Self-filtering based on the fault ride-through technique using a robust model predictive control for wind turbine...

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Systems

Background:

• Doubly-fed induction generators (DFIGs) in wind farms (WF) can introduce power quality issues like harmonics and active power ripples.
• Grid connection of WFs requires advanced control strategies for stable operation and power quality enhancement.
• Fault ride-through capabilities are essential for DFIGs to maintain grid stability during disturbances.

Purpose of the Study:

• To develop and validate a Finite Space Model Predictive Control (FS-MPC) strategy for DFIG-based WFs.
• To improve the quality of current output by reducing harmonics and active power ripples.
• To enhance the power factor and ensure effective fault ride-through capabilities.

Main Methods:

• Implementation of a Finite Space Model Predictive Control (FS-MPC) algorithm for DFIG-WF management.
• Integration of a self-active filtering mechanism within the DFIGs-WF control.
• Simulation and verification using MATLAB software, comparing the proposed method against conventional strategies.

Main Results:

• The proposed FS-MPC effectively manages four operational modes: Maximum Power Point Tracking, Delta, Fault, and Filtering.
• Significant reduction in current Total Harmonic Distortion (THD) was achieved, with THD values decreasing from over 50% to below 0.4%.
• The technique demonstrated robustness, high accuracy, and fast dynamic response compared to existing methods.

Conclusions:

• The FS-MPC strategy offers a robust and effective solution for improving power quality in DFIG-based wind farms.
• The integrated filtering mechanism successfully mitigates harmonics caused by non-linear loads.
• The proposed control method enhances grid integration of renewable energy sources by ensuring stable and high-quality power output.