An Efficient Transformer Condition Monitoring Method Based on LSTM and Dynamic Correction Mechanism

Distribution transformers are important equipment in the power grid, which are widely distributed in the power grid. The monitoring of distribution transformers plays a key role in the efficient management of the distribution network. Sensing and collecting equipment status at a higher frequency is conducive to timely detection and handling of equipment anomalies. This study introduces a novel predictive modeling method to enhance the monitoring capabilities of transformers that only support low-frequency data acquisition systems in the early stage by leveraging high-frequency data from post-assembly a subset of post-assembly transformers. By combining machine learning techniques with error correction and Kalman filter enhancement, our method effectively converts sparse low-frequency measurements into detailed near-real-time insights. Simulation results show that our method effectively improves the monitoring quality of transformers that only support low-frequency sensing capabilities while provides a scalable and cost-effective solution to upgrade the monitoring capabilities of existing power infrastructure without centralized hardware updates, thereby achieving smarter and more proactive operation management.