Analysis of Operating Regimes and THD Forecasting in Steelmaking Plant Power Systems Using Advanced Neural Architectures

This study offers a comprehensive study of power quality in industrial rolling mill grids, focusing on total harmonic distortion (THD) and its forecasting under different operational conditions. The research begins with a measurement-based evaluation of load variations and the effects of reactive power compensation using capacitor banks. To improve these results, forecasting algorithms were developed utilizing modern methods based on data capable of recognizing both short-term and long-term dependencies within the THD signal. The models were evaluated using three forecasting strategies: classical prediction on test data, autoregressive one-step forecasting, and direct multi-step forecasting. This was done using well-known error and correlation indices like RMSE, MAE, sMAPE, the coefficient of determination (R2), and the Pearson correlation coefficient (ρ). The results indicate that models incorporating both local feature extraction and temporal dynamics provide the most accurate forecasts. In particular, the hybrid convolutional-recurrent structure achieved the best overall performance, with R2 = 0.923 and ρ = 0.961 in classical prediction, and it was the only approach to maintain a positive R2 (0.285) in multi-step forecasting. These results demonstrate the usefulness of modern predictive modeling for Total Harmonic Distortion (THD) in industrial grids, combining conventional measurement-based techniques by offering relevant observations for power quality monitoring and control.