Estimation of Primary Current of DC Bias Current Transformer in Power System Based on LSTM

In high-voltage transmission systems, current transformers (CTs) are susceptible to core saturation when subjected to DC bias, which leads to severe distortion of the secondary output current, affecting protection and metering accuracy. In order to solve this problem, this paper proposes a CT distortion current inverse derivation method based on long short-term memory (LSTM) neural network. The method takes the secondary current (referred to the primary side) as input, models its temporal structure with an LSTM, and outputs the corresponding estimate of the true primary current. First, the simulation model considering DC bias effect is built in PSCAD to generate training samples; then, the LSTM network structure is designed, and finally, the training samples are trained and the inverse derivation results are generated. Simulation results demonstrate accurate recovery of the primary current across saturation levels and DC-bias magnitudes, reducing RMSE by 66.29% and 79.54%, and MAE by 44.12% and 72.06%, relative to an SVM and a shallow ANN, respectively, indicating strong generalization and robustness.