7 th INTERNATIONAL CONGRESS ON ENGINEERING AND TECHNOLOGY MANAGEMENT, İstanbul, Turkey, 16 - 18 April 2022, pp.14-21
Small and medium power monophase transformers usually have cylindrical and two layered form. Therefore, two-layer and cylindrical winding transformers are widely used in the energy sector in industry. The transformer has a magnetic circuit and losses due to magnetism are one of the most important factors affecting the performance of the transformer. The magnetic losses of the transformer are mainly caused by leakage fluxes. Leakage fluxes are represented by the leakage induction coefficient and are calculated depending on the structural dimensions of the transformer. The performance of such transformers, which is widely used in the industry, directly affects the users and therefore the consumers. Artificial intelligence algorithms that can automatically detect or classify the leakage fluxes of transformers can facilitate the work of designers. In this study, five different machine learning (ML) models are applied to detect and classify the leakage flux of cylindrical two-layer monophase transformers. As a result, it was observed that ML methods gave successful results in determining the leakage induction coefficient.