Mitigation of LFOs within Smart Grid Using Adaptive Fuzzy Logic Neural Network control Scheme

Abstract

Power system stability is challenged by low inertia and Low-Frequency Oscillations (LFOs) within 0.1–2 Hz. Short-circuit faults occurring within milliseconds can cause generator desynchronization. Reducing LFOs under stress improves stability and power transfer control, which is vital for inter-regional power exchange. Based on third-generation devices from the Flexible AC Transmission System (FACTS), this study introduces a Smart Grid (SG) control strategy that emphasizes the Generalized Unified Power Flow Controller (GUPFC) combined with an adaptive auxiliary control scheme. A centralized, real-time supplementary controller is developed to mitigate low-frequency oscillations (LFOs), enhance effective system inertia, and optimize power flow. A quantitative evaluation of the proposed approach performance in per unit (p.u.), demonstrates measurable reductions in oscillations and noticeable improvements in system inertia.  The proposed Adaptive Fuzzy Logic Neural Network Controller (AFLNNC) mitigates oscillations up to 15-25%, enhances dynamic performance, reduces DC capacitor voltage fluctuations by 20%, and stabilizes bus voltage magnitude within 5% of the nominal values. Synchronous and asynchronous generators, grid-to-vehicle systems, renewable energy sources (wind, photovoltaic), and residential loads are all included in the two-area test system.  The sever  faults in the Smart Grid are modeled and validated using MATLAB/Simulink simulations. The AFLNNC proves to be a powerful and intelligent strategy for boosting the secure and stable system for future renewable-dominated power systems.