Combination Prediction Method of Power Transformers Based on Feature Gas Arrangement Diagram and Grey Model

The reliability of the power system depends directly on the operating state of the transformer. As equipment manufacturing processes and maintenance levels are continuously improved, the number of machine downtimes caused by faults is significantly reduced. Transformer faults are dominated by overheating faults and internal discharges, and energy is released at the same time as the fault occurs. The insulation material of the transformer is decomposed and cracked in the above process. Alkanes are produced and dissolved in transformer oil. A large amount of data indicates that there is a correlation between the condition of the equipment and the dissolved gas content in the oil. The time sequences data of oil chromatography shows small-scale fluctuations in the local area. In order to reduce the impact of data fluctuation on the prediction model, this paper proposes a combination prediction method of power transformers based on the feature gas arrangement diagram and grey model. Compared with the traditional grey correlation analysis, this model can solve the model divergence caused by data fluctuations. In addition, a method based on the feature gas arrangement diagram is proposed, which corrects the deviation of the prediction result based on the actual operation state of the equipment. This method improves the efficiency of the genetic algorithm and allows the model to obtain more accurate predictions. In the actual case analysis, the model proposed in this paper obtains a good application effect, especially for small sample prediction nroblems with high accuracy.