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In this paper, a set of dissolved gas analysis (DGA) new feature combinations is selected as input from the mixed DGA feature quantity, and an improved krill herd (IKH) algorithm optimized support vector machine (SVM) transformer fault diagnosis model is established to solve the problem that the single characteristic gas or characteristic gas ratio, which are utilized as the DGA feature quantity cannot fully reflect the transformer fault classification. The following work has been done in this paper: 1) IEC TC 10 fault data and other 117 sets of fault data in China are preprocessed in order to reduce the influence on the diagnosis results causing by the edge data in the fuzzy area; 2) the SVM parameters and 11 features are encoded by a binary code technique; 3) a preferred DGA feature set for fault diagnosis of power transformers is selected by genetic algorithm (GA) and SVM, and; 4) IKH is utilized to optimize the parameters of SVM. Combining with cross-validation principle, a transformer fault diagnosis model based on IKH algorithm to optimize SVM is established. The fault diagnosis results based on the new fault sample show that the proposed DGA feature set to increase the accuracy by 26.78% and 10.83% over the DGA full data and IEC ratios. Moreover, the accuracy of IKHSVM is better than the GASVM, back-propagation neural network (BPNN), and particle swarm optimization optimized support vector machine (PSOSVM), the accuracy rates are 85.71%, 75%, 64.29%, and 71.43%, which proves the validity of the proposed fault diagnosis model. INDEX TERMS Power transformers, fault diagnosis, support vector machine, improved krill herd algorithm, DGA feature.

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Polarization-depolarization current (PDC) measurements are now being used as a diagnosis tool to predict the ageing condition of transformer oil-paper insulation. Unfortunately, it is somewhat difficult to obtain the ageing condition of transformer cellulose insulation using the PDC technique due to the variation in transformer insulation geometry. In this literature, to quantify the ageing condition of transformer cellulose insulation using the PDC technique, we firstly designed a series of experiments under controlled laboratory conditions, and then obtained the branch parameters of an extended Debye model using the technique of curve fitting the PDC data. Finally, the ageing effect and water effect on the parameters of large time constant branches were systematically investigated. In the present paper, it is observed that there is a good exponential correlation between large time constants and degree of polymerization (DP). Therefore, the authors believe that the large time constants may be regard as a sensitive ageing indicator and the nice correlations might be utilized for the quantitative assessment of ageing condition in transformer cellulose insulation in the future due to the geometry independence of large time constants. In addition, it is found that the water in cellulose pressboards has a predominant effect on large time constants.

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