A dissolved gas analysis-based failure prediction model for power transformers using artificial neural networks - Pati, Eduardo M. (2004)
This paper presents a three-step artificial neural network (ANN) approach to predict power transformer failures based on dissolved gas-in-oil analysis (DGA). The purpose is to use ANN-based detectors to initially classifiy the condition of a power transformer. Once the abnormal condition is established, another sets of ANN detectors are then employed to determine the type of fault and possible involvement of cellulose. All of these can be accomplished accurately in a short period of time and solve the problem on over reliance of human experts to evaluate the condition of a power transformer using conventional DGA methods. The developed ANN model was tested on various cases of power transformers. The results indicate that compared to conventional DGA methods, the developed ANN model has been able to accurately identify the true condition of the power transformers in a short period of time.