A new technique for fault diagnosis in transformer insulating oil based on infrared spectroscopy measurements

Abstract

Condition monitoring of the insulating system within power transformers has a massive importance according to the electrical utilities. Dissolved gas analysis (DGA) is frequently used for this purpose. However, DGA lacks the necessary level of accuracy to identify all equipment faults, particularly in their initial stages of degradation. Also, it does not have the capability for real-time monitoring and relies on manual sampling and laboratory testing, causing potential delays in fault identification. Additionally, the interpretation of DGA data necessitates specialised expertise, which may pose difficulties for smaller entities that have limited access to resources. Therefore, the contribution of this research is to use infrared spectroscopy measurements as a new effective technique substituting the DGA method for fault diagnosis in insulating oil. The inception faults that were considered in this study were the electrical fault (discharges of high energy) and the thermal fault (300°C < Temperature < 700°C). Regarding that, two test cells were crafted especially for serving the simulation processes inside the laboratory for both types of inception faults. Subsequently, six samples of pure paraffinic mineral oil were taken to be degraded in the laboratory. Following that, all of them besides another sample that were not subjected to any kind of faults were taken to be examined by Fourier transform infrared (FTIR) spectroscopy to obtain an overview of the oil's behaviour in each fault case. After that, the FTIR analysis was initially verified utilising the DGA method. Then, for further affirmation, the dielectric dissipation factor (DDF) for all samples was measured. In the final analysis, the verification tests provide experimental evidence about the outperformance of this new optical technique in detecting the transformer's inception faults in addition to proving its potential for being a superior alternative to the well-known traditional diagnostic techniques.