Optimization Performance Power plant Solar With Algorithms Metaheuristic and Fuzzy:Analysis Efficiency and Sustainability

As an essential component of the renewable energy landscape, solar power plants play a pivotal role in generating clean and sustainable electricity. With the increasing demand for green energy and the decreasing costs of photovoltaic technology, optimizing the performance of these plants has become a key challenge. Advanced control systems, especially those based on computational intelligence, have become increasingly important in improving efficiency, managing environmental changes, and extending the lifespan of solar systems. In the meantime, the combination of fuzzy controllers with metaheuristic algorithms has emerged as a smart solution to overcome the limitations of traditional methods such as PID or P&O has been proposed. This approach not only has the ability to adapt to changing operating conditions, but can also automatically optimize system parameters to achieve maximum output power. This paper explores an innovative hybrid control framework in which fuzzy logic for decision making under environmental uncertainties is combined with the ability of meta-heuristic algorithms such as particle swarm optimization. PSO or Genetic Algorithm GA is integrated in finding the global optimal solutions. This technology synergy results in MPPT systems with higher accuracy, improved stability and shorter response time. Furthermore, this paper analyzes the potential impact of this solution on reducing operating costs, increasing reliability and facilitating the integration of solar power plants into smart grids. By presenting case studies and simulation results, it is shown how this approach can bridge the gap between academic research and practical applications in the solar energy industry.

Keywords:

Solar Power Plants , Performance Optimization , P&O , Maximum Power Point Tracking , Metaheuristic Algorithms