Application of artificial neural networks for saturation correction in current and voltage transformers

Abstract

The article investigates the application of artificial neural networks for saturation correction in current and voltage transformers. Under saturation conditions, these transformers can distort signals, leading to the incorrect operation of measuring and protection devices. The use of artificial neural networks allows increasing accuracy in signal processing, thereby improving the reliability and safety of electric power systems. The paper describes methods for training neural networks using historical data, modeling transformer operation under various conditions, and developing algorithms for correcting distortions caused by saturation.