The actuation time of UHV circuit breakers has a great dispersion

many influencing factors are not conducive to the realization of circuit breaker phase selection and big data technology has high efficiency in data processing. The principle of the disc spring hydraulic operating mechanisms is analyzed and a lot of experimental data about the 1 100 kV miniature GIS circuit breaker are collected. The offline distributed processing platform containing three major functions of data acquisition

data analysis and data processing is designed and built by Hadoop software. By using eclipse to program on the platform

the multivariate linear analysis method is realized and the forecasting actuation time of breaker mathematical model based on control voltage and oil pressure is obtained and the model is further optimized. It is shown that the action time of breaker can be effectively predicted by initial influencing factors under the condition of high efficiency and high reliability of the platform. From the analysis of the prediction results

the error rate in the optimized model is significantly reduced than the previous one and the corresponding error range within ±0.8 ms

which verifies the effectiveness of the model. This paper provides some reference value for the grid big data analysis and the stable operation of power transmission and transformation equipment.