Research of the FGA-ANN method for transformer fault diagnosis based on the dissolved gas analysis

Dissolved gas analysis has been used as a diagnostic method to determine the conditions of transformers for a long time. The criteria used in dissolved gas analysis are based on crisp value norms. Due to the dichotomous nature of crisp criteria, transformers with similar gas-in-oil conditions may lead to very different conclusions of diagnosis especially when the gas concentrations are around the crisp norms. To deal with this problem, gas-in-oil data of failed transformers were collected and treated in order to obtain the membership functions of fault patterns using a grey relational analysis method. All crisp norms were transformed into mapping rules. In this paper, the novel method of fuzzy genetic algorithm-artificial neural networks (FGA-ANN) was applied to transformer fault diagnosis instead of the ratio method. The novel method combined GA and ANN, during genetic algorithm's optimized, crossover rate and mutation rate were adjusted dynamically by fuzzy control. The treated data of the model samples were operated by FGA-ANN and a group of weighs and biases were found. Finally examples were given. Compared to the other traditional method, the results have demonstrated the robustness of the method.