Frequency domain spectroscopy (FDS) is a nondestructive insulation test method that has been widely applied to the insulation evaluation of power transformers. However, a major shortcoming of conventional FDS is the long test duration. Existing studies on accelerated FDS tests suffer from a lack of accuracy and are difficult to apply to field testing. To tackle this problem, a novel FDS test method (named iFDS) is proposed to reduce the duration and improve the accuracy of the oil–paper insulation test. Specifically, the test duration is reduced first by signal superposition and waveform prediction. Then, the test accuracy is improved from the aspects of the parameters design of excitation, the optimization of the test process, and time–frequency-domain data processing. Finally, a test platform is built based on the above methods to implement iFDS testing. To verify the effectiveness of iFDS, the dielectric parameters (tan $\boldsymbol {\delta }$ and $C\ast$ ) of oil–paper insulation were measured using the iFDS platform and compared with that of the existing FDS methods in terms of duration and accuracy. Comparative experimental results show that iFDS is a superior method for saving time and accurate testing and has the potential to be versatile for evaluating more insulation objects.

Frequency-domain spectroscopy (FDS) is demonstrated to be affected by electrode polarization and conductance behavior in the low-frequency ranges, which causes the unreliable prediction results of transformer cellulose insulation. In order to solve this issue, a fingerprint database based on the dielectric modulus is reported to predict the degree of polymerization (DP) and moisture content of cellulose insulation. In the current work, the relevant fingerprints that characterize the insulation conditions are obtained by studying the dielectric modulus curves of cellulose insulation with various insulation conditions, as well as the DC conductivity of transformer oil. Then, the dielectric modulus fingerprint database is established in the lab, and the accuracy of the reported fingerprint database is later verified. As a potential tool, the dielectric modulus fingerprint database is tested by several samples, and the results demonstrate that the accuracy of this method is more than 80%. In that respect, an interesting discovery of this paper is that the dielectric modulus fingerprint database may be a helpful tool for conditions prediction of the transformer cellulose insulation system.