Design and analysis of UPQC in a microgrid using model reference adaptive control ensemble with back-stepping...

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Systems

Background:

• The power sector is transitioning to decentralized generation, with microgrids integrating diverse energy sources.
• Increased renewable energy and distributed generators necessitate advanced strategies for power quality (PQ) and reliability.
• Existing microgrid systems require enhanced control for optimal performance and stability.

Purpose of the Study:

• To design and evaluate a novel control system for microgrids to improve power quality and grid management.
• To introduce a Model Reference Adaptive Control (MRAC) with Back-stepping and online parameter tuning for enhanced adaptability.
• To demonstrate the cost-effectiveness and sustainability of the proposed microgrid model.

Main Methods:

• A microgrid model integrating photovoltaics, wind energy, and fuel cells was developed.
• A Unified Power Quality Conditioner (UPQC) was integrated to address PQ issues.
• A Back-stepping controller with MRAC and online parameter tuning was implemented and compared against ANFIS and Fuzzy controllers.
• A Matlab model and hardware setup were used for validation.

Main Results:

• The MRAC-Back-stepping controller demonstrated superior adaptability and responsiveness in microgrid operations.
• The proposed system achieved optimal grid management, enhancing efficiency and stability.
• Comparative analysis confirmed the efficacy and versatility of the MRAC-Back-stepping controller over ANFIS and Fuzzy controllers.
• The model requires minimal additional infrastructure, proving cost-effective and sustainable.

Conclusions:

• The developed MRAC-Back-stepping controller is highly effective for microgrid power quality and stability enhancement.
• The proposed system offers a sustainable and cost-effective solution for modern microgrid challenges.
• The research validates the robustness and versatility of the proposed control strategy through simulation and hardware implementation.