A fuzzy system for detection of incipient fault in power transformers based on gas-in-oil analysis

Dissolved gas analysis (DGA) is one of the most useful techniques do detect the incipient faults of power transformer. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational natures. This work aims to develop an intelligent system of preventive maintenance for automatically detecting incipient fault in power transformers through analysis of dissolved gases in transformer insulating oil by introducing the IEC 599 standard (International Electrotechnical Commission). The data used to model the system are taken from chromatographic analysis of CELPE (Electrical Company from Pernambuco). The results show a system with high accuracy when compared with other papers approaching the same problem. Moreover, the results also proved the ability of multiple incipient faults detection.