As a component of a power system, the special transformer has a very important impact on the stability and safety. Given the volatility and heterogeneity of real-time data of special transformers, a standardization method based on limit intervals is proposed. Considering the dynamic aggregation and limit interval of the real-time data of the special transformer, this method can provide complete information for quantitative evaluation. To clearly quantify the influence of specific electrical operation parameters on their own health, we combine expert experience evaluation with a neural network to propose a quantitative evaluation method based on limit interval and improved Self-Organizing Map-Principal Component Analysis (PCA-SOM) electrical operation parameters. Based on the SOM neural network, this method uses the contribution rate of each principal component in PCA to modify the objective function, and combines the artificial evaluation of each neuron to calculate a real-time evaluation of the health status of the dedicated transformer. Finally, the experiment uses real-time data from a special transformer on the “Zhedian Cloud” big data system. The results show that the method in this paper has higher accuracy than other methods and can better reflect the real-time health status of the special transformer. This work is supported by the National Key Research and Development Program of China (No. 2017YFA0700300) and the Key Research and Development Program of Zhejiang Province (No. 2021C01112).