Research on Heavy Overload Identification of Distribution Transformer Based on Hybrid Sampling and Random Forest

Affected by special periods such as holidays, people returning home, seasons, and distribution network planning and operation factors, distribution transformers are prone to heavy overload. The existence of heavy overload will not only damage the life of the transformer, but also cause the equipment to burn out, affecting the stability and reliability of power supply. However, in the actual collection process, the number of heavy overloads of distribution transformers is relatively small, resulting in the inability of the classifier to effectively identify abnormal samples. Therefore, a distribution transformer heavy overload identification method based on mixed sampling and random forest is proposed. Clustering and stratified under-sampling are performed on normal samples, and SMOTETomek sampling is performed on heavy overload samples to narrow the sample distribution gap. The sampled data set is then used to train the random forest model. Experimental results show that the proposed sampling method can improve the performance of the classifier to a certain extent.