Publicação
Wavelet LSTM for Fault Forecasting in Electrical Power Grids
| Resumo: | An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed. |
|---|---|
| Autores principais: | Branco, Nathielle |
| Outros Autores: | Santos Matos Cavalca, Mariana; Stefenon, Stéfano Frizzo; LEITHARDT, VALDERI |
| Assunto: | electrical power grids; fault forecasting; long short-term memory; time series forecasting; wavelet transform |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Portalegre |
| Idioma: | inglês |
| Origem: | Instituto Politécnico de Portalegre |
| _version_ | 1865248821656682496 |
|---|---|
| author | Branco, Nathielle |
| author2 | Santos Matos Cavalca, Mariana Stefenon, Stéfano Frizzo LEITHARDT, VALDERI |
| author2_role | author author author |
| author_facet | Branco, Nathielle Santos Matos Cavalca, Mariana Stefenon, Stéfano Frizzo LEITHARDT, VALDERI |
| author_role | author |
| contributor_name_str_mv | Repositório Comum |
| country_str | PT |
| creators_json_str | [{\"Person.name\":\"Branco, Nathielle\",\"Person.identifier.orcid\":\"0000-0001-7565-3274\"},{\"Person.name\":\"Santos Matos Cavalca, Mariana\",\"Person.identifier.orcid\":\"0000-0001-5728-2158\"},{\"Person.name\":\"Stefenon, Stéfano Frizzo\",\"Person.identifier.orcid\":\"0000-0002-3723-616X\"},{\"Person.name\":\"LEITHARDT, VALDERI\",\"Person.identifier.orcid\":\"0000-0003-0446-9271\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Repositório Comum |
| datacite.creators.creator.creatorName.fl_str_mv | Branco, Nathielle Santos Matos Cavalca, Mariana Stefenon, Stéfano Frizzo LEITHARDT, VALDERI |
| datacite.date.Accepted.fl_str_mv | 2022-10-30T00:00:00Z |
| datacite.date.available.fl_str_mv | 2023-02-01T18:09:26Z |
| datacite.date.embargoed.fl_str_mv | 2023-02-01T18:09:26Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | electrical power grids; fault forecasting; long short-term memory; time series forecasting; wavelet transform |
| datacite.titles.title.fl_str_mv | Wavelet LSTM for Fault Forecasting in Electrical Power Grids |
| dc.contributor.none.fl_str_mv | Repositório Comum |
| dc.creator.none.fl_str_mv | Branco, Nathielle Santos Matos Cavalca, Mariana Stefenon, Stéfano Frizzo LEITHARDT, VALDERI |
| dc.date.Accepted.fl_str_mv | 2022-10-30T00:00:00Z |
| dc.date.available.fl_str_mv | 2023-02-01T18:09:26Z |
| dc.date.embargoed.fl_str_mv | 2023-02-01T18:09:26Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10400.26/43551 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | electrical power grids; fault forecasting; long short-term memory; time series forecasting; wavelet transform |
| dc.title.fl_str_mv | Wavelet LSTM for Fault Forecasting in Electrical Power Grids |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://comum.rcaap.pt/bitstreams/88ccd7f7-50a2-421b-abc7-442fa6ab89d5/download |
| id | ipp_ddca4e30e1eec1cff41b21685e82aec4 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10400.26/43551 |
| instacron_str | ipp |
| institution | Instituto Politécnico de Portalegre |
| instname_str | Instituto Politécnico de Portalegre |
| language | eng |
| network_acronym_str | ipp |
| network_name_str | Instituto Politécnico de Portalegre |
| oai_identifier_str | oai:comum.rcaap.pt:10400.26/43551 |
| organization_str_mv | urn:organizationAcronym:ipp |
