A Fault Diagnosis Method for Power Transformers Based on Wavelet Neural Network and D-S Evidence Theory

Transformer faults are quite complicated phenomena and can occur due to a variety of reasons. There have been several methods for transformer fault synthetic diagnosis, but each of them has its own limitations in real fault diagnosis applications. In order to overcome those shortcomings in the existing methods, a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm (AGA) and an improved D-S evidence theory fusion technique is proposed in this paper. The proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault diagnosis. Based on the fusion mechanism of D-S evidence theory, the comprehensive reliability of evidence is constructed by considering the evidence importance, the outputs of the neural network and the expert experience. The new method increases the objectivity of the basic probability assignment (BPA) and reduces the basic probability assigned for uncertain and unimportant information. The case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers.