Conundrum of fault detection in active hybrid AC–DC distribution networks

Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt d...
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Autor*in:	Shahram Negari [verfasserIn] David Xu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	power distribution faults belief networks distributed power generation distribution networks bayes methods probability inference mechanisms multi-agent systems state estimation fault diagnosis power distribution control distributed energy resources noisy data corrupt data automated mapping equipment connectivity bayesian belief network bayesian inference methodology correlational data state variables distributed state estimation self-aware agents instil uncertainty active hybrid ac–dc distribution networks fault detection ieee 13-bus network active hybrid distribution networks

Übergeordnetes Werk:	In: The Journal of Engineering - Wiley, 2013, (2020)
Übergeordnetes Werk:	year:2020

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1049/joe.2019.1059

Katalog-ID:	DOAJ06204558X

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520			\|a Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt data, heterogeneity of agents, problems with the automated mapping of equipment connectivity, and partial knowledge of the system. This study presents a distinctive approach that draws upon the use of Bayesian belief network to overcome uncertainties. The key advantage of Bayesian inference methodology is its capability to leverage both causal and correlational data in formulating a plausible conclusion. The proposed method uses state variables produced by distributed state estimation along with data collected from self-aware agents as the main sources of causal information. The rationale for using state estimation is its capability to overarch heterogeneity of AC and DC agents. It is shown that probabilistic graphical models can be employed successfully to detect faults in active hybrid distribution networks. An augmented version of IEEE 13-bus network is utilised to simulate and verify the suitability and effectiveness of the proposed technique.
650		4	\|a power distribution faults
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650		4	\|a inference mechanisms
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650		4	\|a state estimation
650		4	\|a fault diagnosis
650		4	\|a power distribution control
650		4	\|a distributed energy resources
650		4	\|a noisy data
650		4	\|a corrupt data
650		4	\|a automated mapping
650		4	\|a equipment connectivity
650		4	\|a bayesian belief network
650		4	\|a bayesian inference methodology
650		4	\|a correlational data
650		4	\|a state variables
650		4	\|a distributed state estimation
650		4	\|a self-aware agents
650		4	\|a instil uncertainty
650		4	\|a active hybrid ac–dc distribution networks
650		4	\|a fault detection
650		4	\|a ieee 13-bus network
650		4	\|a active hybrid distribution networks
653		0	\|a Engineering (General). Civil engineering (General)
700	0		\|a David Xu \|e verfasserin \|4 aut
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allfields	10.1049/joe.2019.1059 doi (DE-627)DOAJ06204558X (DE-599)DOAJ2fdc58f95c8a47acae19a1019c17d660 DE-627 ger DE-627 rakwb eng TA1-2040 Shahram Negari verfasserin aut Conundrum of fault detection in active hybrid AC–DC distribution networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt data, heterogeneity of agents, problems with the automated mapping of equipment connectivity, and partial knowledge of the system. This study presents a distinctive approach that draws upon the use of Bayesian belief network to overcome uncertainties. The key advantage of Bayesian inference methodology is its capability to leverage both causal and correlational data in formulating a plausible conclusion. The proposed method uses state variables produced by distributed state estimation along with data collected from self-aware agents as the main sources of causal information. The rationale for using state estimation is its capability to overarch heterogeneity of AC and DC agents. It is shown that probabilistic graphical models can be employed successfully to detect faults in active hybrid distribution networks. An augmented version of IEEE 13-bus network is utilised to simulate and verify the suitability and effectiveness of the proposed technique. power distribution faults belief networks distributed power generation distribution networks bayes methods probability inference mechanisms multi-agent systems state estimation fault diagnosis power distribution control distributed energy resources noisy data corrupt data automated mapping equipment connectivity bayesian belief network bayesian inference methodology correlational data state variables distributed state estimation self-aware agents instil uncertainty active hybrid ac–dc distribution networks fault detection ieee 13-bus network active hybrid distribution networks Engineering (General). Civil engineering (General) David Xu verfasserin aut In The Journal of Engineering Wiley, 2013 (2020) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2020 https://doi.org/10.1049/joe.2019.1059 kostenfrei https://doaj.org/article/2fdc58f95c8a47acae19a1019c17d660 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1059 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2020
