Enhancement of Power Transformer State of Health Diagnostics Based on Fuzzy Logic System of DGA

Dissolved Gas Analysis (DGA) of liquid insulation is an effective means for diagnosing power transformers. The gas contents in insulating oil can be gathered on-line and off-line to indicate the health condition of the transformers, thereafter there are many interpretations of the gas contents. In this work, Seven-fuzzy interpretation modules are individually established, tested and lately combined to monitor power transformers' health. The developed method incorporates trending of the concentration of the dissolved gases over the operating life. The approach processes current and/or historical DGA data, using the 7-developed logic modules, to determine the current state of a transformer, provide information regarding the fault type, fault probability, fault severity and recommended future sampling interval in addition to operating procedure, consistent with industry standards. The developed diagnosis system has been validated using 1290 samples from fresh and previously tested mineral oil filled transformers. The proposed system is proved, based on field data, to be 99% accurate in identifying transformers being in normal or abnormal operation. For the cases where a transformer fault was known, the proposed technique has less than 2% inaccuracy in recognizing the fault's type in comparison to other approaches discussed in literature.