Research and implementation of fault data recovery method for dry-type transformer temperature control sensor based on ISSA-LSTM algorithm

Abstract

This study proposes a temperature control sensor fault data recovery model based on the improved Sparrow Search Algorithm (ISSA) optimizing Long Short-Term Memory (LSTM) to address erroneous data output caused by temperature monitoring sensor failures in dry-type transformers. The model aims to recover faulty sensor data in dynamic processes. It enhances the algorithm by initializing the population using Tent chaotic mapping, employing t-distribution and differential variation perturbations, and introducing a dynamic step size factor. Simulation experiments evaluated the performance of ISSA-LSTM, SSA-LSTM, PSO-LSTM, GA-LSTM, and ACSA-LSTM algorithms. The ISSA-LSTM model outperforms the worst PSO-LSTM model by 82.6%, 82.1%, and 1.8% in terms of MAE, RMSE and R2 values, respectively. Field experiments confirmed the algorithm's effectiveness in recovering data from faulty sensors and different fault types, improving the accuracy and stability of temperature control sensor fault data recovery in dry-type transformers.