Transformer Fault Diagnosis Using Improved Artificial Fish Swarm with Rough Set Algorithm
Bo Fu · Gao dianya jishu · 2012
Facing a large number of incomplete fault data,the traditional artificial intelligence methods cannot effectively and timely analyze or accurately diagnosed because of the ill-conditioned problem caused by inefficient discretization approaches.We presented a method based on rough set theory integrated with improved artificial fish swarm algorithm(AFSA) for fault diagnosis of transformer.Firstly,the values of dissolved gas analysis(DGA) in oil were taken as conditional attributes and the type faults were taken as decision attributes.Various relations between fault and symptom were connected,and decision table was established.Then,the improved artificial fish swarm algorithm was used to discrete continuous attribute,and the rough set theory was used to reduce the decision table.Finally,the simplified decision rules were got,which greatly simplified the difficulty of diagnosis.The experimental results indicate that the method increases the diagnosis accuracy compared with the traditional algorithm.