The Study Of Power Transformer Fault Diagnosis Based On Artificial Neural Network

With the technical level of modern facility improves continually, the fault probability increases greatly. Power transformer has a very significant influence to power system, enterprise's production and people's life. How to forecast transformer's fault ahead and find the fault reason quickly after the fault is a good way to increase work efficiency and lighten the economy losing.In the paper ANN is applied to fault diagnosis system to overcome shortcomings of traditional fault diagnosis and reduce error brought by single method. Furthermore because fault symptom space and fault space have complicated non-linear relations, the mathematical model of diagnosis system is difficult to get. Artificial neural networks (ANN) proposes a new way for this problem because of its such advantages as parallel processing, self-adaptation, self-study, association memory, non-linear mapping, etc. Thereby ANN is used as fault-classifying implement in exploit ting the system.In the beginning of the paper, basic theories of fault diagnosis and ANN are presented. BP algorithm is performed by computer program. Some improving is done to the algorithm. Adding momentum item while correcting weight and limiting range of input value reduce error and improve diagnosis correctness greatly. While normalizing the input value, a new way is put forward that normalization is performed item by item according to its sort. In this way error training can avoid going into the flat field that is caused by existing of 0 or 1 of the input value. This way can improve the efficiency and correctness greatly. Combining ANN with Dissolved Gas Analysis (DGA) that is a typical method in transformer diagnosis, transformer fault diagnosis system with friendly interface and excellent capability is finished by Java. Besides, the method of selecting networks parameter is discussed detailedly, and the influence of different parameters on diagnosis results is analyzed. Finally, the paper summarizes the excellent capability of ANN fault diagnosis system and its shortcomings, and then analyses the outlook and development direction of ANN in fault diagnosis systems in the future.