DC Bias Phenomenon Recognition Based on Supervised Learning Model

dc bias phenomenon refers to the abnormal operation state of transformers whose vibration and noise signals increase due to the high voltage direct current (HVDC) transmission lines operating in ground return mode or the geomagnetically induced current (GIC). Shanghai Power Grid have built transformers' neutral point dc, vibration and noise monitoring platform since 2015. In this paper, Logistics Regression is firstly selected as a supervised learning model to judge whether the transformer has dc bias phenomenon based on the acquiring data. Two characteristic indexes representing dc bias phenomenon are found to improves the efficiency of model building. Simultaneously, appropriate regularization coefficients are chosen according to the evaluation effect and the physical meaning of model parameters. Considering most of data are unlabeled owing to the enormous effort of tagging data, a semi-supervised learning method is used to utilize the unlabeled data. Finally, an estimation model is established which calculates a probability value to indicate the risk of transformers' de bias phenomenon quantitatively. This model can remind operation and maintenance personnel strengthen devices inspection, and improves the efficiency of equipment management.