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Implementation of Hybrid Metaheuristic MPPT for Partial Shaded Solar PV System

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Implementation of Hybrid Metaheuristic MPPT for Partial Shaded Solar PV System

Overview of the MPPT Algorithms

  • Algorithms Introduced: The video explores four optimization algorithms:

    • Particle Swarm Optimization (PSO)

    • C Search Optimization

    • Flower Pollination Algorithm (FPA)

    • Grey Wolf Optimization (GWO)

Understanding the Solar PV System

  • Panel Configuration: The system consists of three solar panels connected in series, with specific voltage and current characteristics.

  • Impact of Shading: Partial shading can significantly reduce the efficiency of solar panels, leading to multiple local maxima in the power-voltage curve, complicating the task of extracting maximum power.

Implementation of MPPT Algorithms

  • PSO Algorithm: The implementation involves initializing parameters, measuring voltage and current, and iteratively updating the duty cycle until the maximum power is achieved.

  • C Search Optimization: Similar to PSO, it employs a random duty cycle and updates it based on calculated power outputs, utilizing a Lev flight concept for effective optimization.

  • Flower Pollination Algorithm: This algorithm incorporates biotic and abiotic processes to optimize duty cycles for maximum power extraction.

  • Grey Wolf Optimization: GWO uses hierarchical structures to guide the search for maximum power, employing a series of mathematical equations to update positions of the agents.

Results and Conclusions

  • Power Extraction: The results from each algorithm demonstrate their effectiveness in maximizing power output from the solar PV system, even under shading conditions. The performance of PSO, C Search, FPA, and GWO algorithms was showcased, with each successfully achieving the maximum power point.

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