Fault Attribute Reduction of Oil Immersed Transformer Based on Improved Imperialist Competitive Algorithm

The original fault data of oil immersed transformer often contains a large number of unnecessary attributes, which greatly increases the elapsed time of the algorithm and reduces the classification accuracy, leading to the rise of the diagnosis error rate. Therefore, in order to obtain high quality oil immersed transformer fault attribute data sets, an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction. The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms. Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25% and a reduction accuracy of 98%. By using BP neural network to classify the reduction results, the accuracy was 86.25%, and the overall effect was better than those of the original data and other algorithms. Hence, the proposed method is effective for fault attribute reduction of oil immersed transformer.