Current Transformer Saturation Compensation Based on Autoencoder and Deep Learning

摘要

Current transformer saturation is a key issue for power systems because it negatively affects the operation of relays, resulting in the malfunction of protection devices. Recently, deep learning has been used in many academic fields with promising results. Here, we present a technique to compensate for saturated waveforms using deep learning to reconstruct an undistorted waveform. The optimal structure was obtained using pre- and fine-tuning mechanisms, which yielded good performance and initialized the optimum weight during the pre-training stage. The deep learning parameters were determined using particle swarm optimization prior to training. Finally, deep learning performance was evaluated using newly introduced conditions that were not observed during the training stage.