Incipient interturn fault detection for ONAN power transformers using electrothermal characteristic fusion analysis

Existing interturn fault detecting methods rely on winding impedance, winding current, and dissolved gases. They are effective only when the insulation is severely damaged. This paper proposes a novel detection method based on fusion analysis of electrothermal characteristics including winding currents, temperatures of four areas on the tank wall, top oil and ambient temperatures, which can identify the interturn fault at an early stage. When an incipient interturn fault occurs, the heat generated by the faulty turns is transferred to the oil and tank wall, leading to an increase in top oil and tank wall temperatures. Thus, the incipient fault can be detected by analysing these electrothermal characteristic parameters. Borrowing the idea of digital twin (DT), this method establishes a high‐fidelity simulation model to simulate the transformer electrothermal characteristics under different operating conditions. Afterward, an intelligent neural network is adopted to extract the quantitative relationship between the eight feature attributions and fault conditions. Finally, this neural network is utilized to detect the incipient interturn fault for the transformer entity. Case studies are conducted on a 100 kVA transformer with oil natural air natural (ONAN) cooling mode. The detection accuracy is improved by 68.5% compared to the winding current‐based method. The manuscript proposes a novel method based on fusion analysis of electrothermal characteristics for detecting incipient interturn fault of ONAN transformers. This method relies on winding currents, ambient temperature, top oil temperature, and the temperatures in four areas of the tank wall, which can identify the early faults before interturn short circuit and arc discharge faults. The work in this paper provides a new idea for the early interturn fault diagnosis of transformers.