Magnetostriction of Silicon Steel Sheets and Its Application in Predicting DC Bias

DC bias is a primary cause of anomalous vibration and noise in power transformers. This study investigates the magnetostriction characteristics of grain-oriented silicon steel sheets under simultaneous AC excitation and DC bias. A novel prediction method is proposed, which integrates multi-scale mutual information features with frequency-domain features, and employs a long short-term memory (LSTM) network for DC bias identification. The experimental platform with six voltage levels and seven bias ratios was set up to collect strain signals under various operating conditions. The results indicate that DC bias alters the magnetostriction spectrum by modulating the nonlinear response. Specifically, the amplitude of the 100 Hz harmonic decreases monotonically as bias increases, whereas the high-frequency harmonics are noticeably amplified, leading to greater waveform asymmetry and harmonic distortion. The proposed prediction model achieves a root-mean-square error (RMSE) of 0.0336 and a coefficient of determination (R2) of 0.8810 under stratified 5-fold cross-validation, offering theoretical support and experimental evidence for DC bias monitoring and transformer condition assessment.