV_ILN_2048
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912			\|a GBV_ILN_2059
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912			\|a GBV_ILN_2068
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912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
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912			\|a GBV_ILN_2147
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912			\|a GBV_ILN_2152
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912			\|a GBV_ILN_2336
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936	b	k	\|a 53.00 \|j Elektrotechnik: Allgemeines
936	b	k	\|a 35.06 \|j Computeranwendungen \|x Chemie
936	b	k	\|a 54.00 \|j Informatik: Allgemeines
951			\|a AR
952			\|d 86

Indexfelder

author_variant	k a ka r k rk
matchkey_str	article:18790755:2020----::nacmnototpttetmtopromnennaac
hierarchy_sort_str	2020
bklnumber	53.00 35.06 54.00
publishDate	2020
allfields	10.1016/j.compeleceng.2020.106724 doi (DE-627)ELV004801105 (ELSEVIER)S0045-7906(20)30579-6 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Amiri, Karim verfasserin aut Enhancement of two-step state estimation performance in unbalanced distribution networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based on the results obtained from SE. ADN is intrinsically unbalanced. A two-step state estimation is applied to an unbalanced DN using a network reduction process (NRP) and a meter locating process (MLP) in this paper. Considering the shortage of measurements in DN, obtaining accurate primary information from network conditions enhances SE. Primary SE runs on the reduced network and its outputs are used as measurements to enhance total estimation (secondary SE). This method resolves the shortage of accurate measurements and elevates SE accuracy. The considered method is executed in a 37-bus unbalanced DN, and the results of the simulation are used to validate the proposed method. State estimation Two-step state estimation Measurement locating process Network reduction process Unbalanced three-phase networks Weighted least square error Kazemzadeh, Rasool verfasserin (orcid)0000-0001-9511-4454 aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 86 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:86 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 86
spelling	10.1016/j.compeleceng.2020.106724 doi (DE-627)ELV004801105 (ELSEVIER)S0045-7906(20)30579-6 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Amiri, Karim verfasserin aut Enhancement of two-step state estimation performance in unbalanced distribution networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based on the results obtained from SE. ADN is intrinsically unbalanced. A two-step state estimation is applied to an unbalanced DN using a network reduction process (NRP) and a meter locating process (MLP) in this paper. Considering the shortage of measurements in DN, obtaining accurate primary information from network conditions enhances SE. Primary SE runs on the reduced network and its outputs are used as measurements to enhance total estimation (secondary SE). This method resolves the shortage of accurate measurements and elevates SE accuracy. The considered method is executed in a 37-bus unbalanced DN, and the results of the simulation are used to validate the proposed method. State estimation Two-step state estimation Measurement locating process Network reduction process Unbalanced three-phase networks Weighted least square error Kazemzadeh, Rasool verfasserin (orcid)0000-0001-9511-4454 aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 86 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:86 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 86
allfields_unstemmed	10.1016/j.compeleceng.2020.106724 doi (DE-627)ELV004801105 (ELSEVIER)S0045-7906(20)30579-6 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Amiri, Karim verfasserin aut Enhancement of two-step state estimation performance in unbalanced distribution networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based on the results obtained from SE. ADN is intrinsically unbalanced. A two-step state estimation is applied to an unbalanced DN using a network reduction process (NRP) and a meter locating process (MLP) in this paper. Considering the shortage of measurements in DN, obtaining accurate primary information from network conditions enhances SE. Primary SE runs on the reduced network and its outputs are used as measurements to enhance total estimation (secondary SE). This method resolves the shortage of accurate measurements and elevates SE accuracy. The considered method is executed in a 37-bus unbalanced DN, and the results of the simulation are used to validate the proposed method. State estimation Two-step state estimation Measurement locating process Network reduction process Unbalanced three-phase networks Weighted least square error Kazemzadeh, Rasool verfasserin (orcid)0000-0001-9511-4454 aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 86 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:86 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 86
allfieldsGer	10.1016/j.compeleceng.2020.106724 doi (DE-627)ELV004801105 (ELSEVIER)S0045-7906(20)30579-6 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Amiri, Karim verfasserin aut Enhancement of two-step state estimation performance in unbalanced distribution networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based on the results obtained from SE. ADN is intrinsically unbalanced. A two-step state estimation is applied to an unbalanced DN using a network reduction process (NRP) and a meter locating process (MLP) in this paper. Considering the shortage of measurements in DN, obtaining accurate primary information from network conditions enhances SE. Primary SE runs on the reduced network and its outputs are used as measurements to enhance total estimation (secondary SE). This method resolves the shortage of accurate measurements and elevates SE accuracy. The considered method is executed in a 37-bus unbalanced DN, and the results of the simulation are used to validate the proposed method. State estimation Two-step state estimation Measurement locating process Network reduction process Unbalanced three-phase networks Weighted least square error Kazemzadeh, Rasool verfasserin (orcid)0000-0001-9511-4454 aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 86 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:86 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 86
