Stability Analysis of a Grid‐Connected DFIG Wind Farm Using Static VAR Compensator

The integration of wind farms has greatly impacted the stability and performance of power systems. This is primarily due to the intermittent nature of wind energy, which requires advanced control strategies and robust grid‐integration techniques. This paper employs a Static VAR Compensator (SVC) equipped with a proportional integral (PI) controller to enhance the dynamic stability of power systems incorporating wind power stations. For the SVC to effectively improve system performance, the parameters of its PI controller must be properly tuned. In this study, the SVC controller parameters are optimized using the simulated annealing (SA), particle swarm optimization (PSO), and water cycle algorithm (WCA) to enhance system damping and the low‐voltage ride through (LVRT) capability of wind farms. In addition, a fuzzy logic controller (FLC) and a sliding mode controller (SMC) are designed and simulated to validate the effectiveness of the proposed optimization approach. The simulations are performed on the IEEE 9‐bus test system integrated with a 45‐MW wind farm comprising 30 doubly fed induction generator (DFIG)–based wind turbines. The efficacy of the proposed approach is evaluated under three‐line‐to‐ground (3LG) and line‐to‐line (LL) fault conditions. The obtained results show that an untuned SVC fails to adequately damp system oscillations during faults, whereas the SA, PSO, and WCA tuned SVCs significantly enhance the dynamic performance of the system. For the 3LG fault, the settling times of terminal voltage with the SA, PSO, and WCA tuned SVCs are 3.15 s, 3.15 s, and 3.14 s, respectively. Following the LL fault, the corresponding settling times are 3.15 s, 3.17 s, and 3.12 s. The simulation results confirm the superior dynamic response and faster postfault recovery of the WCA‐tuned SVC compared with the untuned SVC, as well as the PSO, and SA‐tuned SVCs.