Abstract
Power transformers are vital to the power grid and discovering the latent faults in advance is helpful for avoiding serious problems. This study addressed the problem of forecasting and diagnosing the faults of power transformers with small dissolved gas analysis (DGA) data samples that arise from faults in transformers with low occurrence rates. First, an online monitor that was developed in our previous work was applied to obtain the DGA data. Second, the ensemble learning (EL) of a bagging algorithm with bootstrap resampling was used to deal with small training samples. Finally, a criss-cross-optimized neural network (i.e., CSO-NN) was applied to the short-term prediction of the DGA data, based on which the transformer status could be forecasted. The case studies showed that the proposed EL-CSO-NN algorithm integrated into the monitor was capable of achieving satisfactory classification and prediction accuracy for transformer fault forecasting.