Application of RPROP ANN based fault diagnosis model for transformer dissolved gas-in-oil analysis

Fault diagnosis model is one of the core algorithms in transmission and transformation equipment. An artificial neural network (ANN) programming based on RPROP (resilient propagation) algorithm is presented for the dissolved gas-in-oil analysis (DGA) methodologies. The comparative analysis shows RPROP algorithm provides both higher learning efficiency and stronger generalization capacity versus standard BP, Bold Driver and SuperSAB algorithms once used in DGA. When RPROP is applied to the transformer DGA, the fault diagnosis accuracy is enhanced compared to other conventional methods. Therefore, it shows a promising future in the diagnostic field for power transformation equipment.