Voltage Stability Enrichment in DC Microgrids with UPFC Compensator and Artificial Neural Networks

Abstract

Integrating renewable energy sources (RESs) presents several challenges in power systems, such as power management, voltage stability, and power quality issues. This paper aims to maintain voltage stability within microgrids by introducing a unique approach that utilizes an artificial neural network (ANN) in conjunction with approximating dynamic programming integrated with the unified power flow controller (UPFC) for rapid fault detection and voltage stabilization in DC microgrids. The UPFC injects compensating current to counter disruptions and uphold voltage stability. A comparative study validates the effectiveness of the ANN-based UPFC in comparison to Fuzzy-PI and Fuzzy-PID controller-based distribution static synchronous compensator systems under faulty conditions in a DC microgrid connected to various RESs and the grid. This comparison aims to maintain microgrid stability and achieve a more reliable system. The ANN’s ability to adapt to predicting and learning from historical data enhances the UPFC’s performance, enabling a rapid and precise response to faults. This methodology significantly enhances the reliable operation and sustainability of the DC microgrid, mitigating the impacts of power fluctuations and faults. Simulation results demonstrate that the proposed ANN-based UPFC outperforms conventional controllers.