Condition assessment of power transformers using a synthetic analysis method based on association rule and variable weight coefficients

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Condition assessment for power transformer requires not only integrating the known artificial intelligence (AI) technology, but also exploiting the interrelation of the measured data. According to the association rule of information data and the variable weight synthesizing theory of factor spaces, an assessment method of transformer condition was proposed in this paper. Via analyzing the interrelation of the independent status parameters (ISP) and transformer fault types, the set of synthetic status parameters (SSP) can be built up. For avoiding interference from subjective experience, association rule theory was used to calculate the constant weight coefficients (CWC) of the ISPs. Since the true transformer condition may not always be accurately reflected under the condition of CWCs of a few SSPs, the method of variable weight synthesizing was used for computing the variable weight coefficients (VWC) of the SSPs. Then, combining with the existing maintenance procedures, a preferable condition assessing system of power transformer was proposed. Operational example proved the condition assessing system may reflect the real operation condition of power transformer.