**2. Framework of the Feature Selection Method Based on the SVM Model for Main Transformers**

The research framework for the feature selection method based on the architecture of the SVM model for the main transformers is shown in Figure 1.

**Figure 1.** Framework of Feature Selection method based on SVM model for main transformers.

The gas concentration values measured in the continuous operation process of nuclear power transformer are obtained, and there is almost no-fault data.

The features used in various power transformer condition diagnosis methods based on dissolved gas in oil are extensively studied. On this basis, the initial feature set is formed.

The unsupervised mutual information feature extraction algorithm is adopted to extract features, and the set of sequence features is obtained according to the weight coefficient from high to low.

In the feature set, different number of feature sets are selected sequentially and verified by optimized SVM model for transformer fault diagnosis.

In order to reduce the contingency of the experiment, the 5-fold verification method is used to process the training samples and test samples to verify the validity of the selection feature in the diagnosis of the nuclear power transformer condition diagnosis.

Based on the accuracy of diagnosis, the feasibility of different feature extraction algorithms in the condition diagnosis of main transformers is analyzed.