| person_str_mv | Branco, Nathielle Branco, Nathielle http://orcid.org/0000-0001-7565-3274 0000-0001-7565-3274 Santos Matos Cavalca, Mariana Santos Matos Cavalca, Mariana http://orcid.org/0000-0001-5728-2158 0000-0001-5728-2158 Stefenon, Stéfano Frizzo Stefenon, Stéfano Frizzo https://www.ciencia-id.pt/4019-BB36-7F74 4019-BB36-7F74 http://orcid.org/0000-0002-3723-616X 0000-0002-3723-616X LEITHARDT, VALDERI LEITHARDT, VALDERI https://www.ciencia-id.pt/0614-5834-E7F3 0614-5834-E7F3 http://orcid.org/0000-0003-0446-9271 0000-0003-0446-9271 |
| publishDate | 2022 |
| reponame_str | Instituto Politécnico de Portalegre |
| repository_id_str | urn:repositoryAcronym:ipp |
| service_str_mv | urn:repositoryAcronym:ipp |
| spelling | engpt_PTAn electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed.application/pdfpt_PTWavelet LSTM for Fault Forecasting in Electrical Power GridsPersonalBranco, NathielleDSpacehttp://dspace.org/items/4d5ebdfb-3a32-4508-8692-156f7fa846aaDSpacehttp://dspace.org/items/4d5ebdfb-3a32-4508-8692-156f7fa846aaBrancoNathielleORCIDhttp://orcid.org0000-0001-7565-3274PersonalSantos Matos Cavalca, MarianaDSpacehttp://dspace.org/items/e88ef994-d875-4f7d-8f5d-bfcd4fab6bb1DSpacehttp://dspace.org/items/e88ef994-d875-4f7d-8f5d-bfcd4fab6bb1Santos Matos CavalcaMarianaORCIDhttp://orcid.org0000-0001-5728-2158Scopus Author IDhttps://www.scopus.com36545649600Scopus Author IDhttps://www.scopus.com57188836598PersonalStefenon, Stéfano FrizzoDSpacehttp://dspace.org/items/ae8b0861-1e25-47fb-bcdb-a44c98768634DSpacehttp://dspace.org/items/ae8b0861-1e25-47fb-bcdb-a44c98768634StefenonStefano FrizzoCiência IDhttps://www.ciencia-id.pt4019-BB36-7F74ORCIDhttp://orcid.org0000-0002-3723-616XResearcher IDhttps://www.researcherid.comAAD-7639-2019Scopus Author IDhttps://www.scopus.com57194147390PersonalLEITHARDT, VALDERIDSpacehttp://dspace.org/items/ab15f7c6-e882-406e-813d-2629e9cec5c8DSpacehttp://dspace.org/items/ab15f7c6-e882-406e-813d-2629e9cec5c8REIS QUIETINHO LEITHARDTVALDERICiência IDhttps://www.ciencia-id.pt0614-5834-E7F3ORCIDhttp://orcid.org0000-0003-0446-9271Scopus Author IDhttps://www.scopus.com35303109600HostingInstitutionOrganizationalRepositório Comume-mailmailto:comum@rcaap.ptcomum@rcaap.ptDOIIsPartOf10.3390/s222183232023-02-01T18:09:26Z2022-10-302022-11-03T15:10:51Z2022-10-30T00:00:00ZHandlehttp://hdl.handle.net/10400.26/43551http://purl.org/coar/access_right/c_abf2open accesselectrical power grids;fault forecasting;long short-term memory;time series forecasting;wavelet transform1403744 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://comum.rcaap.pt/bitstreams/88ccd7f7-50a2-421b-abc7-442fa6ab89d5/downloadSensors |
| spellingShingle | Wavelet LSTM for Fault Forecasting in Electrical Power Grids Branco, Nathielle electrical power grids; fault forecasting; long short-term memory; time series forecasting; wavelet transform |
| status | NEW |
| subject.fl_str_mv | electrical power grids; fault forecasting; long short-term memory; time series forecasting; wavelet transform |
| title | Wavelet LSTM for Fault Forecasting in Electrical Power Grids |
| title_full | Wavelet LSTM for Fault Forecasting in Electrical Power Grids |
| title_fullStr | Wavelet LSTM for Fault Forecasting in Electrical Power Grids |
| title_full_unstemmed | Wavelet LSTM for Fault Forecasting in Electrical Power Grids |
| title_short | Wavelet LSTM for Fault Forecasting in Electrical Power Grids |
| title_sort | Wavelet LSTM for Fault Forecasting in Electrical Power Grids |
| topic | electrical power grids; fault forecasting; long short-term memory; time series forecasting; wavelet transform |
| topic_facet | electrical power grids; fault forecasting; long short-term memory; time series forecasting; wavelet transform |
| url | http://hdl.handle.net/10400.26/43551 |
| visible | 1 |