Optimized PI based fuzzy controller for power quality assessment in autonomous microgrid using novel hybrid bio-inspired optimization technique

The increasing integration of nonlinear loads within three-phase distribution systems contributes to significant harmonic distortion, leading to degraded power quality, elevated losses, and potential voltage instability. To address these challenges, this research proposes an advanced control strategy for shunt active power filters (SAPF). The methodology employs a hybrid Sand Cat Swarm Zebra Optimization (SCS-ZO) algorithm to tune a Second-Order Generalized Integrator (SOGI) coupled with a Fuzzy Logic Controller (FLC). This integrated approach is designed to accurately identify phase angle variations, extract fundamental signal components, and regulate compensating signals effectively under dynamic operating conditions. The SOGI module facilitates precise signal decomposition, while the FLC ensures robust voltage regulation and a rapid

dynamic response. Furthermore, the SCS-ZO optimization algorithm is utilized for optimal tuning of controller parameters, aiming to enhance convergence speed and overall system performance. The proposed controller's efficacy is evaluated through MATLAB/Simulink simulations on a three-phase microgrid, considering balanced, unbalanced, and nonlinear load conditions. Results indicate that the SCS-ZO-based strategy significantly improves power quality by effectively mitigating harmonics, voltage fluctuations, and other disturbances, thereby contributing to enhanced system stability and reliability.

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