Robust predictive control strategy of SCIG-based drive system

Area of Science:

• Electrical Engineering
• Control Systems
• Renewable Energy Systems

Background:

• Stand-alone power systems require stable voltage and frequency control.
• Squirrel cage induction generators (SCIGs) are common in distributed generation but present control challenges.
• Existing control methods may struggle with nonlinearities and parameter uncertainties.

Purpose of the Study:

• To present a finite control set predictive scheme for stand-alone SCIG drives.
• To ensure stable output voltage and maintain system variables during load and source changes.
• To minimize cross-coupling between active and reactive power and mitigate parameter uncertainties.

Main Methods:

• A finite control set predictive control strategy is developed.
• Optimized switching instants are calculated to minimize a cost function.
• The scheme incorporates system nonlinearities and addresses parameter uncertainties.
• Simulation (MATLAB/Simulink) and real-time experiments (dSPACE-1104) are used for evaluation.

Main Results:

• The predictive scheme effectively maintains dc-link voltage, machine, and load variables at set values.
• Minimized cross-coupling between active and reactive power during transients is achieved.
• The control strategy demonstrates robustness against parameter uncertainties and speed variations.
• Real-time experimental results validate the feasibility of the proposed predictive solution.

Conclusions:

• The finite control set predictive scheme offers a robust and effective solution for stand-alone SCIG drives.
• The proposed method enhances voltage regulation and power quality in distributed generation.
• The scheme provides a viable alternative to existing drive systems, particularly for dynamic performance.