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The explicitly numerical or well-defined correlations between the dielectrics responses (DRs) and dielectric essences of oil-impregnated paper (OIP) insulation have not been fully understood yet. As a result, it is rather difficult to quantitatively diagnose the critical insulation condition, like determining moisture content in insulation paper (MCP) of power equipment only using electrical-based techniques such as frequency domain spectroscopy (FDS). To obtain MCP value, from a new perspective of parametric study on the circuital DR equivalence of OIP insulation-extended Debye model (EDM), the present contribution introduces a novel approach by exploring the pattern when EDM parameters vary with MCP and temperatures T. Further, mathematical correlations are developed between sensitive R-C values and MCP-T values. On the above analysis, small-scale physical models of real-life transformer bushings were prepared as test samples. Their OIP condenser bodies were artificially absorbed different controlled quantities of moisture and conditioned at different temperatures to record corresponding FDS results. Then a hybrid genetic algorithm combined with the Levenberg-Marquardt algorithm was proposed to estimate the EDM parameters by fitting the measured FDS data. Sensitive parameters therein were identified using subspectrum decomposition and formulated with MCP-T values so that a reliable moisture content estimation can be achieved once the testing temperature and FDS recordings of an insulation body are known.

A Circuital Model-Based Analysis of Moisture Content in Oil-Impregnated-Paper Insulation Using Frequency Domain Spectroscopy