Evaluation of Distribution Transformer Operation Status under Big Data Analysis Technology

Traditional transformer operation status evaluation methods rely on manual data collection, which requires a lot of manpower and time for data collection and recording, and the inspection and evaluation cycle is long. To address the above issues, this article proposed an evaluation method based on big data analysis technology. After collecting a large amount of operation data of distribution transformers, they were stored in a data warehouse for management and processing. Subsequently, machine learning and deep learning algorithms were used to analyze and model the data to extract key features and predict the operating status of the transformer. At the same time, cross validation and evaluation indicators were used to evaluate the performance of the model, and parameter tuning and feature selection were carried out to improve the accuracy of the model. The research conclusion indicates that the error of this method was between 0.01 and 0.05. The distribution transformer operation status evaluation model based on big data analysis technology could accurately identify and predict normal operation status and fault status. A model with a high recall rate could detect potential problems in a timely manner and provide accurate warnings, thereby reducing the risk of malfunctions and maintenance costs. The smaller average absolute error indicated that the prediction accuracy of the model was significantly improved, providing more reliable decision support for operation and maintenance personnel.