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A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for
Nassir Deghfel1, Abd Essalam Badoud2, Farid Merahi1
1Setif Automatic Laboratory, Electrical Engineering Department, Ferhat Abbas University Setif 1, Setif, Algeria.
Scientific Reports
|March 22, 2024
Summary
This study introduces an optimal adaptive controller for photovoltaic systems to maximize power output during rapid weather changes. The novel method ensures efficient energy harvesting, improving renewable energy integration.
Area of Science:
- Electrical Engineering
- Renewable Energy Systems
- Control Theory
Background:
- Photovoltaic (PV) systems are crucial for sustainable energy generation.
- Efficient Maximum Power Point Tracking (MPPT) is vital for PV system performance, especially under variable conditions.
- Existing MPPT techniques face challenges with rapidly changing environmental factors like irradiance and temperature.
Purpose of the Study:
- To develop an innovative and robust MPPT method for PV systems operating under dynamic weather conditions.
- To enhance the efficiency and power output of PV systems by addressing MPPT challenges.
- To optimize controller parameters using meta-heuristic algorithms for improved performance.
Main Methods:
- A novel optimal Model Reference Adaptive Controller (MRAC) based on the MIT rule was designed for rapid, ripple-free global maximum power tracking.
- The MRAC controller's adaptation gain was optimized using the Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA).
- An adaptive neuro-fuzzy inference system (ANFIS) was employed to generate the reference voltage for MPPT.
Main Results:
- The proposed optimal MRAC controller demonstrated effective MPPT performance under varying temperature and radiation conditions via MATLAB/Simulink simulations.
- The controller successfully achieved maximum power extraction without significant output ripples.
- Comparative analysis showed the optimal MRAC (GA and WOA) outperformed the conventional Incremental Conductance (INC) method.
Conclusions:
- The developed optimal MRAC controller, optimized by GA and WOA, offers a superior solution for MPPT in PV systems under fluctuating environmental conditions.
- The integration of ANFIS for reference voltage generation further enhances the controller's adaptability and effectiveness.
- This research contributes to more efficient and reliable integration of solar energy into power grids.
Keywords:
Adaptive neuro-fuzzy inference systemConvergence analysisGenetic algorithmMaximum power point trackingModel reference adaptive controlPhotovoltaic systemsRenewable energyMore Related Videos
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