A Fault Diagnosis Model of Power Transformers Based on Dissolved Gas Analysis Features Selection and Improved Krill Herd Algorithm Optimized Support Vector Machine

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.