A novel approach for oil-based transformer fault identification in electrical secondary distribution networks

Abstract

Oil-immersed transformers are mostly installed equipment in electrical power networks. Thus, ensuring the reliability of these equipment is paramount. Fault identification is one of the measures taken to ensure the reliability. Concerning fault identification mechanisms, several methods exist; one of which is the dissolved gas analysis (DGA) tool that has been widely used purposefully for oil-immersed transformers. The tool is used to capture and analyze the gases dissolved in the oil. Studies have proposed various fault identification methods based on this tool such as the Dornenburg, key gas, and International Electrotechnical Commission (IEC) standards methods using DGA data. However, the accuracy of these methods seems to be limited by the context, such as the type and number of parameters used. In this study, the novel hybrid fault identification method based on multilayer neural networks (MLANN) and expert knowledge is proposed to improve the accuracy fault identification process by considering the specific characteristic parameters extracted from the historical DGA dataset. The proposed hybrid method improves accuracy by 14.46% when compared to 12 benchmark methods. This improvement portrays that our method can be integrated into a scheduling platform for maintenance decision support in an electrical power network with accuracy. Furthermore, the promising accuracy in transformer fault identification is expected to increase safety and power reliability.