Harris hawks optimization algorithm for model order reduction of interconnected wind turbines

Area of Science:

• Electrical Engineering
• Control Systems
• Renewable Energy Systems

Background:

• Accurate modeling of high-scale interconnected wind turbines is crucial for performance analysis and grid integration.
• Model order reduction techniques are essential for managing the complexity of large-scale power systems.
• Existing optimization techniques may face challenges in achieving robustness and efficiency for high-dimensional systems.

Purpose of the Study:

• To investigate the analytical design and simulation performance of high-scale interconnected wind turbines using reduced-order models.
• To evaluate the efficacy of the Harris Hawks Optimization (HHO) approach for model order reduction.
• To compare the HHO method against state-of-the-art optimization techniques for higher-dimensional power systems.

Main Methods:

• Development of a reduced-order model for high-scale interconnected wind turbines.
• Application of the Harris Hawks Optimization (HHO) algorithm to optimize reduced-order transfer functions.
• Comparative analysis of HHO performance against existing optimization methods in terms of stability and computational effort.

Main Results:

• The HHO approach demonstrated improved stability and reduced computational effort compared to the higher-scale model.
• HHO proved effective in achieving better results for reduced-order transfer functions in model order reduction studies.
• The methodology showed enhanced effectiveness and robustness for higher-dimensional power systems.

Conclusions:

• The Harris Hawks Optimization (HHO) is a promising technique for simplifying complex wind turbine models.
• HHO offers significant advantages in stability and computational efficiency for reduced-order modeling of power systems.
• The proposed method provides a robust and effective solution for analyzing high-dimensional interconnected wind turbine systems.