Big data analysis method based on time series temperature field data of substation transformer

Abstract

Transformers are one of the main electrical equipment in the power system, and their operating status directly determines the safety, stability, and reliability of the power supply in the power system. The temperature or temperature rise of a transformer is the most important technical parameter that describes the operating status (accidents, faults, and abnormal operation) of the transformer. The following mainly introduces that the monitoring area is gridded by modeling the three-dimensional reality of the transformer, and then the collected infrared temperature data is accurately mapped into the monitoring grid in three dimensions to form the patrol time sequence temperature big data. Through trend analysis, temperature difference and temperature rise analysis, temperature field gradient analysis and other models, the equipment temperature hidden danger detection focusing on prevention is realized.