Fault diagnosis for power transformer based on the self-organization antibody net

Inspired by the highly efficient antibody-antigen recognition and memory mechanism of biological immune system, a self-organization antibody net (soAbNet) and the antibody generation algorithm are proposed and applied to fault diagnosis for power transformer. By the new definitions of antibody style and antibody density, the soAbNet works well with the initial number of antibodies, need not set any other artificial parameters and thresholds. According the antibody generation algorithm, antibodies learn and memory the characters of antigens effectively by three different strategies: antibody evolution, antibody combination and antibody production. Experimental result on Iris dataset from the UCI and diagnosis result on dissolved gas analysis data demonstrate that the proposed soAbNet can make full use of a priori information, it has effective classifying capability as well as higher precision.