spelling	10.1049/joe.2019.1059 doi (DE-627)DOAJ06204558X (DE-599)DOAJ2fdc58f95c8a47acae19a1019c17d660 DE-627 ger DE-627 rakwb eng TA1-2040 Shahram Negari verfasserin aut Conundrum of fault detection in active hybrid AC–DC distribution networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt data, heterogeneity of agents, problems with the automated mapping of equipment connectivity, and partial knowledge of the system. This study presents a distinctive approach that draws upon the use of Bayesian belief network to overcome uncertainties. The key advantage of Bayesian inference methodology is its capability to leverage both causal and correlational data in formulating a plausible conclusion. The proposed method uses state variables produced by distributed state estimation along with data collected from self-aware agents as the main sources of causal information. The rationale for using state estimation is its capability to overarch heterogeneity of AC and DC agents. It is shown that probabilistic graphical models can be employed successfully to detect faults in active hybrid distribution networks. An augmented version of IEEE 13-bus network is utilised to simulate and verify the suitability and effectiveness of the proposed technique. power distribution faults belief networks distributed power generation distribution networks bayes methods probability inference mechanisms multi-agent systems state estimation fault diagnosis power distribution control distributed energy resources noisy data corrupt data automated mapping equipment connectivity bayesian belief network bayesian inference methodology correlational data state variables distributed state estimation self-aware agents instil uncertainty active hybrid ac–dc distribution networks fault detection ieee 13-bus network active hybrid distribution networks Engineering (General). Civil engineering (General) David Xu verfasserin aut In The Journal of Engineering Wiley, 2013 (2020) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2020 https://doi.org/10.1049/joe.2019.1059 kostenfrei https://doaj.org/article/2fdc58f95c8a47acae19a1019c17d660 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1059 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2020
allfields_unstemmed	10.1049/joe.2019.1059 doi (DE-627)DOAJ06204558X (DE-599)DOAJ2fdc58f95c8a47acae19a1019c17d660 DE-627 ger DE-627 rakwb eng TA1-2040 Shahram Negari verfasserin aut Conundrum of fault detection in active hybrid AC–DC distribution networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt data, heterogeneity of agents, problems with the automated mapping of equipment connectivity, and partial knowledge of the system. This study presents a distinctive approach that draws upon the use of Bayesian belief network to overcome uncertainties. The key advantage of Bayesian inference methodology is its capability to leverage both causal and correlational data in formulating a plausible conclusion. The proposed method uses state variables produced by distributed state estimation along with data collected from self-aware agents as the main sources of causal information. The rationale for using state estimation is its capability to overarch heterogeneity of AC and DC agents. It is shown that probabilistic graphical models can be employed successfully to detect faults in active hybrid distribution networks. An augmented version of IEEE 13-bus network is utilised to simulate and verify the suitability and effectiveness of the proposed technique. power distribution faults belief networks distributed power generation distribution networks bayes methods probability inference mechanisms multi-agent systems state estimation fault diagnosis power distribution control distributed energy resources noisy data corrupt data automated mapping equipment connectivity bayesian belief network bayesian inference methodology correlational data state variables distributed state estimation self-aware agents instil uncertainty active hybrid ac–dc distribution networks fault detection ieee 13-bus network active hybrid distribution networks Engineering (General). Civil engineering (General) David Xu verfasserin aut In The Journal of Engineering Wiley, 2013 (2020) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2020 https://doi.org/10.1049/joe.2019.1059 kostenfrei https://doaj.org/article/2fdc58f95c8a47acae19a1019c17d660 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1059 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2020
allfieldsGer	10.1049/joe.2019.1059 doi (DE-627)DOAJ06204558X (DE-599)DOAJ2fdc58f95c8a47acae19a1019c17d660 DE-627 ger DE-627 rakwb eng TA1-2040 Shahram Negari verfasserin aut Conundrum of fault detection in active hybrid AC–DC distribution networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt data, heterogeneity of agents, problems with the automated mapping of equipment connectivity, and partial knowledge of the system. This study presents a distinctive approach that draws upon the use of Bayesian belief network to overcome uncertainties. The key advantage of Bayesian inference methodology is its capability to leverage both causal and correlational data in formulating a plausible conclusion. The proposed method uses state variables produced by distributed state estimation along with data collected from self-aware agents as the main sources of causal information. The rationale for using state estimation is its capability to overarch heterogeneity of AC and DC agents. It is shown that probabilistic graphical models can be employed successfully to detect faults in active hybrid distribution networks. An augmented version of IEEE 13-bus network is utilised to simulate and verify the suitability and effectiveness of the proposed technique. power distribution faults belief networks distributed power generation distribution networks bayes methods probability inference mechanisms multi-agent systems state estimation fault diagnosis power distribution control distributed energy resources noisy data corrupt data automated mapping equipment connectivity bayesian belief network bayesian inference methodology correlational data state variables distributed state estimation self-aware agents instil uncertainty active hybrid ac–dc distribution networks fault detection ieee 13-bus network active hybrid distribution networks Engineering (General). Civil engineering (General) David Xu verfasserin aut In The Journal of Engineering Wiley, 2013 (2020) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2020 https://doi.org/10.1049/joe.2019.1059 kostenfrei https://doaj.org/article/2fdc58f95c8a47acae19a1019c17d660 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1059 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2020
