M. Shpilievyi

COMPARATIVE ANALYSIS OF NEURAL NETWORK ARCHITECTURES FOR REACTIVE POWER FORECASTING IN AC TRACTION POWER SUPPLY SYSTEM

These results validate the effectiveness of recurrent neural networks, particularly LSTM, for modeling complex processes in traction networks and provide a reliable algorithmic basis for implementing predictive real-time intelligent control of compensating devices, such as FACTS and UPQC systems.