Mohammad Sherkat Masoum

Characterization of transformer FRA signature under various winding faults

Frequency response analysis (FRA) is gaining global popularity in detecting power transformer winding and core deformations due to the development of FRA test equipment. However, because FRA relies on graphical analysis, interpretation of its signatures is still a very specialized area that calls for skillful personnel to detect the sort and likely place of the fault as so far, there is no reliable standard code for FRA signature classification and quantification. This paper aims to initiate the establishment of standard codes for FRA signature interpretation through comprehensive simulation analysis on a detailed transformer distributed parameters-based model. Various mechanical faults such as axial displacement, buckling stress, disk space variation and bushing fault are simulated on the model to study its impact on the FRA signature. The main contribution of this paper is the comprehensive table it presents for FRA signature sensitivity to winding and core deformations that can be used for classification and quantification of the transformer FRA signature.

Standardization of DGA interpretation techniques using fuzzy logic approach

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation. As a result, various DGA interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. DGA interpretation is yet a challenge in the power transformer condition monitoring research area. To alleviate this issue, this paper introduces a fuzzy logic approach to help in standardizing DGA results quantification and classification using various interpretation techniques such as key gas, Rogers ratio, IEC ratio, Doernenburg and Duval triangle methods. In this context, DGA results for 2000 oil samples have been collected from different transformers of different ratings, life span and operating conditions. Traditional DGA interpretation techniques are used to analyze the results which are then compared with the results of the fuzzy logic models. Results show that the fuzzy logic models enhance the consistency among all current interpretation techniques and can eliminate the need for expert personal to interpret DGA results.