{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T07:06:08Z","timestamp":1762067168105,"version":"build-2065373602"},"reference-count":18,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T00:00:00Z","timestamp":1664150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Oil-submerged transformer is one of the inherent instruments in the South African power system. Transformer malfunction or impairment may interpose the operation of the electric power distribution and transmission system, coupled with liability for high overhaul costs. Hence, recognition of inchoate faults in an oil-submerged transformer is indispensable and it has turned into an intriguing subject of interest by utility owners and transformer manufacturers. This work proposes a hybrid implementation of a multi-layer artificial neural network (MLANN) and IEC 60599:2022 gas ratio method in identifying inchoate faults in mineral oil-based submerged transformers by employing the dissolved gas analysis (DGA) method. DGA is a staunch practice to discover inchoate faults as it furnishes comprehensive information in examining the transformer state. In current work, MLANN was established to pigeonhole seven fault types of transformer states predicated on the three International Electrotechnical Commission (IEC) combustible gas ratios. The designs enmesh the development of numerous MLANN algorithms and picking networks with the optimum performance. The gas ratios are in accordance with the IEC 60599:2022 standard whilst an empirical databank comprised of 100 datasets was used in the training and testing activities. The designated MLANN design produces an overall correlation coefficient of 0.998 in the categorization of transformer state with reference to the combustible gas produced.<\/jats:p>","DOI":"10.3390\/make4040042","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T01:51:49Z","timestamp":1664329909000},"page":"839-851","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["On the Application of Artificial Neural Network for Classification of Incipient Faults in Dissolved Gas Analysis of Power Transformers"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3635-0988","authenticated-orcid":false,"given":"Bonginkosi A.","family":"Thango","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"118031","DOI":"10.1109\/ACCESS.2021.3107332","article-title":"Enhancing the Diagnostic Accuracy of DGA Techniques Based on IEC-TC10 and Related Databases","volume":"9","author":"Gouda","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.1109\/TDEI.2021.009470","article-title":"Gaussian Process Multi-Class Classification for Transformer Fault Diagnosis Using Dissolved Gas Analysis","volume":"28","author":"Wang","year":"2021","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/TDEI.2015.005410","article-title":"Interpretation of DGA for transformer fault diagnosis with complementary SaE-ELM and arctangent transform","volume":"23","author":"Li","year":"2016","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"150637","DOI":"10.1109\/ACCESS.2021.3125379","article-title":"Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty","volume":"9","author":"Rediansyah","year":"2021","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Thango, B.A., and Jordaan, J.A. (October, January 28). Stray Gassing of Transformer Oil in Distributed Solar Photovoltaic (DSPV) Systems. Proceedings of the 2020 6th IEEE International Energy Conference (ENERGYCon), Gammarth, Tunisia.","key":"ref_5","DOI":"10.1109\/ENERGYCon48941.2020.9236522"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"170343","DOI":"10.1109\/ACCESS.2019.2926435","article-title":"Impact of Load Ramping on Power Transformer Dissolved Gas Analysis","volume":"7","author":"Cui","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1109\/TDEI.2021.009415","article-title":"Two Graphical Shapes Based on DGA for Power Transformer Fault Types Discrimination","volume":"28","author":"Emara","year":"2021","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1109\/TDEI.2021.009770","article-title":"Identification and Application of Machine Learning Algorithms for Transformer Dissolved Gas Analysis","volume":"28","author":"Rao","year":"2021","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"25954","DOI":"10.1109\/ACCESS.2022.3156102","article-title":"Reliable Estimation for Health Index of Transformer Oil Based on Novel Combined Predictive Maintenance Techniques","volume":"10","author":"Badawi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TDEI.2018.007191","article-title":"Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis","volume":"25","author":"Faiz","year":"2018","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"unstructured":"(2022). Mineral Oil-Filled Electrical Equipment in Service\u2014Guidance on the Interpretation of Dissolved and Free Gases Analysis (Standard No. IEC 60599:2022). Available online: https:\/\/webstore.iec.ch\/preview\/info_iec60599%7Bed4.0.CMV%7Den.pdf.","key":"ref_11"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/TSG.2014.2384501","article-title":"Smart Transformer for Smart Grid\u2014Intelligent Framework and Techniques for Power Transformer Asset Management","volume":"6","author":"Ma","year":"2015","journal-title":"IEEE Trans. Smart Grid"},{"doi-asserted-by":"crossref","unstructured":"Patekar, K.D., and Chaudhry, B. (2019, January 21\u201323). DGA analysis of transformer using Artificial neutral network to improve reliability in Power Transformers. 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Sci."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/4\/42\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:39:57Z","timestamp":1760143197000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/4\/42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,26]]},"references-count":18,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["make4040042"],"URL":"https:\/\/doi.org\/10.3390\/make4040042","relation":{},"ISSN":["2504-4990"],"issn-type":[{"type":"electronic","value":"2504-4990"}],"subject":[],"published":{"date-parts":[[2022,9,26]]}}}