Distribution System Topology Identification for DER Management Systems Using Deep Neural Networks

DC FieldValueLanguage
dc.contributor.authorJafarian, Mohammad-
dc.contributor.authorSoroudi, Alireza-
dc.contributor.authorKeane, Andrew-
dc.date.accessioned2021-11-08T14:13:23Z-
dc.date.available2021-11-08T14:13:23Z-
dc.date.copyright2020 IEEEen_US
dc.date.issued2020-08-06-
dc.identifier.urihttp://hdl.handle.net/10197/12584-
dc.descriptionThe 2020 IEEE Power & Energy PES General Meeting, Virtual Conference, 3-6 August 2020en_US
dc.description.abstractFor DER management systems (DERMS) to manage and coordinate the DER units, awareness of distribution system topology is necessary. Most of the approaches developed for the identification of distribution network topology rely on the accessibility of network model and load forecasts, which are logically not available to DERMS. In this paper, the application of deep neural networks in pattern recognition is availed for this purpose, relying only on the measurements available to DERMS. IEEE 123 node test feeder is used for simulation. Six switching configurations and operation of two protective devices are considered, resulting in 24 different topologies. Monte Carlo simulations are conducted to explore different DER production and load values. A two-hidden layer feed-forward deep neural network is used to classify different topologies. Results show the proposed approach can successfully predict the switching configurations and status of protective devices. Sensitivity analysis shows that the positive and negative sequence components of the voltage (from DER units and substation) have the most contribution to discrimination among different switching configurations.en_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectDeep neural networksen_US
dc.subjectDistributed energy resources management systemsen_US
dc.subjectDistribution networksen_US
dc.subjectTopology identificationen_US
dc.titleDistribution System Topology Identification for DER Management Systems Using Deep Neural Networksen_US
dc.typeConference Publicationen_US
dc.internal.authorcontactothermohammad.jafarian@ucd.ieen_US
dc.internal.webversionshttps://pes-gm.org/2020/-
dc.statusPeer revieweden_US
dc.identifier.doi10.1109/PESGM41954.2020.9282121-
dc.neeo.contributorJafarian|Mohammad|aut|-
dc.neeo.contributorSoroudi|Alireza|aut|-
dc.neeo.contributorKeane|Andrew|aut|-
dc.date.updated2020-09-04T15:41:43Z-
dc.identifier.grantid16/IA/4496-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:Electrical and Electronic Engineering Research Collection
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