Study On Fault Diagnosis About Power Transformers Based On The Artificial Immune System

A large electric power transformer is one of the key apparatus in the electric power system. Faults of a power transformer may have a great effect on stability of the power system. Therefore, there is great academic and engineering significance to do an earlier research on fault diagnosis technology of power transformers. To investigate this problem, this paper thoroughly studies the similarity measures, cluster analysis and artificial immune system (AIS), and achieves some breakthroughs on the theory of the similarity estimation and AIS. The proposed methods in this paper have been tested on some benchmark data sets from the UCI repository, and robust results are obtained. The innovative achievements are concluded as follows:1. Two kinds of distance measures to similarity estimation are proposed. The relation between the characteristic of differences and shape similarity is discussed, the Vector Shape Parameter (VSP ) is defined, merging the classical Euclidean distance, two new measures based on the analysis of the differences between vectors are presented, named as the Shape Similarity Distance (SSD) and the Morphology Similarity Distance (MSD) respectively. The FCM clustering results on many rand datasets show the new method can estimates similarity on both the size and shape of objects. A lot of classification and clustering results on some benchmarked datasets from the UCI repository and a real dataset conclude that, the presented method is one kind of similarity estimation measure which can achieve higher accuracy than the classical methods.2. One kind of antibody generation algorithm is proposed. In the biological immune system, antibody (Ab) has ultra-high-speed variation capacity with significant different mechanism of genetic system. From this point of view, the antibody generation algorithm does not use random search and optimization strategies like most of artificial immune systems used, but learn and memory the characters of antigen with three different strategies according to different situation: antibody evolution, antibody combination and antibody production. This method greatly enhances the efficiency of this algorithm.3. A self-organization, self-learning and self-memory named self-organization Antibody net (soAbNet) is suggested. The soAbNet is inspired by reinforcement learning and immune memory ability of antibody to antigen. It is easy to calculate, only need to define the number of initial antibodies, without any other parameters and thresholds. The concentration of antibody is designed, and it effectively improve memory capacity of antibody, and the data analysis ability of this model. The proposed approaches have been tested on a variety of benchmark dataset from the UCI repository. In all the experiments, this method demonstrates effective performance compared with other methods.4. Faults diagnosis of power transformers is discussed based on the antibody generation algorithm and soAbNet. The Dissolved gases analysis experimental results show that this method has higher accuracy, this provides a new approach to solve faults diagnosis of power transformers problem.