To address the problems posed by redundant features in transformer fault recognition and the low accuracy of traditional methods, a transformer fault recognition method leveraging kernel principal component analysis (KPCA) in conjunction with chaotic sparrow search algorithm (CGSSA) is introduced. Initially, KPCA is employed to preprocess the transformer fault data, aiming to mitigate the correlations among features. Subsequently, CGSSA is improved by incorporating the improved Tent map and Gaussian mutation to increase the search accuracy and convergence speed of the algorithm. Comparing the results involving CGSSA, SSA, GWO and WOA. Utilizing the data extracted through the KPCA as the model input, CGSSA is then used to select the kernel function parameters and regularization coefficient of KELM, thereby establishing the KPCA-CGSSA-KELM transformer fault recognition model. The experimental results demonstrate that, with the identical input data, CGSSA has the best results in terms of convergence speed and optimization accuracy. In addition, the proposed method shows the fault recognition accuracy of 95.7%, which is 18.6%, 10%, and 15.7% higher than WOA-KELM, GWO-KELM, and SSA-KELM, respectively. These findings suggest that the proposed method effectively manages the impact of redundant features and enhances the precision of transformer fault recognition, thus verifying the validity and feasibility of the proposed method for transformer fault recognition under the feature redundancy.