{"id":"phs5wyfbph","doi":{"id":"10.17632/phs5wyfbph.2","status":"allocated","prefix":"10.17632"},"name":"Dissolved Gas Analysis Method Fusion Based on Machine Learning for Fault Diagnosis and Condition Assessment of Power Transformers ","description":"This directory contains the Python codes used to implement the proposed methodology for power transformer fault diagnosis based on Dissolved Gas Analysis (DGA), Dempster–Shafer evidence fusion, and Random Forest classification.\n\nPython Codes:\n\nRF_con_subtipos_FIN.py: Main code of the methodology. This script executes the final Random Forest-based diagnostic process and integrates the diagnostic refinement using subtypes. It represents the main file to be executed for reproducing the proposed methodology.\nD_S_para_RF.py: Python code used to generate the consensus pseudo-labels through Dempster–Shafer evidence theory. The outputs generated by this script are used as input labels for the Random Forest training stage.\nDoernenburg_RF.py: Python code containing the implementation of the Doernenburg DGA interpretation method used as one of the diagnostic evidence sources.\nRogers_RF.py: Python code containing the implementation of the Rogers DGA interpretation method used as one of the diagnostic evidence sources.\nGas_Key_RF.py: Python code containing the implementation of the Gas Key DGA interpretation method used as one of the diagnostic evidence sources.\nPentagono_1_2_RF.py: Python code containing the implementation of the Duval Pentagon 1 and Duval Pentagon 2 methods. These methods are used for DGA-based diagnosis and diagnostic refinement.\nTriangulo_1_4_5_RF.py: Python code containing the implementation of the Duval Triangle 1, Duval Triangle 4, and Duval Triangle 5 methods. These methods are used for conventional DGA diagnosis and physical diagnostic refinement through subtypes.\n\nThese codes allow the main stages of the research to be reproduced, including the application of conventional DGA methods, the generation of pseudo-labels using Dempster–Shafer, classification through Random Forest, and diagnostic refinement with Duval subtypes. In this way, a methodology based on Random Forest is proposed for fault analysis and diagnosis in power transformers using DGA.","version":2,"contributors":[{"profile_id":"5cb8c30c-fc61-4286-b761-833f14e03b98","first_name":"Helen Julieta","last_name":"Alarcon Carizales"}],"files":[{"filename":"CODES V2.zip","id":"e7f91edb-3e34-45a8-be66-a90784a04298","content_details":{"id":"fdf11ee1-d177-4a86-9a59-cc2070b6600c","sha256_hash":"93e61604cb0ed571614852d6873ef40ba01442f77c1c72972c4aa9a15193b1ea","content_type":"application/x-zip-compressed","size":45764,"created_date":"2026-08-21T05:49:50.859Z","download_url":"https://data.mendeley.com/public-files/datasets/phs5wyfbph/files/e7f91edb-3e34-45a8-be66-a90784a04298/file_downloaded","view_url":"https://data.mendeley.com/public-files/datasets/phs5wyfbph/files/e7f91edb-3e34-45a8-be66-a90784a04298/file_viewed","download_expiry_time":"2126-09-15T16:34:59.089124926Z"},"metrics":{"downloads":0,"previews":0},"size":45764,"status":"COMPLETED"},{"filename":"DATOS_VALIDACION_P2-iknVw9.xlsx","id":"3ef8ff32-e345-421a-808f-f315eaa88b89","content_details":{"id":"a64a1bb6-af77-46c2-b863-3b7d18790a78","sha256_hash":"f807ab63e88e7cdaa76d85a71aea541ce2a93292a640990ea35cc74413b514f0","content_type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","size":17744,"created_