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However, existing approaches face inherent limitations; thus, traveling\u2010wave (TW) methods offer high localization accuracy but require stringent measurement conditions, while data\u2010driven models provide robustness but may suffer from reduced precision under certain scenarios. To address these challenges, this paper proposes a hybrid fault detection and localization methodology that combines physics\u2010based and deep learning approaches within a unified and modular framework. The proposed method integrates a TW analytical module and a one\u2010dimensional convolutional neural network (1D\u2010CNN) model, designed to exploit their complementary strengths. A key contribution of this work lies in the development of an adaptive decision fusion strategy, which combines the outputs of both modules based on confidence levels to produce a more accurate and reliable fault location estimate. The entire approach is implemented within a modular platform architecture, enabling clear separation between data acquisition, preprocessing, individual localization modules, and decision\u2010making. To ensure a rigorous evaluation, a large dataset of fault scenarios is generated using high\u2010fidelity electromagnetic transient simulations (PSCAD\/EMTDC) of the Song Loulou\u2013Logbaba 225\u2010kV transmission line in Cameroon, covering a wide range of fault types, locations, resistances, and operating conditions. The performance of each module and the hybrid approach is systematically assessed using standard detection and localization metrics. The results demonstrate that the TW module achieves high localization precision, while the 1D\u2010CNN model ensures robust detection and fault classification under varying conditions. The proposed hybrid approach significantly improves overall performance, achieving a 100% detection rate and reducing the average localization error to 0.017\u2009km, outperforming individual methods. These findings confirm that the proposed hybrid methodology enhances both accuracy and robustness, making it a promising solution for advanced fault monitoring and protection in modern power grids.<\/jats:p>","DOI":"10.1155\/jece\/8886698","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T09:03:27Z","timestamp":1780045407000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Hybrid Platform Based on Deep Learning for Fault Detection and Localization in a Smart Distribution Grid"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1257-8470","authenticated-orcid":false,"given":"Felix Ghislain Yem","family":"Souhe","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2741-9313","authenticated-orcid":false,"given":"Gabriel","family":"Ekemb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0867-2357","authenticated-orcid":false,"given":"Camille Franklin","family":"Mbey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7848-3004","authenticated-orcid":false,"given":"Vinny Junior Foba","family":"Kakeu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3783-3343","authenticated-orcid":false,"given":"Alexandre Teplaira","family":"Boum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.64539\/sjer.v2i1.2026.374"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2026.112758"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.21608\/EIJEST.2025.372472.1327"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/en19020507"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2025.111458"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00202-020-01186-1"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/15567036.2021.1948637"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-85563-5"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2022.02.275"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2021.107563"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2025.111927"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/en15145005"},{"key":"e_1_2_11_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2024.110943"},{"key":"e_1_2_11_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2020.106261"},{"key":"e_1_2_11_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/en16227680"},{"key":"e_1_2_11_16_2","doi-asserted-by":"crossref","unstructured":"KaplanH. 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