allfieldsSound	10.1016/j.compeleceng.2020.106724 doi (DE-627)ELV004801105 (ELSEVIER)S0045-7906(20)30579-6 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Amiri, Karim verfasserin aut Enhancement of two-step state estimation performance in unbalanced distribution networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based on the results obtained from SE. ADN is intrinsically unbalanced. A two-step state estimation is applied to an unbalanced DN using a network reduction process (NRP) and a meter locating process (MLP) in this paper. Considering the shortage of measurements in DN, obtaining accurate primary information from network conditions enhances SE. Primary SE runs on the reduced network and its outputs are used as measurements to enhance total estimation (secondary SE). This method resolves the shortage of accurate measurements and elevates SE accuracy. The considered method is executed in a 37-bus unbalanced DN, and the results of the simulation are used to validate the proposed method. State estimation Two-step state estimation Measurement locating process Network reduction process Unbalanced three-phase networks Weighted least square error Kazemzadeh, Rasool verfasserin (orcid)0000-0001-9511-4454 aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 86 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:86 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 86
language	English
source	Enthalten in Computers & electrical engineering 86 volume:86
sourceStr	Enthalten in Computers & electrical engineering 86 volume:86
format_phy_str_mv	Article
bklname	Elektrotechnik: Allgemeines Computeranwendungen Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	State estimation Two-step state estimation Measurement locating process Network reduction process Unbalanced three-phase networks Weighted least square error
dewey-raw	620
isfreeaccess_bool	false
container_title	Computers & electrical engineering
authorswithroles_txt_mv	Amiri, Karim @@aut@@ Kazemzadeh, Rasool @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	306715872
dewey-sort	3620
id	ELV004801105
language_de	englisch
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author	Amiri, Karim
spellingShingle	Amiri, Karim ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc State estimation misc Two-step state estimation misc Measurement locating process misc Network reduction process misc Unbalanced three-phase networks misc Weighted least square error Enhancement of two-step state estimation performance in unbalanced distribution networks
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topic_title	620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Enhancement of two-step state estimation performance in unbalanced distribution networks State estimation Two-step state estimation Measurement locating process Network reduction process Unbalanced three-phase networks Weighted least square error
topic	ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc State estimation misc Two-step state estimation misc Measurement locating process misc Network reduction process misc Unbalanced three-phase networks misc Weighted least square error
topic_unstemmed	ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc State estimation misc Two-step state estimation misc Measurement locating process misc Network reduction process misc Unbalanced three-phase networks misc Weighted least square error
topic_browse	ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc State estimation misc Two-step state estimation misc Measurement locating process misc Network reduction process misc Unbalanced three-phase networks misc Weighted least square error
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title	Enhancement of two-step state estimation performance in unbalanced distribution networks
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title_full	Enhancement of two-step state estimation performance in unbalanced distribution networks
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title_sort	enhancement of two-step state estimation performance in unbalanced distribution networks
title_auth	Enhancement of two-step state estimation performance in unbalanced distribution networks
abstract	The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based on the results obtained from SE. ADN is intrinsically unbalanced. A two-step state estimation is applied to an unbalanced DN using a network reduction process (NRP) and a meter locating process (MLP) in this paper. Considering the shortage of measurements in DN, obtaining accurate primary information from network conditions enhances SE. Primary SE runs on the reduced network and its outputs are used as measurements to enhance total estimation (secondary SE). This method resolves the shortage of accurate measurements and elevates SE accuracy. The considered method is executed in a 37-bus unbalanced DN, and the results of the simulation are used to validate the proposed method.
abstractGer	The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based on the results obtained from SE. ADN is intrinsically unbalanced. A two-step state estimation is applied to an unbalanced DN using a network reduction process (NRP) and a meter locating process (MLP) in this paper. Considering the shortage of measurements in DN, obtaining accurate primary information from network conditions enhances SE. Primary SE runs on the reduced network and its outputs are used as measurements to enhance total estimation (secondary SE). This method resolves the shortage of accurate measurements and elevates SE accuracy. The considered method is executed in a 37-bus unbalanced DN, and the results of the simulation are used to validate the proposed method.
abstract_unstemmed	The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based on the results obtained from SE. ADN is intrinsically unbalanced. A two-step state estimation is applied to an unbalanced DN using a network reduction process (NRP) and a meter locating process (MLP) in this paper. Considering the shortage of measurements in DN, obtaining accurate primary information from network conditions enhances SE. Primary SE runs on the reduced network and its outputs are used as measurements to enhance total estimation (secondary SE). This method resolves the shortage of accurate measurements and elevates SE accuracy. The considered method is executed in a 37-bus unbalanced DN, and the results of the simulation are used to validate the proposed method.
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title_short	Enhancement of two-step state estimation performance in unbalanced distribution networks
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doi_str	10.1016/j.compeleceng.2020.106724
up_date	2024-07-07T00:11:36.458Z
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Enhancement of two-step state estimation performance in unbalanced distribution networks

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