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Properties of oil and paper in a transformer degrade primarily due to thermal ageing and moisture ingress. Dielectric diagnostic tests, such as Recovery Voltage (RV), and Polarizations and Depolarization Current (PDC) measurement are currently being explored as potential tools for insulation condition assessment. A modern chemical analysis tool for paper molecular weight (MW) measurement, Gel Permeation Chromatography (GPC) or the more accurately described Size Exclusion Chromatography (SEC) promises to be useful in assessing ageing condition. However, the issue of separately assessing the impacts of ageing and moisture on oil and paper has been a key issue for many years. In the current research project, a series of experiments have been performed under controlled laboratory conditions with preset moisture content, and at controlled high temperature ageing. Whereas RV and PDC measurement results were found to be more sensitive to the moisture content of the oil and paper insulation, the MW distribution measurement by SEC provided a trend of insulation thermal ageing. This paper first provides a brief description of RVM, PDC and SEC procedures followed by a description of the experimental techniques adopted. Results are then analysed with the view of separately understanding the impacts of thermal ageing and moisture on the condition of oil and paper insulation in a transformer.

The need for economic, reliable, and effective delivery of electric power has lead to the search for fast, efficient, and effective methods for diagnosing the insulation of high-voltage (HV) equipment in the power industries. The recent dielectric techniques that have been carefully considered by major industries for transformer insulation condition assessment are the recovery voltage method (RVM) and the polarization and depolarization current (PDC) measurement. However, due to the complexity of the transformer insulation structure and various degradation mechanisms under multiple stresses, insulation condition assessment is usually performed by experts with special knowledge and experience. In this paper, an expert system (ES) is developed, which imitates the performance of a human expert, to make the complicated insulation condition assessment procedure accessible to plant maintenance engineers. The structure of the ES is described in detail including knowledge base, inference engine, and human-computer interface. Examples of the application of the ES are also presented to confirm that the system can provide accurate insulation diagnosis.