Online stability assessment and enhancement of microgrids using a decentralized control algorithm

• A novel decentralized algorithm estimates the microgrid's dominant stability mode using local measurements. • Variational mode decomposition enables accurate online mode identification at each distributed generator unit. • A dedicated fuzzy logic controller enhances system stability through reactive power droop gain adjustments. • The proposed control strategy improves stability without disrupting active power sharing among generators. • Comprehensive simulation testing validates performance under both normal and contingency-based operating conditions. Microgrid stability is sensitive to both system parameters and structure. The dominant mode location, which determines overall stability, is highly influenced by active and reactive power droop gains. Therefore, estimating the dominant mode online and enhancing it through control techniques is essential. In this paper, a variational mode decomposition algorithm is employed to estimate the dominant mode at each distributed generator (DG) in a decentralized manner using only local measurements. A fuzzy logic controller is then designed to improve the dominant mode location by adjusting the reactive power droop control loop. The proposed decentralized approach enhances microgrid stability while maintaining proper active power sharing among DGs. Simulation results under various operating conditions confirm the effectiveness of the proposed algorithm in accurately estimating the dominant mode and improving system stability. Different simulation scenarios are conducted, considering normal and abnormal conditions to test the performance of the proposed algorithm. Relative to the small-signal model, the proposed controller captures approximately 97.2% of the damping magnitude (real part) and 99.5% of the oscillation frequency (imaginary part) of the dominant mode. In comparison, the LASSO-based neural network algorithm captures about 93.5% of the damping magnitude and 97.9% of the oscillation frequency. Unlike the LASSO-based approach that requires pre-tuning for specific fault scenarios such as DG disconnection, our method maintains robust performance across both normal and sudden abnormal conditions without scenario-specific adjustments.