Optimized PI based fuzzy controller for power quality assessment in autonomous microgrid using novel hybrid...

Area of Science:

• Electrical Engineering
• Power Systems Engineering

Background:

• Nonlinear loads (NLLs) in three-phase systems cause harmonic distortion, degrading power quality (PQ), increasing losses, and risking voltage instability.
• Existing solutions struggle with dynamic conditions and optimal parameter tuning for effective harmonic mitigation.

Purpose of the Study:

• To propose an enhanced shunt active power filter (SAPF) control strategy for improving power quality in three-phase distribution systems.
• To utilize a hybrid Sand Cat Swarm Zebra Optimization (SCS-ZO) algorithm for tuning a Second-Order Generalized Integrator (SOGI) with Fuzzy Logic Controller (FLC).

Main Methods:

• Implementing a control strategy combining SOGI for signal decomposition and FLC for voltage regulation.
• Employing the SCS-ZO algorithm for optimal tuning of controller parameters to enhance convergence and performance.
• Evaluating the proposed controller in a MATLAB/Simulink-based three-phase microgrid under various load conditions.

Main Results:

• The SCS-ZO-tuned SAPF controller effectively identified phase angle variations and extracted fundamental signal components.
• Accurate signal decomposition by SOGI and robust voltage regulation by FLC were achieved under dynamic conditions.
• Significant improvement in power quality was demonstrated through effective mitigation of harmonics and voltage fluctuations.

Conclusions:

• The proposed SCS-ZO-tuned SAPF control strategy offers a robust and efficient solution for enhancing power quality in three-phase systems.
• The hybrid optimization approach significantly improves controller performance, leading to increased system stability and reliability.
• This method provides a viable approach for addressing challenges posed by nonlinear loads in modern power distribution networks.