Research on Monitoring Data Redundancy of 500 kV Power Grid Based on Big Data Technology
With the increasing amount of data in the power grid monitoring system,data redundancy has become a key challenge affecting the stability of the power grid.Through in-depth analysis of the monitoring data characteristics of the 500 kV high-voltage power grid,an autoencoder network structure was designed and implemented to effectively learn the compressed representation of data,thereby optimizing the data storage and processing flow.In addition,an efficient model lightweighting strategy has been proposed to accelerate the inference speed of the model and adapt to the resource constraints of end devices.The results indicate that the proposed method has achieved significant results in reducing data storage requirements and improving data processing efficiency,while maintaining the integrity of monitoring data and the system's rapid response capability.
万方数据
维普
