Transformer fault diagnosis based on improved fuzzy ISODATA algorithm

Dissolved Gas Analysis (DGA) has already gained its popularity in fault diagnosis for the oil-immersed transformers. However, owing to the fuzziness and uncertainty between the failure phenomena and failure mechanisms, power equipment failure reasons are very complicated and the accuracy of existing algorithms is low. Diagnostic methods based on artificial intelligence are commonly introduced in the field above. Because of the complexity of the network, the speed of the algorithm convergence is badly affected. With the limitation of artificial guidance and expertise, the current algorithms are short of the self-learning ability. That is why there is no common diagnostic program can be formed. Based on this reason, the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) based on DGA is proposed. First, the feasibility of transformer fault diagnosis method based on the ISODATA and DGA are analyzed, as well as its limitations. In order to improve the efficiency of the algorithm, a genetic algorithm is introduced to optimize the transformer fault diagnosis model by reducing the dependence of the initial clustering for ISODATA based on DGA. Through these methods, the accuracy and efficiency of optimizing diagnosis are improved. With the analysis of the principles of fuzzy ISODATA algorithm and genetic algorithm, and the optimization the initial cluster centers on ISODATA algorithm, the feasibility of the optimized transformer fault diagnosis program is proved. Finally, a specific case is programmed and compared to prove its accuracy and efficiency by analysis and comparing indicators before and after improvement. It is shown in the experimental comparison that the number of iterations is less after the improvement with the same precision and the operating speed is faster with the less error. The results showed that the fuzzy ISODATA algorithm optimized by the genetic algorithms is more in line with actual needs by largely overcoming the dependence on initial cluster center and can be easily applied to oil-immersed transformer fault diagnosis.