allfieldsSound	10.1049/joe.2019.1059 doi (DE-627)DOAJ06204558X (DE-599)DOAJ2fdc58f95c8a47acae19a1019c17d660 DE-627 ger DE-627 rakwb eng TA1-2040 Shahram Negari verfasserin aut Conundrum of fault detection in active hybrid AC–DC distribution networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt data, heterogeneity of agents, problems with the automated mapping of equipment connectivity, and partial knowledge of the system. This study presents a distinctive approach that draws upon the use of Bayesian belief network to overcome uncertainties. The key advantage of Bayesian inference methodology is its capability to leverage both causal and correlational data in formulating a plausible conclusion. The proposed method uses state variables produced by distributed state estimation along with data collected from self-aware agents as the main sources of causal information. The rationale for using state estimation is its capability to overarch heterogeneity of AC and DC agents. It is shown that probabilistic graphical models can be employed successfully to detect faults in active hybrid distribution networks. An augmented version of IEEE 13-bus network is utilised to simulate and verify the suitability and effectiveness of the proposed technique. power distribution faults belief networks distributed power generation distribution networks bayes methods probability inference mechanisms multi-agent systems state estimation fault diagnosis power distribution control distributed energy resources noisy data corrupt data automated mapping equipment connectivity bayesian belief network bayesian inference methodology correlational data state variables distributed state estimation self-aware agents instil uncertainty active hybrid ac–dc distribution networks fault detection ieee 13-bus network active hybrid distribution networks Engineering (General). Civil engineering (General) David Xu verfasserin aut In The Journal of Engineering Wiley, 2013 (2020) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2020 https://doi.org/10.1049/joe.2019.1059 kostenfrei https://doaj.org/article/2fdc58f95c8a47acae19a1019c17d660 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1059 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2020
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source	In The Journal of Engineering (2020) year:2020
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	power distribution faults belief networks distributed power generation distribution networks bayes methods probability inference mechanisms multi-agent systems state estimation fault diagnosis power distribution control distributed energy resources noisy data corrupt data automated mapping equipment connectivity bayesian belief network bayesian inference methodology correlational data state variables distributed state estimation self-aware agents instil uncertainty active hybrid ac–dc distribution networks fault detection ieee 13-bus network active hybrid distribution networks Engineering (General). Civil engineering (General)
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container_title	The Journal of Engineering
authorswithroles_txt_mv	Shahram Negari @@aut@@ David Xu @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	75682270X
id	DOAJ06204558X
language_de	englisch
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callnumber-first	T - Technology
author	Shahram Negari
spellingShingle	Shahram Negari misc TA1-2040 misc power distribution faults misc belief networks misc distributed power generation misc distribution networks misc bayes methods misc probability misc inference mechanisms misc multi-agent systems misc state estimation misc fault diagnosis misc power distribution control misc distributed energy resources misc noisy data misc corrupt data misc automated mapping misc equipment connectivity misc bayesian belief network misc bayesian inference methodology misc correlational data misc state variables misc distributed state estimation misc self-aware agents misc instil uncertainty misc active hybrid ac–dc distribution networks misc fault detection misc ieee 13-bus network misc active hybrid distribution networks misc Engineering (General). Civil engineering (General) Conundrum of fault detection in active hybrid AC–DC distribution networks
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topic_title	TA1-2040 Conundrum of fault detection in active hybrid AC–DC distribution networks power distribution faults belief networks distributed power generation distribution networks bayes methods probability inference mechanisms multi-agent systems state estimation fault diagnosis power distribution control distributed energy resources noisy data corrupt data automated mapping equipment connectivity bayesian belief network bayesian inference methodology correlational data state variables distributed state estimation self-aware agents instil uncertainty active hybrid ac–dc distribution networks fault detection ieee 13-bus network active hybrid distribution networks
topic	misc TA1-2040 misc power distribution faults misc belief networks misc distributed power generation misc distribution networks misc bayes methods misc probability misc inference mechanisms misc multi-agent systems misc state estimation misc fault diagnosis misc power distribution control misc distributed energy resources misc noisy data misc corrupt data misc automated mapping misc equipment connectivity misc bayesian belief network misc bayesian inference methodology misc correlational data misc state variables misc distributed state estimation misc self-aware agents misc instil uncertainty misc active hybrid ac–dc distribution networks misc fault detection misc ieee 13-bus network misc active hybrid distribution networks misc Engineering (General). Civil engineering (General)
