Optimizing power system stability: An artificial neural network approach for controlling variable frequency transformer

Abstract

SummaryModern power systems face challenges with stability, especially due to interconnections through weak tie‐lines, which can lead to power oscillations. These oscillations occur when the rotor fails to provide sufficient damping force to balance electrical output and mechanical input. Without a reliable damping system, these oscillations can persist, posing a threat to power system stability. In this article, a novel solution is proposed to curb low‐frequency oscillations by implementing a new power transmission technology called variable frequency transformer (VFT) in a two‐area power system. The VFT is equipped with an industrially applied proportional integral derivative controller trained by an artificial neural network to regulate power flow between the two areas. Additionally, a power oscillation damping controller is introduced that utilizes intelligent tuning through salp swarm optimization to enhance the damping capabilities of the VFT. The intelligent tuning of the power oscillation damping controller using salp swarm optimization enhances the damping capabilities of the VFT, resulting in a 25% increase in the damping ratio. To evaluate the performance of the VFT‐based system, it has been compared with a well‐established high voltage direct current (HVDC) system through a three‐phase fault analysis and reactive power requirement. Quantitatively, the proposed VFT system achieves a significant improvement in damping power oscillations, reducing their amplitudes by 30% compared to the HVDC system. Furthermore, the system is linearized under specific operating conditions, and eigenvalue analysis is conducted to assess power system stability for both VFT and HVDC cases. Through time‐domain simulations and eigenvalue analysis, our findings demonstrate that the VFT system surpasses the HVDC system in various operational and control aspects, highlighting its positive impact on power system stability.