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In order to obtain the characteristic parameters of dampness status of resin impregnated paper (RIP),the time domain dielectric response technique is performed on the dampness status evaluation,and the RIP moisture contents are intensively studied based on polarization and depolarization current (PDC) method. Firstly,RIP insulation samples with different moisture contents are prepared in the laboratory and the PDC curves are measured. Then,the optimization algorithm combining genetic algorithm and interior point method is introduced,and the parameters of extended Debye model are obtained by Matlab simulation. Finally,two characteristic parameters of dampness status are extracted,which are the relaxation contribution coefficient of the maximum time constant branch A 9 and the stable depolarization charge quantity Q d-5000. Conclusions from the research results are as follows. The PDC curve moves upward as the moisture content increases. The dampness of the RIP insulation results in the increase of its conductivity and the decrease of its insulation resistance,absorption ratio andpolarization index. The parameters of the 9-branch extended Debye model are sensitive to the RIP insulation dampness status,and the A 9 shows a linear relationship with moisture content. The depolarization charge quantity is sensitive to moisture content,and the Q d-5000 shows an exponential relationship with moisture content.

Researchers have been exploring dielectric testing techniques both in time and frequency domain for insulation condition assessment of oil-paper insulated transformers. In a practical dielectric system, dipoles are found to behave according to a distribution of elementary Debye relaxation properties. Suitable distribution density functions have been proposed to characterise such many-body interaction processes. Cole–Cole diagrams can be one of the methods for studying the nature of frequency dependency of dielectric materials of complex structure. Cole–Cole plots are commonly used for characterising different materials such as dielectric mixtures, ionic liquids, cable insulating oil, polar liquids etc. The scope of its application for assessing transformer oil-paper insulation considering distributed relaxation process has not been explored yet. The present contribution discusses mathematical formulations used for transforming the experimentally obtained time domain dielectric response test data to distribution domain and further to frequency domain for obtaining the Cole–Cole plots. Findings about the influence of various operating conditions and insulation status on the Cole–Cole diagram have been reported in this contribution. Results of tests on field transformers are also presented. This paper attempts to employ the features of Cole–Cole diagrams as potential indicators for analysing condition of the oil-paper insulation considering distributed relaxation process.

Polarisation current profile varies non‐linearly with a magnitude of charging voltage. This implies performance parameters obtained from polarisation–depolarisation current and those measured using specialised equipment cannot always be compared. Results reported in this study suggested that the non‐linearity in insulation response can be explained in terms of the interaction between space charge and insulation conductivity. The influence of using different charging voltages and its effect on performance parameter calculation is investigated in this study with the help of laboratory samples. It is shown in this study that with the help of a trained artificial neural network, it is possible to identify the charging voltage at which insulation response exhibits minimum non‐linearity. To test the accuracy of the proposed method performance parameters measured using commercial equipment are compared with those calculated from the polarisation current profile. [ABSTRACT FROM AUTHOR]

Induction period evaluated from time domain dielectric response of oil‐paper insulation (OPI) contains information related to heat dissipating property of oil. In the present paper, effect of aging sensitive oil properties on the value of induction period is investigated. Three types of dielectric liquids (pure mineral oil, synthetic ester, mixture of synthetic ester, and mineral oil) are considered for the present work. A neural network (NN) is used for modelling the non‐linear relationship between induction period and various oil properties. The trained network is subsequently used to study the influence of various oil properties on induction period. [ABSTRACT FROM AUTHOR]