SBOA ‐Based Multi‐Level Parallel Fuzzy Controller for AGC of Multi‐Area Power Systems With Renewable Energy and Storage

Modern interconnected power systems face increasing challenges due to load fluctuations, renewable energy integration, and complex AC‐HVDC interconnections, which can cause frequency instability and tie‐line power oscillations. In this research, a parallel two‐level fuzzy tilt‐derivative controller with filter (TLF‐TDN) optimized by the Secretary Bird Optimization Algorithm (SBOA) is proposed for Automatic Generation Control (AGC) of multi‐area power systems that include hydro, thermal, solar photovoltaic (SPV), and wind generating units. Superconducting magnetic energy storage systems (SMESSs) provide rapid transient support during disruptions, while AC tie lines and HVDC links enhance power interchange and dynamic performance. The proposed controller enhances damping, suppresses oscillations, and improves dynamic response across a range of operating situations by using a two‐level fuzzy structure and tilt‐derivative action. By lowering the Integral Square Error (ISE) criterion, SBOA optimizes controller parameters. The effectiveness of the proposed method is evaluated in a range of loading and disturbance conditions. The robustness and efficacy of the SBOA‐optimized TLF‐TDN controller in contemporary renewable‐integrated interconnected power systems are confirmed by simulation results, which show that it outperforms traditional PDN, TDN, and single‐level fuzzy TDN (F‐TDN) controllers in terms of frequency deviations, tie‐line oscillations, settling time, and overall system stability.