<![CDATA[Frequency stability in interconnected power systems is increasingly challenged by the growing penetration of renewable energy sources, which introduce variability and uncertainty into generation. This issue is particularly evident in the Nigerian grid, where disturbances significantly affect system dynamic performance. This study presents a hybrid Particle Swarm Optimization–Fuzzy Logic Controller (PSO–FLC) to improve load frequency control (LFC). The approach combines the adaptability of fuzzy logic with the optimization capability of PSO to achieve better dynamic response. A two-area power system model with non-reheat turbine and governor dynamics was developed in MATLAB/Simulink, integrating solar generation in one area and wind generation in the other. A 0.2 p.u. load disturbance was applied to assess system performance. The controller uses Area Control Error (ACE) as input, while PSO is employed to tune the scaling factors by minimizing the Integral of Time-weighted Absolute Error (ITAE). Performance was compared with no control, conventional PI control, and standalone fuzzy logic control. Results show that the PSO–FLC significantly improves system response. Peak undershoot is reduced from 11.25 × 10⁻⁴ to 8.10 × 10⁻⁴, and settling time decreases from over 10 s to about 3.9 s. The proposed method eliminates steady-state error, enhances damping, and reduces oscillations in both areas, while also minimizing tie-line power fluctuations. These findings demonstrate that PSO–FLC is an effective and robust solution for frequency regulation in renewable-integrated systems, with strong potential for improving the stability of the Nigerian power grid.]]>
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