Transformer Fault Diagnosis Based on Immune Antibody Memory Classification Algorithm

In order to make up the shortages of traditional cloning immune algorithm that it studies slowly when being applied in transformer fault diagnosis,has no classification ability and antibody space is small,this paper proposes a classification algorithm of antibody memory based on the principle that the antibodies recognize the antigens in the immune space and antibody memory function.By combining the clonal selection and the evolutionary algorithm,and learns the training antigen and subsidiary type information to build the fault information database which is composed of different types of detection aggregate.The learning speed of this algorithm is very fast while the affinity is high enough.At the same time,it has an expanded immunization search space and uses the linear relationship between the Artificial Recognition Ball(ARB) and the stimulation level for rapid diagnosis of the fault types.Experimental results show that the algorithm can improve the speed of diagnosis and the accuracy of fault diagnosis.