Indirect adaptive soft computing based wavelet-embedded control paradigms for WT/PV/SOFC in a grid/charging station...

Area of Science:

• Renewable Energy Systems
• Control Engineering
• Hybrid Power Systems

Background:

• Renewable energy sources (wind, solar, fuel cells) exhibit low efficiency and intermittency due to weather and uncertain loads.
• Accurate online capture of instantaneous nonlinear dynamics is crucial for efficient power harvesting.
• Grid-connected hybrid systems require robust control for stability and optimal performance.

Purpose of the Study:

• To develop and evaluate indirect adaptive tracking control strategies for maximizing power extraction from variable speed wind turbine-permanent synchronous generators (VSWT-PMSG), photovoltaic (PV) systems, and Solid Oxide Fuel Cells (SOFC).
• To enhance the efficiency and stability of grid-connected hybrid power systems incorporating diverse renewable energy sources.
• To validate the robustness and effectiveness of proposed control paradigms through comprehensive simulations.

Main Methods:

• Proposed a Chebyshev-wavelet embedded NeuroFuzzy indirect adaptive MPPT control for VSWT-PMSG.
• Developed a Hermite-wavelet incorporated NeuroFuzzy indirect adaptive MPPT control for PV systems.
• Implemented an indirect adaptive tracking control scheme for SOFC.
• Created a comprehensive simulation test-bed in Matlab/Simulink for a grid-connected hybrid power system.

Main Results:

• The proposed indirect adaptive control paradigms demonstrated robust performance in simulation.
• Effectiveness was validated through comparative analysis against conventional and intelligent control techniques.
• The control strategies successfully captured instantaneous nonlinear dynamics for efficient power harvesting.

Conclusions:

• The developed indirect adaptive tracking control strategies are effective for optimizing power extraction from renewable energy sources in hybrid systems.
• The proposed methods ensure robust operation and stable integration of renewable energy into the grid.
• Simulation results confirm the superiority of the proposed control paradigms over existing techniques.