Error Analysis of Air-Core Coil Current Transformer Based on Stacking Model Fusion

Air-core coil current transformer is a key piece of equipment in the digital substation development. However, it more vulnerable to various faults when compared with traditional electromagnetic transformer. Aiming at understanding effect parameters on performance air-core transformer, this paper investigates influence these factors using maximum information coefficient. The interference mechanism influencing error also analyzed. Finally, Stacking model fusion algorithm used predict errors. developed base consists deep learning, integrated learning and algorithms. Compared gated recurrent units extreme gradient boosting algorithms, prediction based stacking proposed features higher accuracy reliability which helps improve safety future substations.