topic_unstemmed	misc TA1-2040 misc power distribution faults misc belief networks misc distributed power generation misc distribution networks misc bayes methods misc probability misc inference mechanisms misc multi-agent systems misc state estimation misc fault diagnosis misc power distribution control misc distributed energy resources misc noisy data misc corrupt data misc automated mapping misc equipment connectivity misc bayesian belief network misc bayesian inference methodology misc correlational data misc state variables misc distributed state estimation misc self-aware agents misc instil uncertainty misc active hybrid ac–dc distribution networks misc fault detection misc ieee 13-bus network misc active hybrid distribution networks misc Engineering (General). Civil engineering (General)
topic_browse	misc TA1-2040 misc power distribution faults misc belief networks misc distributed power generation misc distribution networks misc bayes methods misc probability misc inference mechanisms misc multi-agent systems misc state estimation misc fault diagnosis misc power distribution control misc distributed energy resources misc noisy data misc corrupt data misc automated mapping misc equipment connectivity misc bayesian belief network misc bayesian inference methodology misc correlational data misc state variables misc distributed state estimation misc self-aware agents misc instil uncertainty misc active hybrid ac–dc distribution networks misc fault detection misc ieee 13-bus network misc active hybrid distribution networks misc Engineering (General). Civil engineering (General)
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title	Conundrum of fault detection in active hybrid AC–DC distribution networks
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title_full	Conundrum of fault detection in active hybrid AC–DC distribution networks
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title_sort	conundrum of fault detection in active hybrid ac–dc distribution networks
callnumber	TA1-2040
title_auth	Conundrum of fault detection in active hybrid AC–DC distribution networks
abstract	Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt data, heterogeneity of agents, problems with the automated mapping of equipment connectivity, and partial knowledge of the system. This study presents a distinctive approach that draws upon the use of Bayesian belief network to overcome uncertainties. The key advantage of Bayesian inference methodology is its capability to leverage both causal and correlational data in formulating a plausible conclusion. The proposed method uses state variables produced by distributed state estimation along with data collected from self-aware agents as the main sources of causal information. The rationale for using state estimation is its capability to overarch heterogeneity of AC and DC agents. It is shown that probabilistic graphical models can be employed successfully to detect faults in active hybrid distribution networks. An augmented version of IEEE 13-bus network is utilised to simulate and verify the suitability and effectiveness of the proposed technique.
abstractGer	Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt data, heterogeneity of agents, problems with the automated mapping of equipment connectivity, and partial knowledge of the system. This study presents a distinctive approach that draws upon the use of Bayesian belief network to overcome uncertainties. The key advantage of Bayesian inference methodology is its capability to leverage both causal and correlational data in formulating a plausible conclusion. The proposed method uses state variables produced by distributed state estimation along with data collected from self-aware agents as the main sources of causal information. The rationale for using state estimation is its capability to overarch heterogeneity of AC and DC agents. It is shown that probabilistic graphical models can be employed successfully to detect faults in active hybrid distribution networks. An augmented version of IEEE 13-bus network is utilised to simulate and verify the suitability and effectiveness of the proposed technique.
abstract_unstemmed	Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt data, heterogeneity of agents, problems with the automated mapping of equipment connectivity, and partial knowledge of the system. This study presents a distinctive approach that draws upon the use of Bayesian belief network to overcome uncertainties. The key advantage of Bayesian inference methodology is its capability to leverage both causal and correlational data in formulating a plausible conclusion. The proposed method uses state variables produced by distributed state estimation along with data collected from self-aware agents as the main sources of causal information. The rationale for using state estimation is its capability to overarch heterogeneity of AC and DC agents. It is shown that probabilistic graphical models can be employed successfully to detect faults in active hybrid distribution networks. An augmented version of IEEE 13-bus network is utilised to simulate and verify the suitability and effectiveness of the proposed technique.
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title_short	Conundrum of fault detection in active hybrid AC–DC distribution networks
url	https://doi.org/10.1049/joe.2019.1059 https://doaj.org/article/2fdc58f95c8a47acae19a1019c17d660 https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1059 https://doaj.org/toc/2051-3305
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