An RFCSO-based grid stability enhancement by integrating solar photovoltaic systems with multilevel unified power flow controllers

Multilevel unified power flow controllers (ML-UPFCs) aim to improve grid stability, power quality, and fault management. This approach is particularly beneficial for renewable energy systems connected to a grid, where efficient power flow and robust fault handling are crucial for maintaining system reliability. However, current grid-integrated systems face challenges such as inefficient fault management, harmonic distortions, and instability when dealing with nonlinear loads. Existing control strategies often lack the flexibility and optimization required to handle these issues effectively in dynamic grid environments. Therefore, the proposed methodology involves a multistep control strategy to optimize the integration of solar photovoltaic (SPV) systems with ML-UPFCs. Initially, the SPV array generates direct current (DC) power, which is optimized using a perturb and observe maximum power point tracking controller. The DC-to-DC boost converter then steps up the voltage for input to a voltage source inverter (VSI) or voltage source converter (VSC). The VSI/VSC, enhanced by greedy control-based monarch butterfly optimization, converts DC to AC while minimizing harmonic distortion. The power is then fed into the grid, which supplies sensitive critical and nonlinear loads. Three-phase fault detection mechanisms and series transformers manage the power flow and fault conditions. Furthermore, the ML-UPFC, controlled by a random forest cuckoo search optimization algorithm, enhances the fault ride-through capabilities and power regulation. Additional transformers and a shunt transformer optimize the voltage levels and reactive power management, ensuring stable and high-quality power delivery to both sensitive and nonlinear loads. Finally, the proposed approach addresses power flow optimization, fault mitigation, and nonlinear load management with the aim of enhancing grid stability and efficiency.