Study On The Intelligent Diagnosis System For Transformer Fault Based On Dissolved Gases Analysis

Power transformer is one of the most important electrical equipments in the electric system. Its operating state attaches importance to system's safety directly. In addition, it is a significant issue for electrical department to find potential faults of the transformer so as to keep it operating safely. Therefore, the fault diagnosis technology is available and reliable to operate and maintain the transformer.Dissolved Gases Analysis (DGA) is an important method to diagnose the internal fault of transformer and it offers a main basis to find the general incipient faults of the transformer indirectly. Furthermore, it has been proved in practice that the DGA technology is very effective to find the potential faults and its developing trend in the transformer. Thus, Dissolved Gases in oil is used as character of the fault diagnosis.Because fault symptom space and fault space have complicated non-linear relations, the mathematical model of diagnosis system is difficult to build up. However, artificial neural networks (ANN) provides a new way for this problem due to its advantages such as parallel processing, self-adaptation, self-study, association memory, non-linear mapping, etc. Thereby, ANN is applied to fault diagnosis system in the paper.Basic theories of fault diagnosis and ANN are introduced in the beginning of the paper. Then, some measures are taken to improve the BP algorithm which possesses great efficiency and correctness. Combining ANN with Dissolved Gas Analysis (DGA) is a typical method and the transformer fault intelligent diagnosis system is of friendly interface and excellent capability using LabVIEW.In the end, the paper summarizes the excellent capability of ANN fault diagnosis system and its shortcomings, and then analyses the outlook and development trend of fault intelligent diagnosis systems in the future.