Semantic-AI–Enhanced LFC for PV-Integrated Grids Using a TFOID-(PDN+1) Framework

As photovoltaic (PV) penetration increases, reduced system inertia intensifies frequency stability challenges in interconnected power grids. Conventional proportional integral derivative–based and single-loop fractional-order load frequency control schemes often exhibit limited disturbance rejection and robustness under stochastic PV fluctuations and parameter uncertainties. This paper proposes a cascaded tilted fractional-order integral–derivative–(PDN+1) load frequency control framework for a two-area PV–storage-integrated system, enhanced by a semantics-guided intelligent optimization strategy. A modified multi-objective function combining integral squared time absolute error and squared control effort embeds control semantics related to disturbance persistence and energy limitation. The lemur optimizer is employed for parameter tuning. Simulation results demonstrate faster response, smaller frequency deviations, and improved robustness compared with Proportional–Integral–(Proportional–Derivative+1), functional-order–proportional integral derivative, and single-objective integral squared time absolute error–based controllers, while maintaining engineering feasibility for future low-inertia power systems.