date":"2026-08-21T05:50:16.932Z","download_url":"https://data.mendeley.com/public-files/datasets/phs5wyfbph/files/3ef8ff32-e345-421a-808f-f315eaa88b89/file_downloaded","view_url":"https://data.mendeley.com/public-files/datasets/phs5wyfbph/files/3ef8ff32-e345-421a-808f-f315eaa88b89/file_viewed","download_expiry_time":"2126-09-15T16:34:59.089128686Z"},"metrics":{"downloads":0,"previews":0},"size":17744,"status":"COMPLETED"},{"filename":"DATOS_VALIDACION_P2.xlsx","id":"4ea5e8d3-e770-4124-b873-82fd77fa7e55","content_details":{"id":"c63ac9c9-561e-4398-b6e8-3ef4b0b57e35","sha256_hash":"f807ab63e88e7cdaa76d85a71aea541ce2a93292a640990ea35cc74413b514f0","content_type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","size":17744,"created_date":"2026-08-21T05:50:01.029Z","download_url":"https://data.mendeley.com/public-files/datasets/phs5wyfbph/files/4ea5e8d3-e770-4124-b873-82fd77fa7e55/file_downloaded","view_url":"https://data.mendeley.com/public-files/datasets/phs5wyfbph/files/4ea5e8d3-e770-4124-b873-82fd77fa7e55/file_viewed","download_expiry_time":"2126-09-15T16:34:59.089132466Z"},"metrics":{"downloads":0,"previews":0},"size":17744,"status":"COMPLETED"},{"filename":"README.txt","id":"0c3ce4d5-aded-4c41-9df8-44f1e7a3ceb1","content_details":{"id":"bd0b94e5-c027-4aec-a53d-9fac1399a83d","sha256_hash":"03476886fd098833729738d33001024a6fb8bb45a117a7369fe7ea83c6df1dee","content_type":"text/plain","size":3105,"created_date":"2026-06-22T05:39:27.823Z","download_url":"https://data.mendeley.com/public-files/datasets/phs5wyfbph/files/0c3ce4d5-aded-4c41-9df8-44f1e7a3ceb1/file_downloaded","view_url":"https://data.mendeley.com/public-files/datasets/phs5wyfbph/files/0c3ce4d5-aded-4c41-9df8-44f1e7a3ceb1/file_viewed","download_expiry_time":"2126-09-15T16:34:59.089136076Z"},"metrics":{"downloads":0,"previews":0},"size":3105,"status":"COMPLETED"}],"versions":[{"version":2,"available":true,"publish_date":"2026-08-24T01:09:08.225Z"},{"version":1,"available":true,"publish_date":"2026-06-22T18:23:37.007Z"}],"articles":[],"categories":[{"id":"data.elsevier.com/vocabulary/OmniScience/Concept-170589414","label":"Electrical Engineering"},{"id":"data.elsevier.com/vocabulary/OmniScience/Concept-253540919","label":"Power Transformer"},{"id":"data.elsevier.com/vocabulary/OmniScience/Concept-495388054","label":"Asset Management"}],"institutions":[{"id":"63736bb0-49e4-4fcf-bccc-85be7dc9a2c9","name":"National University of San Juan","ror_id":"https://ror.org/02rsnav77"}],"metrics":{"views":0,"file_downloads":0,"file_previews":0},"available":true,"method":"read the \"README.txt\" document","size":84357,"owner":{"profile_id":"5cb8c30c-fc61-4286-b761-833f14e03b98","first_name":"Helen Julieta","last_name":"Alarcon Carizales"},"channel":"WEB","owner_id":"5cb8c30c-fc61-4286-b761-833f14e03b98","publish_date":"2026-08-24T01:09:08.225Z","data_licence":{"id":"01d9c749-3c4d-4431-9df3-620b2dcfe144","description":"You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.","url":"http://creativecommons.org/licenses/by/4.0","category":"Creative","short_name":"CC BY 4.0","full_name":"Creative Commons Attribution 4.0 International"},"related_links":[],"funders":[],"customer_id":"7377952c-6404-4428-bbfc-77242cfc301e","modified_on":"2026-08-21T05:50:16.919Z","created_on":"2026-06-22T05:39:16.592Z","confidential":false,"links":{"view":"https://data.mendeley.com/datasets/phs5wyfbph"},"repository":{"id":"MENDELEY_DATA","name":"Mendeley Data"}}