Current Transformer Saturation Compensation Based on Deep Learning Approach

摘要

Current Transformer (CT) saturation is regarded as one of the major problems in power system field due to the reason that it negatively impacts the operation of relays, resulting in malfunction protective devices. Recently, deep learning methods have been commonly implemented in most academic fields as the reason of significant generated results. This paper presents a compensation method for saturated waveform by applying deep learning to the aforementioned problem. To achieve a good network structure, pre-training and fine-tuning mechanism have been implemented because it shows a great performance as it well initializes the optimal weight in the pre-training stage. Finally, a training model is evaluated by the newly-introduced conditions, in which has never been experienced during the training stage.